"
Models
The base classes [PreTrainedModel], [TFPreTrainedModel], and
[FlaxPreTrainedModel] implement the common methods for loading/saving a model either from a local
file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS
S3 repository).
[PreTrainedModel] and [TFPreTrainedModel] also implement a few methods which
are common among all the models to:

resize the input token embeddings when new tokens are added to the vocabulary
prune the attention heads of the model.

The other methods that are common to each model are defined in [~modeling_utils.ModuleUtilsMixin]
(for the PyTorch models) and [~modeling_tf_utils.TFModuleUtilsMixin] (for the TensorFlow models) or
for text generation, [~generation.GenerationMixin] (for the PyTorch models),
[~generation.TFGenerationMixin] (for the TensorFlow models) and
[~generation.FlaxGenerationMixin] (for the Flax/JAX models).
PreTrainedModel
[[autodoc]] PreTrainedModel
    - push_to_hub
    - all

Large model loading
In Transformers 4.20.0, the [~PreTrainedModel.from_pretrained] method has been reworked to accommodate large models using Accelerate. This requires Accelerate >= 0.9.0 and PyTorch >= 1.9.0. Instead of creating the full model, then loading the pretrained weights inside it (which takes twice the size of the model in RAM, one for the randomly initialized model, one for the weights), there is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded.
This option can be activated with low_cpu_mem_usage=True. The model is first created on the Meta device (with empty weights) and the state dict is then loaded inside it (shard by shard in the case of a sharded checkpoint). This way the maximum RAM used is the full size of the model only.

from transformers import AutoModelForSeq2SeqLM
t0pp = AutoModelForSeq2SeqLM.from_pretrained(""bigscience/T0pp"", low_cpu_mem_usage=True)

Moreover, you can directly place the model on different devices if it doesn't fully fit in RAM (only works for inference for now). With device_map=""auto"", Accelerate will determine where to put each layer to maximize the use of your fastest devices (GPUs) and offload the rest on the CPU, or even the hard drive if you don't have enough GPU RAM (or CPU RAM). Even if the model is split across several devices, it will run as you would normally expect.
When passing a device_map, low_cpu_mem_usage is automatically set to True, so you don't need to specify it:

from transformers import AutoModelForSeq2SeqLM
t0pp = AutoModelForSeq2SeqLM.from_pretrained(""bigscience/T0pp"", device_map=""auto"")

You can inspect how the model was split across devices by looking at its hf_device_map attribute:
py
t0pp.hf_device_map
python out
{'shared': 0,
 'decoder.embed_tokens': 0,
 'encoder': 0,
 'decoder.block.0': 0,
 'decoder.block.1': 1,
 'decoder.block.2': 1,
 'decoder.block.3': 1,
 'decoder.block.4': 1,
 'decoder.block.5': 1,
 'decoder.block.6': 1,
 'decoder.block.7': 1,
 'decoder.block.8': 1,
 'decoder.block.9': 1,
 'decoder.block.10': 1,
 'decoder.block.11': 1,
 'decoder.block.12': 1,
 'decoder.block.13': 1,
 'decoder.block.14': 1,
 'decoder.block.15': 1,
 'decoder.block.16': 1,
 'decoder.block.17': 1,
 'decoder.block.18': 1,
 'decoder.block.19': 1,
 'decoder.block.20': 1,
 'decoder.block.21': 1,
 'decoder.block.22': 'cpu',
 'decoder.block.23': 'cpu',
 'decoder.final_layer_norm': 'cpu',
 'decoder.dropout': 'cpu',
 'lm_head': 'cpu'}
You can also write your own device map following the same format (a dictionary layer name to device). It should map all parameters of the model to a given device, but you don't have to detail where all the submodules of one layer go if that layer is entirely on the same device. For instance, the following device map would work properly for T0pp (as long as you have the GPU memory):
python
device_map = {""shared"": 0, ""encoder"": 0, ""decoder"": 1, ""lm_head"": 1}
Another way to minimize the memory impact of your model is to instantiate it at a lower precision dtype (like torch.float16) or use direct quantization techniques as described below.
Model Instantiation dtype
Under Pytorch a model normally gets instantiated with torch.float32 format. This can be an issue if one tries to
load a model whose weights are in fp16, since it'd require twice as much memory. To overcome this limitation, you can
either explicitly pass the desired dtype using torch_dtype argument:
python
model = T5ForConditionalGeneration.from_pretrained(""t5"", torch_dtype=torch.float16)
or, if you want the model to always load in the most optimal memory pattern, you can use the special value ""auto"",
and then dtype will be automatically derived from the model's weights:
python
model = T5ForConditionalGeneration.from_pretrained(""t5"", torch_dtype=""auto"")
Models instantiated from scratch can also be told which dtype to use with:
python
config = T5Config.from_pretrained(""t5"")
model = AutoModel.from_config(config)
Due to Pytorch design, this functionality is only available for floating dtypes.
ModuleUtilsMixin
[[autodoc]] modeling_utils.ModuleUtilsMixin
TFPreTrainedModel
[[autodoc]] TFPreTrainedModel
    - push_to_hub
    - all
TFModelUtilsMixin
[[autodoc]] modeling_tf_utils.TFModelUtilsMixin
FlaxPreTrainedModel
[[autodoc]] FlaxPreTrainedModel
    - push_to_hub
    - all
Pushing to the Hub
[[autodoc]] utils.PushToHubMixin
Sharded checkpoints
[[autodoc]] modeling_utils.load_sharded_checkpoint"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate
We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:

from huggingface_hub import notebook_login
notebook_login()

Load ELI5 dataset
Start by loading a smaller subset of the r/askscience subset of the ELI5 dataset from the 🤗 Datasets library. This'll
give you a chance to experiment and make sure everything works before spending more time training on the full dataset.

from datasets import load_dataset
eli5 = load_dataset(""eli5"", split=""train_asks[:5000]"")

Split the dataset's train_asks split into a train and test set with the [~datasets.Dataset.train_test_split] method:

eli5 = eli5.train_test_split(test_size=0.2)

Then take a look at an example:

eli5[""train""][0]
{'answers': {'a_id': ['c3d1aib', 'c3d4lya'],
  'score': [6, 3],
  'text': [""The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up."",
   ""Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?""]},
 'answers_urls': {'url': []},
 'document': '',
 'q_id': 'nyxfp',
 'selftext': 'URL_0\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']},
 'subreddit': 'askscience',
 'title': 'Few questions about this space walk photograph.',
 'title_urls': {'url': []}}

While this may look like a lot, you're only really interested in the text field. What's cool about language modeling tasks is you don't need labels (also known as an unsupervised task) because the next word is the label.
Preprocess

For masked language modeling, the next step is to load a DistilRoBERTa tokenizer to process the text subfield:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""distilroberta-base"")

You'll notice from the example above, the text field is actually nested inside answers. This means you'll need to e
xtract the text subfield from its nested structure with the flatten method:

eli5 = eli5.flatten()
eli5[""train""][0]
{'answers.a_id': ['c3d1aib', 'c3d4lya'],
 'answers.score': [6, 3],
 'answers.text': [""The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up."",
  ""Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?""],
 'answers_urls.url': [],
 'document': '',
 'q_id': 'nyxfp',
 'selftext': 'URL_0\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'],
 'subreddit': 'askscience',
 'title': 'Few questions about this space walk photograph.',
 'title_urls.url': []}

Each subfield is now a separate column as indicated by the answers prefix, and the text field is a list now. Instead
of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them.
Here is a first preprocessing function to join the list of strings for each example and tokenize the result:

def preprocess_function(examples):
     return tokenizer(["" "".join(x) for x in examples[""answers.text""]])

To apply this preprocessing function over the entire dataset, use the 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once, and increasing the number of processes with num_proc. Remove any columns you don't need:

tokenized_eli5 = eli5.map(
     preprocess_function,
     batched=True,
     num_proc=4,
     remove_columns=eli5[""train""].column_names,
 )

This dataset contains the token sequences, but some of these are longer than the maximum input length for the model.
You can now use a second preprocessing function to
- concatenate all the sequences
- split the concatenated sequences into shorter chunks defined by block_size, which should be both shorter than the maximum input length and short enough for your GPU RAM. 

block_size = 128
def group_texts(examples):
     # Concatenate all texts.
     concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
     total_length = len(concatenated_examples[list(examples.keys())[0]])
     # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
     # customize this part to your needs.
     if total_length >= block_size:
         total_length = (total_length // block_size) * block_size
     # Split by chunks of block_size.
     result = {
         k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
         for k, t in concatenated_examples.items()
     }
     return result

Apply the group_texts function over the entire dataset:

lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)

Now create a batch of examples using [DataCollatorForLanguageModeling]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.

Use the end-of-sequence token as the padding token and specify mlm_probability to randomly mask tokens each time you iterate over the data:

from transformers import DataCollatorForLanguageModeling
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)

Use the end-of-sequence token as the padding token and specify mlm_probability to randomly mask tokens each time you iterate over the data:

from transformers import DataCollatorForLanguageModeling
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15, return_tensors=""tf"")

Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load DistilRoBERTa with [AutoModelForMaskedLM]:

from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained(""distilroberta-base"")

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model).
Pass the training arguments to [Trainer] along with the model, datasets, and data collator.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_eli5_mlm_model"",
     evaluation_strategy=""epoch"",
     learning_rate=2e-5,
     num_train_epochs=3,
     weight_decay=0.01,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=lm_dataset[""train""],
     eval_dataset=lm_dataset[""test""],
     data_collator=data_collator,
 )
trainer.train()

Once training is completed, use the [~transformers.Trainer.evaluate] method to evaluate your model and get its perplexity:

import math
eval_results = trainer.evaluate()
print(f""Perplexity: {math.exp(eval_results['eval_loss']):.2f}"")
Perplexity: 8.76

Then share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)

Then you can load DistilRoBERTa with [TFAutoModelForMaskedLM]:

from transformers import TFAutoModelForMaskedLM
model = TFAutoModelForMaskedLM.from_pretrained(""distilroberta-base"")

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     lm_dataset[""train""],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_test_set = model.prepare_tf_dataset(
     lm_dataset[""test""],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

import tensorflow as tf
model.compile(optimizer=optimizer)  # No loss argument!

This can be done by specifying where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
callback = PushToHubCallback(
     output_dir=""my_awesome_eli5_mlm_model"",
     tokenizer=tokenizer,
 )

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callback to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for masked language modeling, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text you'd like the model to fill in the blank with, and use the special <mask> token to indicate the blank:

text = ""The Milky Way is a  galaxy.""

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for fill-mask with your model, and pass your text to it. If you like, you can use the top_k parameter to specify how many predictions to return:

from transformers import pipeline
mask_filler = pipeline(""fill-mask"", ""stevhliu/my_awesome_eli5_mlm_model"")
mask_filler(text, top_k=3)
[{'score': 0.5150994658470154,
  'token': 21300,
  'token_str': ' spiral',
  'sequence': 'The Milky Way is a spiral galaxy.'},
 {'score': 0.07087188959121704,
  'token': 2232,
  'token_str': ' massive',
  'sequence': 'The Milky Way is a massive galaxy.'},
 {'score': 0.06434620916843414,
  'token': 650,
  'token_str': ' small',
  'sequence': 'The Milky Way is a small galaxy.'}]

Tokenize the text and return the input_ids as PyTorch tensors. You'll also need to specify the position of the <mask> token:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""stevhliu/my_awesome_eli5_mlm_model"")
inputs = tokenizer(text, return_tensors=""pt"")
mask_token_index = torch.where(inputs[""input_ids""] == tokenizer.mask_token_id)[1]

Pass your inputs to the model and return the logits of the masked token:

from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained(""stevhliu/my_awesome_eli5_mlm_model"")
logits = model(**inputs).logits
mask_token_logits = logits[0, mask_token_index, :]

Then return the three masked tokens with the highest probability and print them out:

top_3_tokens = torch.topk(mask_token_logits, 3, dim=1).indices[0].tolist()
for token in top_3_tokens:
     print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))
The Milky Way is a spiral galaxy.
The Milky Way is a massive galaxy.
The Milky Way is a small galaxy.
``
</pt>
<tf>
Tokenize the text and return theinput_idsas TensorFlow tensors. You'll also need to specify the position of the` token:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""stevhliu/my_awesome_eli5_mlm_model"")
inputs = tokenizer(text, return_tensors=""tf"")
mask_token_index = tf.where(inputs[""input_ids""] == tokenizer.mask_token_id)[0, 1]

Pass your inputs to the model and return the logits of the masked token:

from transformers import TFAutoModelForMaskedLM
model = TFAutoModelForMaskedLM.from_pretrained(""stevhliu/my_awesome_eli5_mlm_model"")
logits = model(**inputs).logits
mask_token_logits = logits[0, mask_token_index, :]

Then return the three masked tokens with the highest probability and print them out:

top_3_tokens = tf.math.top_k(mask_token_logits, 3).indices.numpy()
for token in top_3_tokens:
     print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))
The Milky Way is a spiral galaxy.
The Milky Way is a massive galaxy.
The Milky Way is a small galaxy.

"
"
Efficient Inference on CPU
This guide focuses on inferencing large models efficiently on CPU.
BetterTransformer for faster inference
We have recently integrated BetterTransformer for faster inference on CPU for text, image and audio models. Check the documentation about this integration here for more details.
PyTorch JIT-mode (TorchScript)
TorchScript is a way to create serializable and optimizable models from PyTorch code. Any TorchScript program can be saved from a Python process and loaded in a process where there is no Python dependency.
Comparing to default eager mode, jit mode in PyTorch normally yields better performance for model inference from optimization methodologies like operator fusion.
For a gentle introduction to TorchScript, see the Introduction to PyTorch TorchScript tutorial.
IPEX Graph Optimization with JIT-mode
Intel® Extension for PyTorch provides further optimizations in jit mode for Transformers series models. It is highly recommended for users to take advantage of Intel® Extension for PyTorch with jit mode. Some frequently used operator patterns from Transformers models are already supported in Intel® Extension for PyTorch with jit mode fusions. Those fusion patterns like Multi-head-attention fusion, Concat Linear, Linear+Add, Linear+Gelu, Add+LayerNorm fusion and etc. are enabled and perform well. The benefit of the fusion is delivered to users in a transparent fashion. According to the analysis, ~70% of most popular NLP tasks in question-answering, text-classification, and token-classification can get performance benefits with these fusion patterns for both Float32 precision and BFloat16 Mixed precision.
Check more detailed information for IPEX Graph Optimization.
IPEX installation:
IPEX release is following PyTorch, check the approaches for IPEX installation.
Usage of JIT-mode
To enable JIT-mode in Trainer for evaluaion or prediction, users should add jit_mode_eval in Trainer command arguments.

for PyTorch >= 1.14.0. JIT-mode could benefit any models for prediction and evaluaion since dict input is supported in jit.trace
for PyTorch < 1.14.0. JIT-mode could benefit models whose forward parameter order matches the tuple input order in jit.trace, like question-answering model
In the case where the forward parameter order does not match the tuple input order in jit.trace, like text-classification models, jit.trace will fail and we are capturing this with the exception here to make it fallback. Logging is used to notify users.

Take an example of the use cases on Transformers question-answering

Inference using jit mode on CPU:

python run_qa.py \
--model_name_or_path csarron/bert-base-uncased-squad-v1 \
--dataset_name squad \
--do_eval \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/ \
--no_cuda \
--jit_mode_eval 

Inference with IPEX using jit mode on CPU:

python run_qa.py \
--model_name_or_path csarron/bert-base-uncased-squad-v1 \
--dataset_name squad \
--do_eval \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/ \
--no_cuda \
--use_ipex \
--jit_mode_eval"
"
Attention mechanisms
Most transformer models use full attention in the sense that the attention matrix is square. It can be a big
computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and
use a sparse version of the attention matrix to speed up training.
LSH attention
Reformer uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax
dimension) of the matrix QK^t are going to give useful contributions. So for each query q in Q, we can consider only
the keys k in K that are close to q. A hash function is used to determine if q and k are close. The attention mask is
modified to mask the current token (except at the first position), because it will give a query and a key equal (so
very similar to each other). Since the hash can be a bit random, several hash functions are used in practice
(determined by a n_rounds parameter) and then are averaged together.
Local attention
Longformer uses local attention: often, the local context (e.g., what are the two tokens to the
left and right?) is enough to take action for a given token. Also, by stacking attention layers that have a small
window, the last layer will have a receptive field of more than just the tokens in the window, allowing them to build a
representation of the whole sentence.
Some preselected input tokens are also given global attention: for those few tokens, the attention matrix can access
all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in
their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask:

Using those attention matrices with less parameters then allows the model to have inputs having a bigger sequence
length.
Other tricks
Axial positional encodings
Reformer uses axial positional encodings: in traditional transformer models, the positional encoding
E is a matrix of size \(l\) by \(d\), \(l\) being the sequence length and \(d\) the dimension of the
hidden state. If you have very long texts, this matrix can be huge and take way too much space on the GPU. To alleviate
that, axial positional encodings consist of factorizing that big matrix E in two smaller matrices E1 and E2, with
dimensions \(l_{1} \times d_{1}\) and \(l_{2} \times d_{2}\), such that \(l_{1} \times l_{2} = l\) and
\(d_{1} + d_{2} = d\) (with the product for the lengths, this ends up being way smaller). The embedding for time
step \(j\) in E is obtained by concatenating the embeddings for timestep \(j \% l1\) in E1 and \(j // l1\)
in E2."
"
How to convert a 🤗 Transformers model to TensorFlow?
Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when
designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that
adding TensorFlow compatibility to an existing model is simpler than adding a new model from scratch!
Whether you wish to have a deeper understanding of large TensorFlow models, make a major open-source contribution, or
enable TensorFlow for your model of choice, this guide is for you.
This guide empowers you, a member of our community, to contribute TensorFlow model weights and/or
architectures to be used in 🤗 Transformers, with minimal supervision from the Hugging Face team. Writing a new model
is no small feat, but hopefully this guide will make it less of a rollercoaster 🎢 and more of a walk in the park 🚶.
Harnessing our collective experiences is absolutely critical to make this process increasingly easier, and thus we
highly encourage that you suggest improvements to this guide!
Before you dive deeper, it is recommended that you check the following resources if you're new to 🤗 Transformers:
- General overview of 🤗 Transformers
- Hugging Face's TensorFlow Philosophy
In the remainder of this guide, you will learn what's needed to add a new TensorFlow model architecture, the
procedure to convert PyTorch into TensorFlow model weights, and how to efficiently debug mismatches across ML
frameworks. Let's get started!

Are you unsure whether the model you wish to use already has a corresponding TensorFlow architecture?
 
Check the model_type field of the config.json of your model of choice
(example). If the corresponding model folder in
🤗 Transformers has a file whose name starts with ""modeling_tf"", it means that it has a corresponding TensorFlow
architecture (example).

Step-by-step guide to add TensorFlow model architecture code
There are many ways to design a large model architecture, and multiple ways of implementing said design. However,
you might recall from our general overview of 🤗 Transformers
that we are an opinionated bunch - the ease of use of 🤗 Transformers relies on consistent design choices. From
experience, we can tell you a few important things about adding TensorFlow models:

Don't reinvent the wheel! More often that not, there are at least two reference implementations you should check: the
PyTorch equivalent of the model you are implementing and other TensorFlow models for the same class of problems.
Great model implementations survive the test of time. This doesn't happen because the code is pretty, but rather
because the code is clear, easy to debug and build upon. If you make the life of the maintainers easy with your
TensorFlow implementation, by replicating the same patterns as in other TensorFlow models and minimizing the mismatch
to the PyTorch implementation, you ensure your contribution will be long lived.
Ask for help when you're stuck! The 🤗 Transformers team is here to help, and we've probably found solutions to the same
problems you're facing.

Here's an overview of the steps needed to add a TensorFlow model architecture:
1. Select the model you wish to convert
2. Prepare transformers dev environment
3. (Optional) Understand theoretical aspects and the existing implementation
4. Implement the model architecture
5. Implement model tests
6. Submit the pull request
7. (Optional) Build demos and share with the world
1.-3. Prepare your model contribution
1. Select the model you wish to convert
Let's start off with the basics: the first thing you need to know is the architecture you want to convert. If you
don't have your eyes set on a specific architecture, asking the 🤗 Transformers team for suggestions is a great way to
maximize your impact - we will guide you towards the most prominent architectures that are missing on the TensorFlow
side. If the specific model you want to use with TensorFlow already has a TensorFlow architecture implementation in
🤗 Transformers but is lacking weights, feel free to jump straight into the
weight conversion section
of this page.
For simplicity, the remainder of this guide assumes you've decided to contribute with the TensorFlow version of
BrandNewBert (the same example as in the guide to add a new model from scratch).

Before starting the work on a TensorFlow model architecture, double-check that there is no ongoing effort to do so.
You can search for BrandNewBert on the
pull request GitHub page to confirm that there is no
TensorFlow-related pull request.

2. Prepare transformers dev environment
Having selected the model architecture, open an draft PR to signal your intention to work on it. Follow the
instructions below to set up your environment and open a draft PR.

Fork the repository by clicking on the 'Fork' button on the
   repository's page. This creates a copy of the code under your GitHub user account.

Clone your transformers fork to your local disk, and add the base repository as a remote:

git clone https://github.com/[your Github handle]/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git

Set up a development environment, for instance by running the following command:

python -m venv .env
source .env/bin/activate
pip install -e "".[dev]""
Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
failure with this command. If that's the case make sure to install TensorFlow then do:

pip install -e "".[quality]""
Note: You don't need to have CUDA installed. Making the new model work on CPU is sufficient.

Create a branch with a descriptive name from your main branch

git checkout -b add_tf_brand_new_bert

Fetch and rebase to current main

git fetch upstream
git rebase upstream/main

Add an empty .py file in transformers/src/models/brandnewbert/ named modeling_tf_brandnewbert.py. This will
be your TensorFlow model file.

Push the changes to your account using:

git add .
git commit -m ""initial commit""
git push -u origin add_tf_brand_new_bert

Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the
   GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for
   future changes.

Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page.

Now you have set up a development environment to port BrandNewBert to TensorFlow in 🤗 Transformers.
3. (Optional) Understand theoretical aspects and the existing implementation
You should take some time to read BrandNewBert's paper, if such descriptive work exists. There might be large
sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is
not to get a deep theoretical understanding of the paper, but to extract the necessary information required to
effectively re-implement the model in 🤗 Transformers using TensorFlow. That being said, you don't have to spend too
much time on the theoretical aspects, but rather focus on the practical ones, namely the existing model documentation
page (e.g. model docs for BERT).
After you've grasped the basics of the models you are about to implement, it's important to understand the existing
implementation. This is a great chance to confirm that a working implementation matches your expectations for the
model, as well as to foresee technical challenges on the TensorFlow side.
It's perfectly natural that you feel overwhelmed with the amount of information that you've just absorbed. It is
definitely not a requirement that you understand all facets of the model at this stage. Nevertheless, we highly
encourage you to clear any pressing questions in our forum.
4. Model implementation
Now it's time to finally start coding. Our suggested starting point is the PyTorch file itself: copy the contents of
modeling_brand_new_bert.py inside src/transformers/models/brand_new_bert/ into
modeling_tf_brand_new_bert.py. The goal of this section is to modify the file and update the import structure of
🤗 Transformers such that you can import TFBrandNewBert and
TFBrandNewBert.from_pretrained(model_repo, from_pt=True) successfully loads a working TensorFlow BrandNewBert model.
Sadly, there is no prescription to convert a PyTorch model into TensorFlow. You can, however, follow our selection of
tips to make the process as smooth as possible:
- Prepend TF to the name of all classes (e.g. BrandNewBert becomes TFBrandNewBert).
- Most PyTorch operations have a direct TensorFlow replacement. For example, torch.nn.Linear corresponds to
  tf.keras.layers.Dense, torch.nn.Dropout corresponds to tf.keras.layers.Dropout, etc. If you're not sure
  about a specific operation, you can use the TensorFlow documentation
  or the PyTorch documentation.
- Look for patterns in the 🤗 Transformers codebase. If you come across a certain operation that doesn't have a direct
   replacement, the odds are that someone else already had the same problem.
- By default, keep the same variable names and structure as in PyTorch. This will make it easier to debug, track
   issues, and add fixes down the line.
- Some layers have different default values in each framework. A notable example is the batch normalization layer's
   epsilon (1e-5 in PyTorch
   and 1e-3 in TensorFlow).
   Double-check the documentation!
- PyTorch's nn.Parameter variables typically need to be initialized within TF Layer's build(). See the following
   example: PyTorch /
   TensorFlow
- If the PyTorch model has a #copied from  on top of a function, the odds are that your TensorFlow model can also
   borrow that function from the architecture it was copied from, assuming it has a TensorFlow architecture.
- Assigning the name attribute correctly in TensorFlow functions is critical to do the from_pt=True weight
   cross-loading. name is almost always the name of the corresponding variable in the PyTorch code. If name is not
   properly set, you will see it in the error message when loading the model weights.
- The logic of the base model class, BrandNewBertModel, will actually reside in TFBrandNewBertMainLayer, a Keras
   layer subclass (example).
   TFBrandNewBertModel will simply be a wrapper around this layer.
- Keras models need to be built in order to load pretrained weights. For that reason, TFBrandNewBertPreTrainedModel
   will need to hold an example of inputs to the model, the dummy_inputs
   (example).
- If you get stuck, ask for help - we're here to help you! 🤗
In addition to the model file itself, you will also need to add the pointers to the model classes and related
documentation pages. You can complete this part entirely following the patterns in other PRs
(example). Here's a list of the needed manual
changes:
- Include all public classes of BrandNewBert in src/transformers/__init__.py
- Add BrandNewBert classes to the corresponding Auto classes in src/transformers/models/auto/modeling_tf_auto.py
- Include the modeling file in the documentation test file list in utils/documentation_tests.txt
- Add the lazy loading classes related to BrandNewBert in src/transformers/utils/dummy_tf_objects.py
- Update the import structures for the public classes in src/transformers/models/brand_new_bert/__init__.py
- Add the documentation pointers to the public methods of BrandNewBert in docs/source/en/model_doc/brand_new_bert.md
- Add yourself to the list of contributors to BrandNewBert in docs/source/en/model_doc/brand_new_bert.md
- Finally, add a green tick ✅ to the TensorFlow column of BrandNewBert in docs/source/en/index.md
When you're happy with your implementation, run the following checklist to confirm that your model architecture is
ready:
1. All layers that behave differently at train time (e.g. Dropout) are called with a training argument, which is
propagated all the way from the top-level classes
2. You have used #copied from  whenever possible
3. TFBrandNewBertMainLayer and all classes that use it have their call function decorated with @unpack_inputs
4. TFBrandNewBertMainLayer is decorated with @keras_serializable
5. A TensorFlow model can be loaded from PyTorch weights using TFBrandNewBert.from_pretrained(model_repo, from_pt=True)
6. You can call the TensorFlow model using the expected input format
5. Add model tests
Hurray, you've implemented a TensorFlow model! Now it's time to add tests to make sure that your model behaves as
expected. As in the previous section, we suggest you start by copying the test_modeling_brand_new_bert.py file in
tests/models/brand_new_bert/ into test_modeling_tf_brand_new_bert.py, and continue by making the necessary
TensorFlow replacements. For now, in all .from_pretrained() calls, you should use the from_pt=True flag to load
the existing PyTorch weights.
After you're done, it's time for the moment of truth: run the tests! 😬

NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \
py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py
The most likely outcome is that you'll see a bunch of errors. Don't worry, this is expected! Debugging ML models is
notoriously hard, and the key ingredient to success is patience (and breakpoint()). In our experience, the hardest
problems arise from subtle mismatches between ML frameworks, for which we have a few pointers at the end of this guide.
In other cases, a general test might not be directly applicable to your model, in which case we suggest an override
at the model test class level. Regardless of the issue, don't hesitate to ask for help in your draft pull request if
you're stuck.
When all tests pass, congratulations, your model is nearly ready to be added to the 🤗 Transformers library! 🎉
6.-7. Ensure everyone can use your model
6. Submit the pull request
Once you're done with the implementation and the tests, it's time to submit a pull request. Before pushing your code,
run our code formatting utility, make fixup 🪄. This will automatically fix any formatting issues, which would cause
our automatic checks to fail.
It's now time to convert your draft pull request into a real pull request. To do so, click on the ""Ready for
review"" button and add Joao (@gante) and Matt (@Rocketknight1) as reviewers. A model pull request will need
at least 3 reviewers, but they will take care of finding appropriate additional reviewers for your model.
After all reviewers are happy with the state of your PR, the final action point is to remove the from_pt=True flag in
.from_pretrained() calls. Since there are no TensorFlow weights, you will have to add them! Check the section
below for instructions on how to do it.
Finally, when the TensorFlow weights get merged, you have at least 3 reviewer approvals, and all CI checks are
green, double-check the tests locally one last time

NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \
py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py
and we will merge your PR! Congratulations on the milestone 🎉
7. (Optional) Build demos and share with the world
One of the hardest parts about open-source is discovery. How can the other users learn about the existence of your
fabulous TensorFlow contribution? With proper communication, of course! 📣
There are two main ways to share your model with the community:
- Build demos. These include Gradio demos, notebooks, and other fun ways to show off your model. We highly
   encourage you to add a notebook to our community-driven demos.
- Share stories on social media like Twitter and LinkedIn. You should be proud of your work and share
   your achievement with the community - your model can now be used by thousands of engineers and researchers around
   the world 🌍! We will be happy to retweet your posts and help you share your work with the community.
Adding TensorFlow weights to 🤗 Hub
Assuming that the TensorFlow model architecture is available in 🤗 Transformers, converting PyTorch weights into
TensorFlow weights is a breeze!
Here's how to do it:
1. Make sure you are logged into your Hugging Face account in your terminal. You can log in using the command
   huggingface-cli login (you can find your access tokens here)
2. Run transformers-cli pt-to-tf --model-name foo/bar, where foo/bar is the name of the model repository
   containing the PyTorch weights you want to convert
3. Tag @joaogante and @Rocketknight1 in the 🤗 Hub PR the command above has just created
That's it! 🎉
Debugging mismatches across ML frameworks 🐛
At some point, when adding a new architecture or when creating TensorFlow weights for an existing architecture, you
might come across errors compaining about mismatches between PyTorch and TensorFlow. You might even decide to open the
model architecture code for the two frameworks, and find that they look identical. What's going on? 🤔
First of all, let's talk about why understanding these mismatches matters. Many community members will use 🤗
Transformers models out of the box, and trust that our models behave as expected. When there is a large mismatch
between the two frameworks, it implies that the model is not following the reference implementation for at least one
of the frameworks. This might lead to silent failures, in which the model runs but has poor performance. This is
arguably worse than a model that fails to run at all! To that end, we aim at having a framework mismatch smaller than
1e-5 at all stages of the model.
As in other numerical problems, the devil is in the details. And as in any detail-oriented craft, the secret
ingredient here is patience. Here is our suggested workflow for when you come across this type of issues:
1. Locate the source of mismatches. The model you're converting probably has near identical inner variables up to a
   certain point. Place breakpoint() statements in the two frameworks' architectures, and compare the values of the
   numerical variables in a top-down fashion until you find the source of the problems.
2. Now that you've pinpointed the source of the issue, get in touch with the 🤗 Transformers team. It is possible
   that we've seen a similar problem before and can promptly provide a solution. As a fallback, scan popular pages
   like StackOverflow and GitHub issues.
3. If there is no solution in sight, it means you'll have to go deeper. The good news is that you've located the
   issue, so you can focus on the problematic instruction, abstracting away the rest of the model! The bad news is
   that you'll have to venture into the source implementation of said instruction. In some cases, you might find an
   issue with a reference implementation - don't abstain from opening an issue in the upstream repository.
In some cases, in dicussion with the 🤗 Transformers team, we might find that the fixing the mismatch is infeasible.
When the mismatch is very small in the output layers of the model (but potentially large in the hidden states), we
might decide to ignore it in favor of distributing the model. The pt-to-tf CLI mentioned above has a --max-error
flag to override the error message at weight conversion time."
"
Installation
Install 🤗 Transformers for whichever deep learning library you're working with, setup your cache, and optionally configure 🤗 Transformers to run offline.
🤗 Transformers is tested on Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, and Flax. Follow the installation instructions below for the deep learning library you are using:

PyTorch installation instructions.
TensorFlow 2.0 installation instructions.
Flax installation instructions.

Install with pip
You should install 🤗 Transformers in a virtual environment. If you're unfamiliar with Python virtual environments, take a look at this guide. A virtual environment makes it easier to manage different projects, and avoid compatibility issues between dependencies.
Start by creating a virtual environment in your project directory:

python -m venv .env
Activate the virtual environment. On Linux and MacOs:

source .env/bin/activate
Activate Virtual environment on Windows

.env/Scripts/activate
Now you're ready to install 🤗 Transformers with the following command:

pip install transformers
For CPU-support only, you can conveniently install 🤗 Transformers and a deep learning library in one line. For example, install 🤗 Transformers and PyTorch with:

pip install 'transformers[torch]'
🤗 Transformers and TensorFlow 2.0:

pip install 'transformers[tf-cpu]'

M1 / ARM Users
You will need to install the following before installing TensorFLow 2.0
brew install cmake
brew install pkg-config

🤗 Transformers and Flax:

pip install 'transformers[flax]'
Finally, check if 🤗 Transformers has been properly installed by running the following command. It will download a pretrained model:

python -c ""from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))""
Then print out the label and score:

[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
Install from source
Install 🤗 Transformers from source with the following command:

pip install git+https://github.com/huggingface/transformers
This command installs the bleeding edge main version rather than the latest stable version. The main version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. However, this means the main version may not always be stable. We strive to keep the main version operational, and most issues are usually resolved within a few hours or a day. If you run into a problem, please open an Issue so we can fix it even sooner!
Check if 🤗 Transformers has been properly installed by running the following command:

python -c ""from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))""
Editable install
You will need an editable install if you'd like to:

Use the main version of the source code.
Contribute to 🤗 Transformers and need to test changes in the code.

Clone the repository and install 🤗 Transformers with the following commands:

git clone https://github.com/huggingface/transformers.git
cd transformers
pip install -e .
These commands will link the folder you cloned the repository to and your Python library paths. Python will now look inside the folder you cloned to in addition to the normal library paths. For example, if your Python packages are typically installed in ~/anaconda3/envs/main/lib/python3.7/site-packages/, Python will also search the folder you cloned to: ~/transformers/.

You must keep the transformers folder if you want to keep using the library.

Now you can easily update your clone to the latest version of 🤗 Transformers with the following command:

cd ~/transformers/
git pull
Your Python environment will find the main version of 🤗 Transformers on the next run.
Install with conda
Install from the conda channel huggingface:

conda install -c huggingface transformers
Cache setup
Pretrained models are downloaded and locally cached at: ~/.cache/huggingface/hub. This is the default directory given by the shell environment variable TRANSFORMERS_CACHE. On Windows, the default directory is given by C:\Users\username\.cache\huggingface\hub. You can change the shell environment variables shown below - in order of priority - to specify a different cache directory:

Shell environment variable (default): HUGGINGFACE_HUB_CACHE or TRANSFORMERS_CACHE.
Shell environment variable: HF_HOME.
Shell environment variable: XDG_CACHE_HOME + /huggingface.

🤗 Transformers will use the shell environment variables PYTORCH_TRANSFORMERS_CACHE or PYTORCH_PRETRAINED_BERT_CACHE if you are coming from an earlier iteration of this library and have set those environment variables, unless you specify the shell environment variable TRANSFORMERS_CACHE.

Offline mode
🤗 Transformers is able to run in a firewalled or offline environment by only using local files. Set the environment variable TRANSFORMERS_OFFLINE=1 to enable this behavior.

Add 🤗 Datasets to your offline training workflow by setting the environment variable HF_DATASETS_OFFLINE=1.

For example, you would typically run a program on a normal network firewalled to external instances with the following command:

python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en 
Run this same program in an offline instance with:

HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en 
The script should now run without hanging or waiting to timeout because it knows it should only look for local files.
Fetch models and tokenizers to use offline
Another option for using 🤗 Transformers offline is to download the files ahead of time, and then point to their local path when you need to use them offline. There are three ways to do this:

Download a file through the user interface on the Model Hub by clicking on the ↓ icon.

Use the [PreTrainedModel.from_pretrained] and [PreTrainedModel.save_pretrained] workflow:

Download your files ahead of time with [PreTrainedModel.from_pretrained]:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained(""bigscience/T0_3B"")
model = AutoModelForSeq2SeqLM.from_pretrained(""bigscience/T0_3B"")

Save your files to a specified directory with [PreTrainedModel.save_pretrained]:

tokenizer.save_pretrained(""./your/path/bigscience_t0"")
model.save_pretrained(""./your/path/bigscience_t0"")

Now when you're offline, reload your files with [PreTrainedModel.from_pretrained] from the specified directory:

tokenizer = AutoTokenizer.from_pretrained(""./your/path/bigscience_t0"")
model = AutoModel.from_pretrained(""./your/path/bigscience_t0"")

Programmatically download files with the huggingface_hub library:

Install the huggingface_hub library in your virtual environment:

python -m pip install huggingface_hub

Use the hf_hub_download function to download a file to a specific path. For example, the following command downloads the config.json file from the T0 model to your desired path:

from huggingface_hub import hf_hub_download
hf_hub_download(repo_id=""bigscience/T0_3B"", filename=""config.json"", cache_dir=""./your/path/bigscience_t0"")

Once your file is downloaded and locally cached, specify it's local path to load and use it:

from transformers import AutoConfig
config = AutoConfig.from_pretrained(""./your/path/bigscience_t0/config.json"")

See the How to download files from the Hub section for more details on downloading files stored on the Hub.
"
"
The Transformer model family
Since its introduction in 2017, the original Transformer model has inspired many new and exciting models that extend beyond natural language processing (NLP) tasks. There are models for predicting the folded structure of proteins, training a cheetah to run, and time series forecasting. With so many Transformer variants available, it can be easy to miss the bigger picture. What all these models have in common is they're based on the original Transformer architecture. Some models only use the encoder or decoder, while others use both. This provides a useful taxonomy to categorize and examine the high-level differences within models in the Transformer family, and it'll help you understand Transformers you haven't encountered before.
If you aren't familiar with the original Transformer model or need a refresher, check out the How do Transformers work chapter from the Hugging Face course.

Computer vision

Convolutional network
For a long time, convolutional networks (CNNs) were the dominant paradigm for computer vision tasks until the Vision Transformer demonstrated its scalability and efficiency. Even then, some of a CNN's best qualities, like translation invariance, are so powerful (especially for certain tasks) that some Transformers incorporate convolutions in their architecture. ConvNeXt flipped this exchange around and incorporated design choices from Transformers to modernize a CNN. For example, ConvNeXt uses non-overlapping sliding windows to patchify an image and a larger kernel to increase its global receptive field. ConvNeXt also makes several layer design choices to be more memory-efficient and improve performance, so it competes favorably with Transformers!
Encoder[[cv-encoder]]
The Vision Transformer (ViT) opened the door to computer vision tasks without convolutions. ViT uses a standard Transformer encoder, but its main breakthrough was how it treated an image. It splits an image into fixed-size patches and uses them to create an embedding, just like how a sentence is split into tokens. ViT capitalized on the Transformers' efficient architecture to demonstrate competitive results with the CNNs at the time while requiring fewer resources to train. ViT was soon followed by other vision models that could also handle dense vision tasks like segmentation as well as detection.
One of these models is the Swin Transformer. It builds hierarchical feature maps (like a CNN 👀 and unlike ViT) from smaller-sized patches and merges them with neighboring patches in deeper layers. Attention is only computed within a local window, and the window is shifted between attention layers to create connections to help the model learn better. Since the Swin Transformer can produce hierarchical feature maps, it is a good candidate for dense prediction tasks like segmentation and detection. The SegFormer also uses a Transformer encoder to build hierarchical feature maps, but it adds a simple multilayer perceptron (MLP) decoder on top to combine all the feature maps and make a prediction.
Other vision models, like BeIT and ViTMAE, drew inspiration from BERT's pretraining objective. BeIT is pretrained by masked image modeling (MIM); the image patches are randomly masked, and the image is also tokenized into visual tokens. BeIT is trained to predict the visual tokens corresponding to the masked patches. ViTMAE has a similar pretraining objective, except it must predict the pixels instead of visual tokens. What's unusual is 75% of the image patches are masked! The decoder reconstructs the pixels from the masked tokens and encoded patches. After pretraining, the decoder is thrown away, and the encoder is ready to be used in downstream tasks.
Decoder[[cv-decoder]]
Decoder-only vision models are rare because most vision models rely on an encoder to learn an image representation. But for use cases like image generation, the decoder is a natural fit, as we've seen from text generation models like GPT-2. ImageGPT uses the same architecture as GPT-2, but instead of predicting the next token in a sequence, it predicts the next pixel in an image. In addition to image generation, ImageGPT could also be finetuned for image classification.
Encoder-decoder[[cv-encoder-decoder]]
Vision models commonly use an encoder (also known as a backbone) to extract important image features before passing them to a Transformer decoder. DETR has a pretrained backbone, but it also uses the complete Transformer encoder-decoder architecture for object detection. The encoder learns image representations and combines them with object queries (each object query is a learned embedding that focuses on a region or object in an image) in the decoder. DETR predicts the bounding box coordinates and class label for each object query.
Natural language processing

Encoder[[nlp-encoder]]
BERT is an encoder-only Transformer that randomly masks certain tokens in the input to avoid seeing other tokens, which would allow it to ""cheat"". The pretraining objective is to predict the masked token based on the context. This allows BERT to fully use the left and right contexts to help it learn a deeper and richer representation of the inputs. However, there was still room for improvement in BERT's pretraining strategy. RoBERTa improved upon this by introducing a new pretraining recipe that includes training for longer and on larger batches, randomly masking tokens at each epoch instead of just once during preprocessing, and removing the next-sentence prediction objective. 
The dominant strategy to improve performance is to increase the model size. But training large models is computationally expensive. One way to reduce computational costs is using a smaller model like DistilBERT. DistilBERT uses knowledge distillation - a compression technique - to create a smaller version of BERT while keeping nearly all of its language understanding capabilities. 
However, most Transformer models continued to trend towards more parameters, leading to new models focused on improving training efficiency. ALBERT reduces memory consumption by lowering the number of parameters in two ways: separating the larger vocabulary embedding into two smaller matrices and allowing layers to share parameters. DeBERTa added a disentangled attention mechanism where the word and its position are separately encoded in two vectors. The attention is computed from these separate vectors instead of a single vector containing the word and position embeddings. Longformer also focused on making attention more efficient, especially for processing documents with longer sequence lengths. It uses a combination of local windowed attention (attention only calculated from fixed window size around each token) and global attention (only for specific task tokens like [CLS] for classification) to create a sparse attention matrix instead of a full attention matrix.
Decoder[[nlp-decoder]]
GPT-2 is a decoder-only Transformer that predicts the next word in the sequence. It masks tokens to the right so the model can't ""cheat"" by looking ahead. By pretraining on a massive body of text, GPT-2 became really good at generating text, even if the text is only sometimes accurate or true. But GPT-2 lacked the bidirectional context from BERT's pretraining, which made it unsuitable for certain tasks. XLNET combines the best of both BERT and GPT-2's pretraining objectives by using a permutation language modeling objective (PLM) that allows it to learn bidirectionally.
After GPT-2, language models grew even bigger and are now known as large language models (LLMs). LLMs demonstrate few- or even zero-shot learning if pretrained on a large enough dataset. GPT-J is an LLM with 6B parameters and trained on 400B tokens. GPT-J was followed by OPT, a family of decoder-only models, the largest of which is 175B and trained on 180B tokens. BLOOM was released around the same time, and the largest model in the family has 176B parameters and is trained on 366B tokens in 46 languages and 13 programming languages.
Encoder-decoder[[nlp-encoder-decoder]]
BART keeps the original Transformer architecture, but it modifies the pretraining objective with text infilling corruption, where some text spans are replaced with a single mask token. The decoder predicts the uncorrupted tokens (future tokens are masked) and uses the encoder's hidden states to help it. Pegasus is similar to BART, but Pegasus masks entire sentences instead of text spans. In addition to masked language modeling, Pegasus is pretrained by gap sentence generation (GSG). The GSG objective masks whole sentences important to a document, replacing them with a mask token. The decoder must generate the output from the remaining sentences. T5 is a more unique model that casts all NLP tasks into a text-to-text problem using specific prefixes. For example, the prefix Summarize: indicates a summarization task. T5 is pretrained by supervised (GLUE and SuperGLUE) training and self-supervised training (randomly sample and drop out 15% of tokens).
Audio

Encoder[[audio-encoder]]
Wav2Vec2 uses a Transformer encoder to learn speech representations directly from raw audio waveforms. It is pretrained with a contrastive task to determine the true speech representation from a set of false ones. HuBERT is similar to Wav2Vec2 but has a different training process. Target labels are created by a clustering step in which segments of similar audio are assigned to a cluster which becomes a hidden unit. The hidden unit is mapped to an embedding to make a prediction.
Encoder-decoder[[audio-encoder-decoder]]
Speech2Text is a speech model designed for automatic speech recognition (ASR) and speech translation. The model accepts log mel-filter bank features extracted from the audio waveform and pretrained autoregressively to generate a transcript or translation. Whisper is also an ASR model, but unlike many other speech models, it is pretrained on a massive amount of ✨ labeled ✨ audio transcription data for zero-shot performance. A large chunk of the dataset also contains non-English languages, meaning Whisper can also be used for low-resource languages. Structurally, Whisper is similar to Speech2Text. The audio signal is converted to a log-mel spectrogram encoded by the encoder. The decoder generates the transcript autoregressively from the encoder's hidden states and the previous tokens.
Multimodal

Encoder[[mm-encoder]]
VisualBERT is a multimodal model for vision-language tasks released shortly after BERT. It combines BERT and a pretrained object detection system to extract image features into visual embeddings, passed alongside text embeddings to BERT. VisualBERT predicts the masked text based on the unmasked text and the visual embeddings, and it also has to predict whether the text is aligned with the image. When ViT was released, ViLT adopted ViT in its architecture because it was easier to get the image embeddings this way. The image embeddings are jointly processed with the text embeddings. From there, ViLT is pretrained by image text matching, masked language modeling, and whole word masking.
CLIP takes a different approach and makes a pair prediction of (image, text) . An image encoder (ViT) and a text encoder (Transformer) are jointly trained on a 400 million (image, text) pair dataset to maximize the similarity between the image and text embeddings of the (image, text) pairs. After pretraining, you can use natural language to instruct CLIP to predict the text given an image or vice versa. OWL-ViT builds on top of CLIP by using it as its backbone for zero-shot object detection. After pretraining, an object detection head is added to make a set prediction over the (class, bounding box) pairs.
Encoder-decoder[[mm-encoder-decoder]]
Optical character recognition (OCR) is a long-standing text recognition task that typically involves several components to understand the image and generate the text. TrOCR simplifies the process using an end-to-end Transformer. The encoder is a ViT-style model for image understanding and processes the image as fixed-size patches. The decoder accepts the encoder's hidden states and autoregressively generates text. Donut is a more general visual document understanding model that doesn't rely on OCR-based approaches. It uses a Swin Transformer as the encoder and multilingual BART as the decoder. Donut is pretrained to read text by predicting the next word based on the image and text annotations. The decoder generates a token sequence given a prompt. The prompt is represented by a special token for each downstream task. For example, document parsing has a special parsing token that is combined with the encoder hidden states to parse the document into a structured output format (JSON).
Reinforcement learning

Decoder[[rl-decoder]]
The Decision and Trajectory Transformer casts the state, action, and reward as a sequence modeling problem. The Decision Transformer generates a series of actions that lead to a future desired return based on returns-to-go, past states, and actions. For the last K timesteps, each of the three modalities are converted into token embeddings and processed by a GPT-like model to predict a future action token. Trajectory Transformer also tokenizes the states, actions, and rewards and processes them with a GPT architecture. Unlike the Decision Transformer, which is focused on reward conditioning, the Trajectory Transformer generates future actions with beam search."
"
Summary of the tokenizers
[[open-in-colab]]
On this page, we will have a closer look at tokenization.

As we saw in the preprocessing tutorial, tokenizing a text is splitting it into words or
subwords, which then are converted to ids through a look-up table. Converting words or subwords to ids is
straightforward, so in this summary, we will focus on splitting a text into words or subwords (i.e. tokenizing a text).
More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: Byte-Pair Encoding
(BPE), WordPiece, and SentencePiece, and show examples
of which tokenizer type is used by which model.
Note that on each model page, you can look at the documentation of the associated tokenizer to know which tokenizer
type was used by the pretrained model. For instance, if we look at [BertTokenizer], we can see
that the model uses WordPiece.
Introduction
Splitting a text into smaller chunks is a task that is harder than it looks, and there are multiple ways of doing so.
For instance, let's look at the sentence ""Don't you love 🤗 Transformers? We sure do.""

A simple way of tokenizing this text is to split it by spaces, which would give:
[""Don't"", ""you"", ""love"", ""🤗"", ""Transformers?"", ""We"", ""sure"", ""do.""]
This is a sensible first step, but if we look at the tokens ""Transformers?"" and ""do."", we notice that the
punctuation is attached to the words ""Transformer"" and ""do"", which is suboptimal. We should take the
punctuation into account so that a model does not have to learn a different representation of a word and every possible
punctuation symbol that could follow it, which would explode the number of representations the model has to learn.
Taking punctuation into account, tokenizing our exemplary text would give:
[""Don"", ""'"", ""t"", ""you"", ""love"", ""🤗"", ""Transformers"", ""?"", ""We"", ""sure"", ""do"", "".""]
Better. However, it is disadvantageous, how the tokenization dealt with the word ""Don't"". ""Don't"" stands for
""do not"", so it would be better tokenized as [""Do"", ""n't""]. This is where things start getting complicated, and
part of the reason each model has its own tokenizer type. Depending on the rules we apply for tokenizing a text, a
different tokenized output is generated for the same text. A pretrained model only performs properly if you feed it an
input that was tokenized with the same rules that were used to tokenize its training data.
spaCy and Moses are two popular
rule-based tokenizers. Applying them on our example, spaCy and Moses would output something like:
[""Do"", ""n't"", ""you"", ""love"", ""🤗"", ""Transformers"", ""?"", ""We"", ""sure"", ""do"", "".""]
As can be seen space and punctuation tokenization, as well as rule-based tokenization, is used here. Space and
punctuation tokenization and rule-based tokenization are both examples of word tokenization, which is loosely defined
as splitting sentences into words. While it's the most intuitive way to split texts into smaller chunks, this
tokenization method can lead to problems for massive text corpora. In this case, space and punctuation tokenization
usually generates a very big vocabulary (the set of all unique words and tokens used). E.g., Transformer XL uses space and punctuation tokenization, resulting in a vocabulary size of 267,735!
Such a big vocabulary size forces the model to have an enormous embedding matrix as the input and output layer, which
causes both an increased memory and time complexity. In general, transformers models rarely have a vocabulary size
greater than 50,000, especially if they are pretrained only on a single language.
So if simple space and punctuation tokenization is unsatisfactory, why not simply tokenize on characters?

While character tokenization is very simple and would greatly reduce memory and time complexity it makes it much harder
for the model to learn meaningful input representations. E.g. learning a meaningful context-independent
representation for the letter ""t"" is much harder than learning a context-independent representation for the word
""today"". Therefore, character tokenization is often accompanied by a loss of performance. So to get the best of
both worlds, transformers models use a hybrid between word-level and character-level tokenization called subword
tokenization.
Subword tokenization

Subword tokenization algorithms rely on the principle that frequently used words should not be split into smaller
subwords, but rare words should be decomposed into meaningful subwords. For instance ""annoyingly"" might be
considered a rare word and could be decomposed into ""annoying"" and ""ly"". Both ""annoying"" and ""ly"" as
stand-alone subwords would appear more frequently while at the same time the meaning of ""annoyingly"" is kept by the
composite meaning of ""annoying"" and ""ly"". This is especially useful in agglutinative languages such as Turkish,
where you can form (almost) arbitrarily long complex words by stringing together subwords.
Subword tokenization allows the model to have a reasonable vocabulary size while being able to learn meaningful
context-independent representations. In addition, subword tokenization enables the model to process words it has never
seen before, by decomposing them into known subwords. For instance, the [~transformers.BertTokenizer] tokenizes
""I have a new GPU!"" as follows:

from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained(""bert-base-uncased"")
tokenizer.tokenize(""I have a new GPU!"")
[""i"", ""have"", ""a"", ""new"", ""gp"", ""##u"", ""!""]

Because we are considering the uncased model, the sentence was lowercased first. We can see that the words [""i"", ""have"", ""a"", ""new""] are present in the tokenizer's vocabulary, but the word ""gpu"" is not. Consequently, the
tokenizer splits ""gpu"" into known subwords: [""gp"" and ""##u""]. ""##"" means that the rest of the token should
be attached to the previous one, without space (for decoding or reversal of the tokenization).
As another example, [~transformers.XLNetTokenizer] tokenizes our previously exemplary text as follows:

from transformers import XLNetTokenizer
tokenizer = XLNetTokenizer.from_pretrained(""xlnet-base-cased"")
tokenizer.tokenize(""Don't you love 🤗 Transformers? We sure do."")
[""▁Don"", ""'"", ""t"", ""▁you"", ""▁love"", ""▁"", ""🤗"", ""▁"", ""Transform"", ""ers"", ""?"", ""▁We"", ""▁sure"", ""▁do"", "".""]

We'll get back to the meaning of those ""▁"" when we look at SentencePiece. As one can see,
the rare word ""Transformers"" has been split into the more frequent subwords ""Transform"" and ""ers"".
Let's now look at how the different subword tokenization algorithms work. Note that all of those tokenization
algorithms rely on some form of training which is usually done on the corpus the corresponding model will be trained
on.

Byte-Pair Encoding (BPE)
Byte-Pair Encoding (BPE) was introduced in Neural Machine Translation of Rare Words with Subword Units (Sennrich et
al., 2015). BPE relies on a pre-tokenizer that splits the training data into
words. Pretokenization can be as simple as space tokenization, e.g. GPT-2, Roberta. More advanced pre-tokenization include rule-based tokenization, e.g. XLM,
FlauBERT which uses Moses for most languages, or GPT which uses
Spacy and ftfy, to count the frequency of each word in the training corpus.
After pre-tokenization, a set of unique words has been created and the frequency with which each word occurred in the
training data has been determined. Next, BPE creates a base vocabulary consisting of all symbols that occur in the set
of unique words and learns merge rules to form a new symbol from two symbols of the base vocabulary. It does so until
the vocabulary has attained the desired vocabulary size. Note that the desired vocabulary size is a hyperparameter to
define before training the tokenizer.
As an example, let's assume that after pre-tokenization, the following set of words including their frequency has been
determined:
(""hug"", 10), (""pug"", 5), (""pun"", 12), (""bun"", 4), (""hugs"", 5)
Consequently, the base vocabulary is [""b"", ""g"", ""h"", ""n"", ""p"", ""s"", ""u""]. Splitting all words into symbols of the
base vocabulary, we obtain:
(""h"" ""u"" ""g"", 10), (""p"" ""u"" ""g"", 5), (""p"" ""u"" ""n"", 12), (""b"" ""u"" ""n"", 4), (""h"" ""u"" ""g"" ""s"", 5)
BPE then counts the frequency of each possible symbol pair and picks the symbol pair that occurs most frequently. In
the example above ""h"" followed by ""u"" is present 10 + 5 = 15 times (10 times in the 10 occurrences of
""hug"", 5 times in the 5 occurrences of ""hugs""). However, the most frequent symbol pair is ""u"" followed by
""g"", occurring 10 + 5 + 5 = 20 times in total. Thus, the first merge rule the tokenizer learns is to group all
""u"" symbols followed by a ""g"" symbol together. Next, ""ug"" is added to the vocabulary. The set of words then
becomes
(""h"" ""ug"", 10), (""p"" ""ug"", 5), (""p"" ""u"" ""n"", 12), (""b"" ""u"" ""n"", 4), (""h"" ""ug"" ""s"", 5)
BPE then identifies the next most common symbol pair. It's ""u"" followed by ""n"", which occurs 16 times. ""u"",
""n"" is merged to ""un"" and added to the vocabulary. The next most frequent symbol pair is ""h"" followed by
""ug"", occurring 15 times. Again the pair is merged and ""hug"" can be added to the vocabulary.
At this stage, the vocabulary is [""b"", ""g"", ""h"", ""n"", ""p"", ""s"", ""u"", ""ug"", ""un"", ""hug""] and our set of unique words
is represented as
(""hug"", 10), (""p"" ""ug"", 5), (""p"" ""un"", 12), (""b"" ""un"", 4), (""hug"" ""s"", 5)
Assuming, that the Byte-Pair Encoding training would stop at this point, the learned merge rules would then be applied
to new words (as long as those new words do not include symbols that were not in the base vocabulary). For instance,
the word ""bug"" would be tokenized to [""b"", ""ug""] but ""mug"" would be tokenized as [""<unk>"", ""ug""] since
the symbol ""m"" is not in the base vocabulary. In general, single letters such as ""m"" are not replaced by the
""<unk>"" symbol because the training data usually includes at least one occurrence of each letter, but it is likely
to happen for very special characters like emojis.
As mentioned earlier, the vocabulary size, i.e. the base vocabulary size + the number of merges, is a hyperparameter
to choose. For instance GPT has a vocabulary size of 40,478 since they have 478 base characters
and chose to stop training after 40,000 merges.
Byte-level BPE
A base vocabulary that includes all possible base characters can be quite large if e.g. all unicode characters are
considered as base characters. To have a better base vocabulary, GPT-2 uses bytes
as the base vocabulary, which is a clever trick to force the base vocabulary to be of size 256 while ensuring that
every base character is included in the vocabulary. With some additional rules to deal with punctuation, the GPT2's
tokenizer can tokenize every text without the need for the  symbol. GPT-2 has a vocabulary
size of 50,257, which corresponds to the 256 bytes base tokens, a special end-of-text token and the symbols learned
with 50,000 merges.

WordPiece
WordPiece is the subword tokenization algorithm used for BERT, DistilBERT, and Electra. The algorithm was outlined in Japanese and Korean
Voice Search (Schuster et al., 2012) and is very similar to
BPE. WordPiece first initializes the vocabulary to include every character present in the training data and
progressively learns a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent
symbol pair, but the one that maximizes the likelihood of the training data once added to the vocabulary.
So what does this mean exactly? Referring to the previous example, maximizing the likelihood of the training data is
equivalent to finding the symbol pair, whose probability divided by the probabilities of its first symbol followed by
its second symbol is the greatest among all symbol pairs. E.g. ""u"", followed by ""g"" would have only been
merged if the probability of ""ug"" divided by ""u"", ""g"" would have been greater than for any other symbol
pair. Intuitively, WordPiece is slightly different to BPE in that it evaluates what it loses by merging two symbols
to ensure it's worth it.

Unigram
Unigram is a subword tokenization algorithm introduced in Subword Regularization: Improving Neural Network Translation
Models with Multiple Subword Candidates (Kudo, 2018). In contrast to BPE or
WordPiece, Unigram initializes its base vocabulary to a large number of symbols and progressively trims down each
symbol to obtain a smaller vocabulary. The base vocabulary could for instance correspond to all pre-tokenized words and
the most common substrings. Unigram is not used directly for any of the models in the transformers, but it's used in
conjunction with SentencePiece.
At each training step, the Unigram algorithm defines a loss (often defined as the log-likelihood) over the training
data given the current vocabulary and a unigram language model. Then, for each symbol in the vocabulary, the algorithm
computes how much the overall loss would increase if the symbol was to be removed from the vocabulary. Unigram then
removes p (with p usually being 10% or 20%) percent of the symbols whose loss increase is the lowest, i.e. those
symbols that least affect the overall loss over the training data. This process is repeated until the vocabulary has
reached the desired size. The Unigram algorithm always keeps the base characters so that any word can be tokenized.
Because Unigram is not based on merge rules (in contrast to BPE and WordPiece), the algorithm has several ways of
tokenizing new text after training. As an example, if a trained Unigram tokenizer exhibits the vocabulary:
[""b"", ""g"", ""h"", ""n"", ""p"", ""s"", ""u"", ""ug"", ""un"", ""hug""],
""hugs"" could be tokenized both as [""hug"", ""s""], [""h"", ""ug"", ""s""] or [""h"", ""u"", ""g"", ""s""]. So which one
to choose? Unigram saves the probability of each token in the training corpus on top of saving the vocabulary so that
the probability of each possible tokenization can be computed after training. The algorithm simply picks the most
likely tokenization in practice, but also offers the possibility to sample a possible tokenization according to their
probabilities.
Those probabilities are defined by the loss the tokenizer is trained on. Assuming that the training data consists of
the words \(x_{1}, \dots, x_{N}\) and that the set of all possible tokenizations for a word \(x_{i}\) is
defined as \(S(x_{i})\), then the overall loss is defined as
$$\mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )$$

SentencePiece
All tokenization algorithms described so far have the same problem: It is assumed that the input text uses spaces to
separate words. However, not all languages use spaces to separate words. One possible solution is to use language
specific pre-tokenizers, e.g. XLM uses a specific Chinese, Japanese, and Thai pre-tokenizer).
To solve this problem more generally, SentencePiece: A simple and language independent subword tokenizer and
detokenizer for Neural Text Processing (Kudo et al., 2018) treats the input
as a raw input stream, thus including the space in the set of characters to use. It then uses the BPE or unigram
algorithm to construct the appropriate vocabulary.
The [XLNetTokenizer] uses SentencePiece for example, which is also why in the example earlier the
""▁"" character was included in the vocabulary. Decoding with SentencePiece is very easy since all tokens can just be
concatenated and ""▁"" is replaced by a space.
All transformers models in the library that use SentencePiece use it in combination with unigram. Examples of models
using SentencePiece are ALBERT, XLNet, Marian, and T5."
"
Custom Tools and Prompts

If you are not aware of what tools and agents are in the context of transformers, we recommend you read the
Transformers Agents page first.

Transformers Agent is an experimental API that is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.

Creating and using custom tools and prompts is paramount to empowering the agent and having it perform new tasks.
In this guide we'll take a look at:

How to customize the prompt
How to use custom tools
How to create custom tools

Customizing the prompt
As explained in Transformers Agents agents can run in [~Agent.run] and [~Agent.chat] mode.
Both the run and chat modes underlie the same logic. The language model powering the agent is conditioned on a long 
prompt and completes the prompt by generating the next tokens until the stop token is reached.
The only difference between the two modes is that during the chat mode the prompt is extended with 
previous user inputs and model generations. This allows the agent to have access to past interactions,
seemingly giving the agent some kind of memory.
Structure of the prompt
Let's take a closer look at how the prompt is structured to understand how it can be best customized.
The prompt is structured broadly into four parts.

Introduction: how the agent should behave, explanation of the concept of tools.

Description of all the tools. This is defined by a <<all_tools>> token that is dynamically replaced at runtime with the tools defined/chosen by the user.

A set of examples of tasks and their solution

Current example, and request for solution.

To better understand each part, let's look at a shortened version of how the run prompt can look like:
````text
I will ask you to perform a task, your job is to come up with a series of simple commands in Python that will perform the task.
[]
You can print intermediate results if it makes sense to do so.
Tools:
- document_qa: This is a tool that answers a question about a document (pdf). It takes an input named document which should be the document containing the information, as well as a question that is the question about the document. It returns a text that contains the answer to the question.
- image_captioner: This is a tool that generates a description of an image. It takes an input named image which should be the image to the caption and returns a text that contains the description in English.
[]
Task: ""Answer the question in the variable question about the image stored in the variable image. The question is in French.""
I will use the following tools: translator to translate the question into English and then image_qa to answer the question on the input image.
Answer:
py
translated_question = translator(question=question, src_lang=""French"", tgt_lang=""English"")
print(f""The translated question is {translated_question}."")
answer = image_qa(image=image, question=translated_question)
print(f""The answer is {answer}"")
Task: ""Identify the oldest person in the document and create an image showcasing the result as a banner.""
I will use the following tools: document_qa to find the oldest person in the document, then image_generator to generate an image according to the answer.
Answer:
py
answer = document_qa(document, question=""What is the oldest person?"")
print(f""The answer is {answer}."")
image = image_generator(""A banner showing "" + answer)
[]
Task: ""Draw me a picture of rivers and lakes""
I will use the following
`
The introduction (the text before ""Tools:"") explains precisely how the model shall behave and what it should do.
This part most likely does not need to be customized as the agent shall always behave the same way.
The second part (the bullet points below ""Tools"") is dynamically added upon calling run or chat. There are 
exactly as many bullet points as there are tools in agent.toolbox and each bullet point consists of the name 
and description of the tool:
text
- <tool.name>: <tool.description>
Let's verify this quickly by loading the document_qa tool and printing out the name and description.

from transformers import load_tool
document_qa = load_tool(""document-question-answering"")
print(f""- {document_qa.name}: {document_qa.description}"")

which gives:
text
- document_qa: This is a tool that answers a question about a document (pdf). It takes an input named `document` which should be the document containing the information, as well as a `question` that is the question about the document. It returns a text that contains the answer to the question.
We can see that the tool name is short and precise. The description includes two parts, the first explaining 
what the tool does and the second states what input arguments and return values are expected.
A good tool name and tool description are very important for the agent to correctly use it. Note that the only
information the agent has about the tool is its name and description, so one should make sure that both 
are precisely written and match the style of the existing tools in the toolbox. In particular make sure the description
mentions all the arguments expected by name in code-style, along with the expected type and a description of what they
are.

Check the naming and description of the curated Transformers tools to better understand what name and 
description a tool is expected to have. You can see all tools with the [Agent.toolbox] property.

The third part includes a set of curated examples that show the agent exactly what code it should produce
for what kind of user request. The large language models empowering the agent are extremely good at 
recognizing patterns in a prompt and repeating the pattern with new data. Therefore, it is very important
that the examples are written in a way that maximizes the likelihood of the agent to generating correct,
executable code in practice. 
Let's have a look at one example:
```text
Task: ""Identify the oldest person in thedocument` and create an image showcasing the result as a banner.""
I will use the following tools: document_qa to find the oldest person in the document, then image_generator to generate an image according to the answer.
Answer:
py
answer = document_qa(document, question=""What is the oldest person?"")
print(f""The answer is {answer}."")
image = image_generator(""A banner showing "" + answer)
`
The pattern the model is prompted to repeat has three parts: The task statement, the agent's explanation of 
what it intends to do, and finally the generated code. Every example that is part of the prompt has this exact 
pattern, thus making sure that the agent will reproduce exactly the same pattern when generating new tokens.
The prompt examples are curated by the Transformers team and rigorously evaluated on a set of 
problem statements
to ensure that the agent's prompt is as good as possible to solve real use cases of the agent.
The final part of the prompt corresponds to:
```text
Task: ""Draw me a picture of rivers and lakes""
I will use the following

is a final and unfinished example that the agent is tasked to complete. The unfinished example
is dynamically created based on the actual user input. For the above example, the user ran:
py
agent.run(""Draw me a picture of rivers and lakes"")
The user input - a.k.a the task: ""Draw me a picture of rivers and lakes"" is cast into the 
prompt template: ""Task:  \n\n I will use the following"". This sentence makes up the final lines of the 
prompt the agent is conditioned on, therefore strongly influencing the agent to finish the example 
exactly in the same way it was previously done in the examples.
Without going into too much detail, the chat template has the same prompt structure with the 
examples having a slightly different style, e.g.:
````text
[]
=====
Human: Answer the question in the variable question about the image stored in the variable image.
Assistant: I will use the tool image_qa to answer the question on the input image.
py
answer = image_qa(text=question, image=image)
print(f""The answer is {answer}"")
Human: I tried this code, it worked but didn't give me a good result. The question is in French
Assistant: In this case, the question needs to be translated first. I will use the tool translator to do this.
py
translated_question = translator(question=question, src_lang=""French"", tgt_lang=""English"")
print(f""The translated question is {translated_question}."")
answer = image_qa(text=translated_question, image=image)
print(f""The answer is {answer}"")
=====
[]
`
Contrary, to the examples of the run prompt, each chat prompt example has one or more exchanges between the 
Human and the Assistant. Every exchange is structured similarly to the example of the run prompt. 
The user's input is appended to behind Human: and the agent is prompted to first generate what needs to be done 
before generating code. An exchange can be based on previous exchanges, therefore allowing the user to refer
to past exchanges as is done e.g. above by the user's input of ""I tried this code"" refers to the 
previously generated code of the agent.
Upon running .chat, the user's input or task is cast into an unfinished example of the form:
text
Human: <user-input>\n\nAssistant:
which the agent completes. Contrary to the run command, the chat command then appends the completed example
to the prompt, thus giving the agent more context for the next chat turn.
Great now that we know how the prompt is structured, let's see how we can customize it!
Writing good user inputs
While large language models are getting better and better at understanding users' intentions, it helps 
enormously to be as precise as possible to help the agent pick the correct task. What does it mean to be 
as precise as possible?
The agent sees a list of tool names and their description in its prompt. The more tools are added the 
more difficult it becomes for the agent to choose the correct tool and it's even more difficult to choose
the correct sequences of tools to run. Let's look at a common failure case, here we will only return 
the code to analyze it.

from transformers import HfAgent
agent = HfAgent(""https://api-inference.huggingface.co/models/bigcode/starcoder"")
agent.run(""Show me a tree"", return_code=True)

gives:
``text
==Explanation from the agent==
I will use the following tool:image_segmenter` to create a segmentation mask for the image.
==Code generated by the agent==
mask = image_segmenter(image, prompt=""tree"")

which is probably not what we wanted. Instead, it is more likely that we want an image of a tree to be generated.
To steer the agent more towards using a specific tool it can therefore be very helpful to use important keywords that 
are present in the tool's name and description. Let's have a look.
py
agent.toolbox[""image_generator""].description
text
'This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which contains the image description and outputs an image.
The name and description make use of the keywords ""image"", ""prompt"", ""create"" and ""generate"". Using these words will most likely work better here. Let's refine our prompt a bit.
py
agent.run(""Create an image of a tree"", return_code=True)
gives:
``text
==Explanation from the agent==
I will use the following toolimage_generator` to generate an image of a tree.
==Code generated by the agent==
image = image_generator(prompt=""tree"")

Much better! That looks more like what we want. In short, when you notice that the agent struggles to 
correctly map your task to the correct tools, try looking up the most pertinent keywords of the tool's name
and description and try refining your task request with it.
Customizing the tool descriptions
As we've seen before the agent has access to each of the tools' names and descriptions. The base tools 
should have very precise names and descriptions, however, you might find that it could help to change the 
the description or name of a tool for your specific use case. This might become especially important 
when you've added multiple tools that are very similar or if you want to use your agent only for a certain 
domain, e.g. image generation and transformations.
A common problem is that the agent confuses image generation with image transformation/modification when 
used a lot for image generation tasks, e.g.
py
agent.run(""Make an image of a house and a car"", return_code=True)
returns
``text
==Explanation from the agent== 
I will use the following toolsimage_generatorto generate an image of a house andimage_transformer` to transform the image of a car into the image of a house.
==Code generated by the agent==
house_image = image_generator(prompt=""A house"")
car_image = image_generator(prompt=""A car"")
house_car_image = image_transformer(image=car_image, prompt=""A house"")

which is probably not exactly what we want here. It seems like the agent has a difficult time 
to understand the difference between image_generator and image_transformer and often uses the two together.
We can help the agent here by changing the tool name and description of image_transformer. Let's instead call it modifier
to disassociate it a bit from ""image"" and ""prompt"":
py
agent.toolbox[""modifier""] = agent.toolbox.pop(""image_transformer"")
agent.toolbox[""modifier""].description = agent.toolbox[""modifier""].description.replace(
    ""transforms an image according to a prompt"", ""modifies an image""
)
Now ""modify"" is a strong cue to use the new image processor which should help with the above prompt. Let's run it again.
py
agent.run(""Make an image of a house and a car"", return_code=True)
Now we're getting:
``text
==Explanation from the agent==
I will use the following tools:image_generatorto generate an image of a house, thenimage_generator` to generate an image of a car.
==Code generated by the agent==
house_image = image_generator(prompt=""A house"")
car_image = image_generator(prompt=""A car"")

which is definitely closer to what we had in mind! However, we want to have both the house and car in the same image. Steering the task more toward single image generation should help:
py
agent.run(""Create image: 'A house and car'"", return_code=True)
``text
==Explanation from the agent==
I will use the following tool:image_generator` to generate an image.
==Code generated by the agent==
image = image_generator(prompt=""A house and car"")

Agents are still brittle for many use cases, especially when it comes to 
slightly more complex use cases like generating an image of multiple objects.
Both the agent itself and the underlying prompt will be further improved in the coming 
months making sure that agents become more robust to a variety of user inputs.

Customizing the whole prompt
To give the user maximum flexibility, the whole prompt template as explained in above
can be overwritten by the user. In this case make sure that your custom prompt includes an introduction section, 
a tool section, an example section, and an unfinished example section. If you want to overwrite the run prompt template, 
you can do as follows:

template = """""" [] """"""
agent = HfAgent(your_endpoint, run_prompt_template=template)

Please make sure to have the <<all_tools>> string and the <<prompt>> defined somewhere in the template so that the agent can be aware 
of the tools, it has available to it as well as correctly insert the user's prompt.

Similarly, one can overwrite the chat prompt template. Note that the chat mode always uses the following format for the exchanges:
```text
Human: <>
Assistant:

Therefore it is important that the examples of the custom chat prompt template also make use of this format.
You can overwrite the chat template at instantiation as follows.

template = """""" [] """"""
agent = HfAgent(url_endpoint=your_endpoint, chat_prompt_template=template)

Please make sure to have the <<all_tools>> string defined somewhere in the template so that the agent can be aware 
of the tools, it has available to it.

In both cases, you can pass a repo ID instead of the prompt template if you would like to use a template hosted by someone in the community. The default prompts live in this repo as an example.
To upload your custom prompt on a repo on the Hub and share it with the community just make sure:
- to use a dataset repository
- to put the prompt template for the run command in a file named run_prompt_template.txt
- to put the prompt template for the chat command in a file named chat_prompt_template.txt
Using custom tools
In this section, we'll be leveraging two existing custom tools that are specific to image generation:

We replace huggingface-tools/image-transformation,
  with diffusers/controlnet-canny-tool 
  to allow for more image modifications.
We add a new tool for image upscaling to the default toolbox: 
  diffusers/latent-upscaler-tool replace the existing image-transformation tool.

We'll start by loading the custom tools with the convenient [load_tool] function:

from transformers import load_tool
controlnet_transformer = load_tool(""diffusers/controlnet-canny-tool"")
upscaler = load_tool(""diffusers/latent-upscaler-tool"")

Upon adding custom tools to an agent, the tools' descriptions and names are automatically
included in the agents' prompts. Thus, it is imperative that custom tools have
a well-written description and name in order for the agent to understand how to use them.
Let's take a look at the description and name of controlnet_transformer:
py
print(f""Description: '{controlnet_transformer.description}'"")
print(f""Name: '{controlnet_transformer.name}'"")
gives 
text
Description: 'This is a tool that transforms an image with ControlNet according to a prompt. 
It takes two inputs: `image`, which should be the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the modified image.'
Name: 'image_transformer'
The name and description are accurate and fit the style of the curated set of tools.
Next, let's instantiate an agent with controlnet_transformer and upscaler:
py
tools = [controlnet_transformer, upscaler]
agent = HfAgent(""https://api-inference.huggingface.co/models/bigcode/starcoder"", additional_tools=tools)
This command should give you the following info:
text
image_transformer has been replaced by <transformers_modules.diffusers.controlnet-canny-tool.bd76182c7777eba9612fc03c0
8718a60c0aa6312.image_transformation.ControlNetTransformationTool object at 0x7f1d3bfa3a00> as provided in `additional_tools`
The set of curated tools already has an image_transformer tool which is hereby replaced with our custom tool.

Overwriting existing tools can be beneficial if we want to use a custom tool exactly for the same task as an existing tool 
because the agent is well-versed in using the specific task. Beware that the custom tool should follow the exact same API 
as the overwritten tool in this case, or you should adapt the prompt template to make sure all examples using that
tool are updated.

The upscaler tool was given the name image_upscaler which is not yet present in the default toolbox and is therefore simply added to the list of tools.
You can always have a look at the toolbox that is currently available to the agent via the agent.toolbox attribute:
py
print(""\n"".join([f""- {a}"" for a in agent.toolbox.keys()]))
text
- document_qa
- image_captioner
- image_qa
- image_segmenter
- transcriber
- summarizer
- text_classifier
- text_qa
- text_reader
- translator
- image_transformer
- text_downloader
- image_generator
- video_generator
- image_upscaler
Note how image_upscaler is now part of the agents' toolbox.
Let's now try out the new tools! We will re-use the image we generated in Transformers Agents Quickstart.

from diffusers.utils import load_image
image = load_image(
    ""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png""
)

 
Let's transform the image into a beautiful winter landscape:
py
image = agent.run(""Transform the image: 'A frozen lake and snowy forest'"", image=image)
``text
==Explanation from the agent==
I will use the following tool:image_transformer` to transform the image.
==Code generated by the agent==
image = image_transformer(image, prompt=""A frozen lake and snowy forest"")

 
The new image processing tool is based on ControlNet which can make very strong modifications to the image.
By default the image processing tool returns an image of size 512x512 pixels. Let's see if we can upscale it.
py
image = agent.run(""Upscale the image"", image)
``text
==Explanation from the agent==
I will use the following tool:image_upscaler` to upscale the image.
==Code generated by the agent==
upscaled_image = image_upscaler(image)

 
The agent automatically mapped our prompt ""Upscale the image"" to the just added upscaler tool purely based on the description and name of the upscaler tool 
and was able to correctly run it.
Next, let's have a look at how you can create a new custom tool.
Adding new tools
In this section, we show how to create a new tool that can be added to the agent.
Creating a new tool
We'll first start by creating a tool. We'll add the not-so-useful yet fun task of fetching the model on the Hugging Face
Hub with the most downloads for a given task.
We can do that with the following code:
thon
from huggingface_hub import list_models
task = ""text-classification""
model = next(iter(list_models(filter=task, sort=""downloads"", direction=-1)))
print(model.id)

For the task text-classification, this returns 'facebook/bart-large-mnli', for translation it returns 't5-base.
How do we convert this to a tool that the agent can leverage? All tools depend on the superclass Tool that holds the
main attributes necessary. We'll create a class that inherits from it:
thon
from transformers import Tool
class HFModelDownloadsTool(Tool):
    pass

This class has a few needs:
- An attribute name, which corresponds to the name of the tool itself. To be in tune with other tools which have a
  performative name, we'll name it model_download_counter.
- An attribute description, which will be used to populate the prompt of the agent.
- inputs and outputs attributes. Defining this will help the python interpreter make educated choices about types,
  and will allow for a gradio-demo to be spawned when we push our tool to the Hub. They're both a list of expected
  values, which can be text, image, or audio.
- A __call__ method which contains the inference code. This is the code we've played with above!
Here's what our class looks like now:
thon
from transformers import Tool
from huggingface_hub import list_models
class HFModelDownloadsTool(Tool):
    name = ""model_download_counter""
    description = (
        ""This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. ""
        ""It takes the name of the category (such as text-classification, depth-estimation, etc), and ""
        ""returns the name of the checkpoint.""
    )
inputs = [""text""]
outputs = [""text""]

def __call__(self, task: str):
    model = next(iter(list_models(filter=task, sort=""downloads"", direction=-1)))
    return model.id

We now have our tool handy. Save it in a file and import it from your main script. Let's name this file
model_downloads.py, so the resulting import code looks like this:
thon
from model_downloads import HFModelDownloadsTool
tool = HFModelDownloadsTool()

In order to let others benefit from it and for simpler initialization, we recommend pushing it to the Hub under your 
namespace. To do so, just call push_to_hub on the tool variable:
python
tool.push_to_hub(""hf-model-downloads"")
You now have your code on the Hub! Let's take a look at the final step, which is to have the agent use it.
Having the agent use the tool
We now have our tool that lives on the Hub which can be instantiated as such (change the user name for your tool):
thon
from transformers import load_tool
tool = load_tool(""lysandre/hf-model-downloads"")

In order to use it in the agent, simply pass it in the additional_tools parameter of the agent initialization method:
thon
from transformers import HfAgent
agent = HfAgent(""https://api-inference.huggingface.co/models/bigcode/starcoder"", additional_tools=[tool])
agent.run(
    ""Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?""
)
which outputs the following:text
==Code generated by the agent==
model = model_download_counter(task=""text-to-video"")
print(f""The model with the most downloads is {model}."")
audio_model = text_reader(model)
==Result==
The model with the most downloads is damo-vilab/text-to-video-ms-1.7b.

and generates the following audio.
| Audio                                                                                                                                            |
|------------------------------------------------------------------------------------------------------------------------------------------------------|
|  |

Depending on the LLM, some are quite brittle and require very exact prompts in order to work well. Having a well-defined
name and description of the tool is paramount to having it be leveraged by the agent.

Replacing existing tools
Replacing existing tools can be done simply by assigning a new item to the agent's toolbox. Here's how one would do so:
thon
from transformers import HfAgent, load_tool
agent = HfAgent(""https://api-inference.huggingface.co/models/bigcode/starcoder"")
agent.toolbox[""image-transformation""] = load_tool(""diffusers/controlnet-canny-tool"")

Beware when replacing tools with others! This will also adjust the agent's prompt. This can be good if you have a better
prompt suited for the task, but it can also result in your tool being selected way more than others or for other
tools to be selected instead of the one you have defined.

Leveraging gradio-tools
gradio-tools is a powerful library that allows using Hugging
Face Spaces as tools. It supports many existing Spaces as well as custom Spaces to be designed with it.
We offer support for gradio_tools by using the Tool.from_gradio method. For example, we want to take
advantage of the StableDiffusionPromptGeneratorTool tool offered in the gradio-tools toolkit so as to
improve our prompts and generate better images.
We first import the tool from gradio_tools and instantiate it:
thon
from gradio_tools import StableDiffusionPromptGeneratorTool
gradio_tool = StableDiffusionPromptGeneratorTool()

We pass that instance to the Tool.from_gradio method:
thon
from transformers import Tool
tool = Tool.from_gradio(gradio_tool)

Now we can manage it exactly as we would a usual custom tool. We leverage it to improve our prompt
a rabbit wearing a space suit:
thon
from transformers import HfAgent
agent = HfAgent(""https://api-inference.huggingface.co/models/bigcode/starcoder"", additional_tools=[tool])
agent.run(""Generate an image of the prompt after improving it."", prompt=""A rabbit wearing a space suit"")

The model adequately leverages the tool:
``text
==Explanation from the agent==
I will use the following  tools:StableDiffusionPromptGeneratorto improve the prompt, thenimage_generator` to generate an image according to the improved prompt.
==Code generated by the agent==
improved_prompt = StableDiffusionPromptGenerator(prompt)
print(f""The improved prompt is {improved_prompt}."")
image = image_generator(improved_prompt)

Before finally generating the image:

gradio-tools requires textual inputs and outputs, even when working with different modalities. This implementation
works with image and audio objects. The two are currently incompatible, but will rapidly become compatible as we
work to improve the support.

Future compatibility with Langchain
We love Langchain and think it has a very compelling suite of tools. In order to handle these tools,
Langchain requires textual inputs and outputs, even when working with different modalities.
This is often the serialized version (i.e., saved to disk) of the objects.
This difference means that multi-modality isn't handled between transformers-agents and langchain.
We aim for this limitation to be resolved in future versions, and welcome any help from avid langchain
users to help us achieve this compatibility.
We would love to have better support. If you would like to help, please 
open an issue and share what you have in mind."
"
Custom hardware for training
The hardware you use to run model training and inference can have a big effect on performance. For a deep dive into GPUs make sure to check out Tim Dettmer's excellent blog post.
Let's have a look at some practical advice for GPU setups.
GPU
When you train bigger models you have essentially three options:
- bigger GPUs
- more GPUs
- more CPU and NVMe (offloaded to by DeepSpeed-Infinity)
Let's start at the case where you have a single GPU.
Power and Cooling
If you bought an expensive high end GPU make sure you give it the correct power and sufficient cooling.
Power:
Some high end consumer GPU cards have 2 and sometimes 3 PCI-E 8-Pin power sockets. Make sure you have as many independent 12V PCI-E 8-Pin cables plugged into the card as there are sockets. Do not use the 2 splits at one end of the same cable (also known as pigtail cable). That is if you have 2 sockets on the GPU, you want 2 PCI-E 8-Pin cables going from your PSU to the card and not one that has 2 PCI-E 8-Pin connectors at the end! You won't get the full performance out of your card otherwise.
Each PCI-E 8-Pin power cable needs to be plugged into a 12V rail on the PSU side and can supply up to 150W of power.
Some other cards may use a PCI-E 12-Pin connectors, and these can deliver up to 500-600W of power.
Low end cards may use 6-Pin connectors, which supply up to 75W of power.
Additionally you want the high-end PSU that has stable voltage. Some lower quality ones may not give the card the stable voltage it needs to function at its peak.
And of course the PSU needs to have enough unused Watts to power the card.
Cooling:
When a GPU gets overheated it will start throttling down and will not deliver full performance and it can even shutdown if it gets too hot.
It's hard to tell the exact best temperature to strive for when a GPU is heavily loaded, but probably anything under +80C is good, but lower is better - perhaps 70-75C is an excellent range to be in. The throttling down is likely to start at around 84-90C. But other than throttling performance a prolonged very high temperature is likely to reduce the lifespan of a GPU.
Next let's have a look at one of the most important aspects when having multiple GPUs: connectivity.
Multi-GPU Connectivity
If you use multiple GPUs the way cards are inter-connected can have a huge impact on the total training time. If the GPUs are on the same physical node, you can run:
nvidia-smi topo -m
and it will tell you how the GPUs are inter-connected. On a machine with dual-GPU and which are connected with NVLink, you will most likely see something like:
GPU0    GPU1    CPU Affinity    NUMA Affinity
GPU0     X      NV2     0-23            N/A
GPU1    NV2      X      0-23            N/A
on a different machine w/o NVLink we may see:
GPU0    GPU1    CPU Affinity    NUMA Affinity
GPU0     X      PHB     0-11            N/A
GPU1    PHB      X      0-11            N/A
The report includes this legend:
X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks
So the first report NV2 tells us the GPUs are interconnected with 2 NVLinks, and the second report PHB we have a typical consumer-level PCIe+Bridge setup.
Check what type of connectivity you have on your setup. Some of these will make the communication between cards faster (e.g. NVLink), others slower (e.g. PHB).
Depending on the type of scalability solution used, the connectivity speed could have a major or a minor impact. If the GPUs need to sync rarely, as in DDP, the impact of a slower connection will be less significant. If the GPUs need to send messages to each other often, as in ZeRO-DP, then faster connectivity becomes super important to achieve faster training.
NVlink
NVLink is a wire-based serial multi-lane near-range communications link developed by Nvidia.
Each new generation provides a faster bandwidth, e.g. here is a quote from Nvidia Ampere GA102 GPU Architecture:

Third-Generation NVLink®
GA102 GPUs utilize NVIDIA’s third-generation NVLink interface, which includes four x4 links,
with each link providing 14.0625 GB/sec bandwidth in each direction between two GPUs. Four
links provide 56.25 GB/sec bandwidth in each direction, and 112.5 GB/sec total bandwidth
between two GPUs. Two RTX 3090 GPUs can be connected together for SLI using NVLink.
(Note that 3-Way and 4-Way SLI configurations are not supported.)

So the higher X you get in the report of NVX in the output of nvidia-smi topo -m the better. The generation will depend on your GPU architecture.
Let's compare the execution of a gpt2 language model training over a small sample of wikitext.
The results are:
| NVlink | Time |
| -----  | ---: |
| Y      | 101s |
| N      | 131s |
You can see that NVLink completes the training ~23% faster. In the second benchmark we use NCCL_P2P_DISABLE=1 to tell the GPUs not to use NVLink.
Here is the full benchmark code and outputs:
```bash
DDP w/ NVLink
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
--dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train \
--output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 101.9003, 'train_samples_per_second': 1.963, 'epoch': 0.69}
DDP w/o NVLink
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 python -m torch.distributed.launch \
--nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
--dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train
--output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 131.4367, 'train_samples_per_second': 1.522, 'epoch': 0.69}

Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m)
Software: pytorch-1.8-to-be + cuda-11.0 / transformers==4.3.0.dev0"
"
Multilingual models for inference
[[open-in-colab]]
There are several multilingual models in 🤗 Transformers, and their inference usage differs from monolingual models. Not all multilingual model usage is different though. Some models, like bert-base-multilingual-uncased, can be used just like a monolingual model. This guide will show you how to use multilingual models whose usage differs for inference.
XLM
XLM has ten different checkpoints, only one of which is monolingual. The nine remaining model checkpoints can be split into two categories: the checkpoints that use language embeddings and those that don't.
XLM with language embeddings
The following XLM models use language embeddings to specify the language used at inference:

xlm-mlm-ende-1024 (Masked language modeling, English-German)
xlm-mlm-enfr-1024 (Masked language modeling, English-French)
xlm-mlm-enro-1024 (Masked language modeling, English-Romanian)
xlm-mlm-xnli15-1024 (Masked language modeling, XNLI languages)
xlm-mlm-tlm-xnli15-1024 (Masked language modeling + translation, XNLI languages)
xlm-clm-enfr-1024 (Causal language modeling, English-French)
xlm-clm-ende-1024 (Causal language modeling, English-German)

Language embeddings are represented as a tensor of the same shape as the input_ids passed to the model. The values in these tensors depend on the language used and are identified by the tokenizer's lang2id and id2lang attributes.
In this example, load the xlm-clm-enfr-1024 checkpoint (Causal language modeling, English-French):

import torch
from transformers import XLMTokenizer, XLMWithLMHeadModel
tokenizer = XLMTokenizer.from_pretrained(""xlm-clm-enfr-1024"")
model = XLMWithLMHeadModel.from_pretrained(""xlm-clm-enfr-1024"")

The lang2id attribute of the tokenizer displays this model's languages and their ids:

print(tokenizer.lang2id)
{'en': 0, 'fr': 1}

Next, create an example input:

input_ids = torch.tensor([tokenizer.encode(""Wikipedia was used to"")])  # batch size of 1

Set the language id as ""en"" and use it to define the language embedding. The language embedding is a tensor filled with 0 since that is the language id for English. This tensor should be the same size as input_ids. 

language_id = tokenizer.lang2id[""en""]  # 0
langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, , 0])
We reshape it to be of size (batch_size, sequence_length)
langs = langs.view(1, -1)  # is now of shape [1, sequence_length] (we have a batch size of 1)

Now you can pass the input_ids and language embedding to the model:

outputs = model(input_ids, langs=langs)

The run_generation.py script can generate text with language embeddings using the xlm-clm checkpoints.
XLM without language embeddings
The following XLM models do not require language embeddings during inference:

xlm-mlm-17-1280 (Masked language modeling, 17 languages)
xlm-mlm-100-1280 (Masked language modeling, 100 languages)

These models are used for generic sentence representations, unlike the previous XLM checkpoints.
BERT
The following BERT models can be used for multilingual tasks:

bert-base-multilingual-uncased (Masked language modeling + Next sentence prediction, 102 languages)
bert-base-multilingual-cased (Masked language modeling + Next sentence prediction, 104 languages)

These models do not require language embeddings during inference. They should identify the language from the
context and infer accordingly.
XLM-RoBERTa
The following XLM-RoBERTa models can be used for multilingual tasks:

xlm-roberta-base (Masked language modeling, 100 languages)
xlm-roberta-large (Masked language modeling, 100 languages)

XLM-RoBERTa was trained on 2.5TB of newly created and cleaned CommonCrawl data in 100 languages. It provides strong gains over previously released multilingual models like mBERT or XLM on downstream tasks like classification, sequence labeling, and question answering.
M2M100
The following M2M100 models can be used for multilingual translation:

facebook/m2m100_418M (Translation)
facebook/m2m100_1.2B (Translation)

In this example, load the facebook/m2m100_418M checkpoint to translate from Chinese to English. You can set the source language in the tokenizer:

from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
en_text = ""Do not meddle in the affairs of wizards, for they are subtle and quick to anger.""
chinese_text = ""不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒.""
tokenizer = M2M100Tokenizer.from_pretrained(""facebook/m2m100_418M"", src_lang=""zh"")
model = M2M100ForConditionalGeneration.from_pretrained(""facebook/m2m100_418M"")

Tokenize the text:

encoded_zh = tokenizer(chinese_text, return_tensors=""pt"")

M2M100 forces the target language id as the first generated token to translate to the target language. Set the forced_bos_token_id to en in the generate method to translate to English:

generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id(""en""))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.'

MBart
The following MBart models can be used for multilingual translation:

facebook/mbart-large-50-one-to-many-mmt (One-to-many multilingual machine translation, 50 languages)
facebook/mbart-large-50-many-to-many-mmt (Many-to-many multilingual machine translation, 50 languages)
facebook/mbart-large-50-many-to-one-mmt (Many-to-one multilingual machine translation, 50 languages)
facebook/mbart-large-50 (Multilingual translation, 50 languages)
facebook/mbart-large-cc25

In this example, load the facebook/mbart-large-50-many-to-many-mmt checkpoint to translate Finnish to English. You can set the source language in the tokenizer:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
en_text = ""Do not meddle in the affairs of wizards, for they are subtle and quick to anger.""
fi_text = ""Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia.""
tokenizer = AutoTokenizer.from_pretrained(""facebook/mbart-large-50-many-to-many-mmt"", src_lang=""fi_FI"")
model = AutoModelForSeq2SeqLM.from_pretrained(""facebook/mbart-large-50-many-to-many-mmt"")

Tokenize the text:

encoded_en = tokenizer(en_text, return_tensors=""pt"")

MBart forces the target language id as the first generated token to translate to the target language. Set the forced_bos_token_id to en in the generate method to translate to English:

generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id(""en_XX""))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
""Don't interfere with the wizard's affairs, because they are subtle, will soon get angry.""

If you are using the facebook/mbart-large-50-many-to-one-mmt checkpoint, you don't need to force the target language id as the first generated token otherwise the usage is the same."
"
What 🤗 Transformers can do
🤗 Transformers is a library of pretrained state-of-the-art models for natural language processing (NLP), computer vision, and audio and speech processing tasks. Not only does the library contain Transformer models, but it also has non-Transformer models like modern convolutional networks for computer vision tasks. If you look at some of the most popular consumer products today, like smartphones, apps, and televisions, odds are that some kind of deep learning technology is behind it. Want to remove a background object from a picture taken by your smartphone? This is an example of a panoptic segmentation task (don't worry if you don't know what this means yet, we'll describe it in the following sections!). 
This page provides an overview of the different speech and audio, computer vision, and NLP tasks that can be solved with the 🤗 Transformers library in just three lines of code!
Audio
Audio and speech processing tasks are a little different from the other modalities mainly because audio as an input is a continuous signal. Unlike text, a raw audio waveform can't be neatly split into discrete chunks the way a sentence can be divided into words. To get around this, the raw audio signal is typically sampled at regular intervals. If you take more samples within an interval, the sampling rate is higher, and the audio more closely resembles the original audio source.
Previous approaches preprocessed the audio to extract useful features from it. It is now more common to start audio and speech processing tasks by directly feeding the raw audio waveform to a feature encoder to extract an audio representation. This simplifies the preprocessing step and allows the model to learn the most essential features.
Audio classification
Audio classification is a task that labels audio data from a predefined set of classes. It is a broad category with many specific applications, some of which include:

acoustic scene classification: label audio with a scene label (""office"", ""beach"", ""stadium"")
acoustic event detection: label audio with a sound event label (""car horn"", ""whale calling"", ""glass breaking"")
tagging: label audio containing multiple sounds (birdsongs, speaker identification in a meeting)
music classification: label music with a genre label (""metal"", ""hip-hop"", ""country"")

from transformers import pipeline
classifier = pipeline(task=""audio-classification"", model=""superb/hubert-base-superb-er"")
preds = classifier(""https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac"")
preds = [{""score"": round(pred[""score""], 4), ""label"": pred[""label""]} for pred in preds]
preds
[{'score': 0.4532, 'label': 'hap'},
 {'score': 0.3622, 'label': 'sad'},
 {'score': 0.0943, 'label': 'neu'},
 {'score': 0.0903, 'label': 'ang'}]

Automatic speech recognition
Automatic speech recognition (ASR) transcribes speech into text. It is one of the most common audio tasks due partly to speech being such a natural form of human communication. Today, ASR systems are embedded in ""smart"" technology products like speakers, phones, and cars. We can ask our virtual assistants to play music, set reminders, and tell us the weather. 
But one of the key challenges Transformer architectures have helped with is in low-resource languages. By pretraining on large amounts of speech data, finetuning the model on only one hour of labeled speech data in a low-resource language can still produce high-quality results compared to previous ASR systems trained on 100x more labeled data.

from transformers import pipeline
transcriber = pipeline(task=""automatic-speech-recognition"", model=""openai/whisper-small"")
transcriber(""https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac"")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}

Computer vision
One of the first and earliest successful computer vision tasks was recognizing images of zip code numbers using a convolutional neural network (CNN). An image is composed of pixels, and each pixel has a numerical value. This makes it easy to represent an image as a matrix of pixel values. Each particular combination of pixel values describes the colors of an image. 
Two general ways computer vision tasks can be solved are:

Use convolutions to learn the hierarchical features of an image from low-level features to high-level abstract things.
Split an image into patches and use a Transformer to gradually learn how each image patch is related to each other to form an image. Unlike the bottom-up approach favored by a CNN, this is kind of like starting out with a blurry image and then gradually bringing it into focus.

Image classification
Image classification labels an entire image from a predefined set of classes. Like most classification tasks, there are many practical use cases for image classification, some of which include:

healthcare: label medical images to detect disease or monitor patient health
environment: label satellite images to monitor deforestation, inform wildland management or detect wildfires
agriculture: label images of crops to monitor plant health or satellite images for land use monitoring 
ecology: label images of animal or plant species to monitor wildlife populations or track endangered species

from transformers import pipeline
classifier = pipeline(task=""image-classification"")
preds = classifier(
     ""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg""
 )
preds = [{""score"": round(pred[""score""], 4), ""label"": pred[""label""]} for pred in preds]
print(*preds, sep=""\n"")
{'score': 0.4335, 'label': 'lynx, catamount'}
{'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}
{'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}
{'score': 0.0239, 'label': 'Egyptian cat'}
{'score': 0.0229, 'label': 'tiger cat'}

Object detection
Unlike image classification, object detection identifies multiple objects within an image and the objects' positions in an image (defined by the bounding box). Some example applications of object detection include:

self-driving vehicles: detect everyday traffic objects such as other vehicles, pedestrians, and traffic lights
remote sensing: disaster monitoring, urban planning, and weather forecasting
defect detection: detect cracks or structural damage in buildings, and manufacturing defects

from transformers import pipeline
detector = pipeline(task=""object-detection"")
preds = detector(
     ""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg""
 )
preds = [{""score"": round(pred[""score""], 4), ""label"": pred[""label""], ""box"": pred[""box""]} for pred in preds]
preds
[{'score': 0.9865,
  'label': 'cat',
  'box': {'xmin': 178, 'ymin': 154, 'xmax': 882, 'ymax': 598}}]

Image segmentation
Image segmentation is a pixel-level task that assigns every pixel in an image to a class. It differs from object detection, which uses bounding boxes to label and predict objects in an image because segmentation is more granular. Segmentation can detect objects at a pixel-level. There are several types of image segmentation:

instance segmentation: in addition to labeling the class of an object, it also labels each distinct instance of an object (""dog-1"", ""dog-2"")
panoptic segmentation: a combination of semantic and instance segmentation; it labels each pixel with a semantic class and each distinct instance of an object

Segmentation tasks are helpful in self-driving vehicles to create a pixel-level map of the world around them so they can navigate safely around pedestrians and other vehicles. It is also useful for medical imaging, where the task's finer granularity can help identify abnormal cells or organ features. Image segmentation can also be used in ecommerce to virtually try on clothes or create augmented reality experiences by overlaying objects in the real world through your camera.

from transformers import pipeline
segmenter = pipeline(task=""image-segmentation"")
preds = segmenter(
     ""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg""
 )
preds = [{""score"": round(pred[""score""], 4), ""label"": pred[""label""]} for pred in preds]
print(*preds, sep=""\n"")
{'score': 0.9879, 'label': 'LABEL_184'}
{'score': 0.9973, 'label': 'snow'}
{'score': 0.9972, 'label': 'cat'}

Depth estimation
Depth estimation predicts the distance of each pixel in an image from the camera. This computer vision task is especially important for scene understanding and reconstruction. For example, in self-driving cars, vehicles need to understand how far objects like pedestrians, traffic signs, and other vehicles are to avoid obstacles and collisions. Depth information is also helpful for constructing 3D representations from 2D images and can be used to create high-quality 3D representations of biological structures or buildings.
There are two approaches to depth estimation:

stereo: depths are estimated by comparing two images of the same image from slightly different angles
monocular: depths are estimated from a single image

from transformers import pipeline
depth_estimator = pipeline(task=""depth-estimation"")
preds = depth_estimator(
     ""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg""
 )

Natural language processing
NLP tasks are among the most common types of tasks because text is such a natural way for us to communicate. To get text into a format recognized by a model, it needs to be tokenized. This means dividing a sequence of text into separate words or subwords (tokens) and then converting these tokens into numbers. As a result, you can represent a sequence of text as a sequence of numbers, and once you have a sequence of numbers, it can be input into a model to solve all sorts of NLP tasks!
Text classification
Like classification tasks in any modality, text classification labels a sequence of text (it can be sentence-level, a paragraph, or a document) from a predefined set of classes. There are many practical applications for text classification, some of which include:

sentiment analysis: label text according to some polarity like positive or negative which can inform and support decision-making in fields like politics, finance, and marketing
content classification: label text according to some topic to help organize and filter information in news and social media feeds (weather, sports, finance, etc.)

from transformers import pipeline
classifier = pipeline(task=""sentiment-analysis"")
preds = classifier(""Hugging Face is the best thing since sliced bread!"")
preds = [{""score"": round(pred[""score""], 4), ""label"": pred[""label""]} for pred in preds]
preds
[{'score': 0.9991, 'label': 'POSITIVE'}]

Token classification
In any NLP task, text is preprocessed by separating the sequence of text into individual words or subwords. These are known as tokens. Token classification assigns each token a label from a predefined set of classes. 
Two common types of token classification are:

named entity recognition (NER): label a token according to an entity category like organization, person, location or date. NER is especially popular in biomedical settings, where it can label genes, proteins, and drug names.
part-of-speech tagging (POS): label a token according to its part-of-speech like noun, verb, or adjective. POS is useful for helping translation systems understand how two identical words are grammatically different (bank as a noun versus bank as a verb).

from transformers import pipeline
classifier = pipeline(task=""ner"")
preds = classifier(""Hugging Face is a French company based in New York City."")
preds = [
     {
         ""entity"": pred[""entity""],
         ""score"": round(pred[""score""], 4),
         ""index"": pred[""index""],
         ""word"": pred[""word""],
         ""start"": pred[""start""],
         ""end"": pred[""end""],
     }
     for pred in preds
 ]
print(*preds, sep=""\n"")
{'entity': 'I-ORG', 'score': 0.9968, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2}
{'entity': 'I-ORG', 'score': 0.9293, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7}
{'entity': 'I-ORG', 'score': 0.9763, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12}
{'entity': 'I-MISC', 'score': 0.9983, 'index': 6, 'word': 'French', 'start': 18, 'end': 24}
{'entity': 'I-LOC', 'score': 0.999, 'index': 10, 'word': 'New', 'start': 42, 'end': 45}
{'entity': 'I-LOC', 'score': 0.9987, 'index': 11, 'word': 'York', 'start': 46, 'end': 50}
{'entity': 'I-LOC', 'score': 0.9992, 'index': 12, 'word': 'City', 'start': 51, 'end': 55}

Question answering
Question answering is another token-level task that returns an answer to a question, sometimes with context (open-domain) and other times without context (closed-domain). This task happens whenever we ask a virtual assistant something like whether a restaurant is open. It can also provide customer or technical support and help search engines retrieve the relevant information you're asking for. 
There are two common types of question answering:

extractive: given a question and some context, the answer is a span of text from the context the model must extract
abstractive: given a question and some context, the answer is generated from the context; this approach is handled by the [Text2TextGenerationPipeline] instead of the [QuestionAnsweringPipeline] shown below

from transformers import pipeline
question_answerer = pipeline(task=""question-answering"")
preds = question_answerer(
     question=""What is the name of the repository?"",
     context=""The name of the repository is huggingface/transformers"",
 )
print(
     f""score: {round(preds['score'], 4)}, start: {preds['start']}, end: {preds['end']}, answer: {preds['answer']}""
 )
score: 0.9327, start: 30, end: 54, answer: huggingface/transformers

Summarization
Summarization creates a shorter version of a text from a longer one while trying to preserve most of the meaning of the original document. Summarization is a sequence-to-sequence task; it outputs a shorter text sequence than the input. There are a lot of long-form documents that can be summarized to help readers quickly understand the main points. Legislative bills, legal and financial documents, patents, and scientific papers are a few examples of documents that could be summarized to save readers time and serve as a reading aid.
Like question answering, there are two types of summarization:

extractive: identify and extract the most important sentences from the original text
abstractive: generate the target summary (which may include new words not in the input document) from the original text; the [SummarizationPipeline] uses the abstractive approach

from transformers import pipeline
summarizer = pipeline(task=""summarization"")
summarizer(
     ""In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.""
 )
[{'summary_text': ' The Transformer is the first sequence transduction model based entirely on attention . It replaces the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention . For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers .'}]

Translation
Translation converts a sequence of text in one language to another. It is important in helping people from different backgrounds communicate with each other, help translate content to reach wider audiences, and even be a learning tool to help people learn a new language. Along with summarization, translation is a sequence-to-sequence task, meaning the model receives an input sequence and returns a target output sequence. 
In the early days, translation models were mostly monolingual, but recently, there has been increasing interest in multilingual models that can translate between many pairs of languages.

from transformers import pipeline
text = ""translate English to French: Hugging Face is a community-based open-source platform for machine learning.""
translator = pipeline(task=""translation"", model=""t5-small"")
translator(text)
[{'translation_text': ""Hugging Face est une tribune communautaire de l'apprentissage des machines.""}]

Language modeling
Language modeling is a task that predicts a word in a sequence of text. It has become a very popular NLP task because a pretrained language model can be finetuned for many other downstream tasks. Lately, there has been a lot of interest in large language models (LLMs) which demonstrate zero- or few-shot learning. This means the model can solve tasks it wasn't explicitly trained to do! Language models can be used to generate fluent and convincing text, though you need to be careful since the text may not always be accurate.
There are two types of language modeling:

causal: the model's objective is to predict the next token in a sequence, and future tokens are masked

from transformers import pipeline
prompt = ""Hugging Face is a community-based open-source platform for machine learning.""
generator = pipeline(task=""text-generation"")
generator(prompt)  # doctest: +SKIP

masked: the model's objective is to predict a masked token in a sequence with full access to the tokens in the sequence

text = ""Hugging Face is a community-based open-source  for machine learning.""
fill_mask = pipeline(task=""fill-mask"")
preds = fill_mask(text, top_k=1)
preds = [
     {
         ""score"": round(pred[""score""], 4),
         ""token"": pred[""token""],
         ""token_str"": pred[""token_str""],
         ""sequence"": pred[""sequence""],
     }
     for pred in preds
 ]
preds
[{'score': 0.2236,
  'token': 1761,
  'token_str': ' platform',
  'sequence': 'Hugging Face is a community-based open-source platform for machine learning.'}]

Multimodal
Multimodal tasks require a model to process multiple data modalities (text, image, audio, video) to solve a particular problem. Image captioning is an example of a multimodal task where the model takes an image as input and outputs a sequence of text describing the image or some properties of the image. 
Although multimodal models work with different data types or modalities, internally, the preprocessing steps help the model convert all the data types into embeddings (vectors or list of numbers that holds meaningful information about the data). For a task like image captioning, the model learns relationships between image embeddings and text embeddings.
Document question answering
Document question answering is a task that answers natural language questions from a document. Unlike a token-level question answering task which takes text as input, document question answering takes an image of a document as input along with a question about the document and returns an answer. Document question answering can be used to parse structured documents and extract key information from it. In the example below, the total amount and change due can be extracted from a receipt.

from transformers import pipeline
from PIL import Image
import requests
url = ""https://datasets-server.huggingface.co/assets/hf-internal-testing/example-documents/--/hf-internal-testing--example-documents/test/2/image/image.jpg""
image = Image.open(requests.get(url, stream=True).raw)
doc_question_answerer = pipeline(""document-question-answering"", model=""magorshunov/layoutlm-invoices"")
preds = doc_question_answerer(
     question=""What is the total amount?"",
     image=image,
 )
preds
[{'score': 0.8531239628791809, 'answer': '17,000', 'start': 4, 'end': 4}]

Hopefully, this page has given you some more background information about all the types of tasks in each modality and the practical importance of each one. In the next section, you'll learn how 🤗 Transformers work to solve these tasks."
"
Model outputs
All models have outputs that are instances of subclasses of [~utils.ModelOutput]. Those are
data structures containing all the information returned by the model, but that can also be used as tuples or
dictionaries.
Let's see how this looks in an example:
thon
from transformers import BertTokenizer, BertForSequenceClassification
import torch
tokenizer = BertTokenizer.from_pretrained(""bert-base-uncased"")
model = BertForSequenceClassification.from_pretrained(""bert-base-uncased"")
inputs = tokenizer(""Hello, my dog is cute"", return_tensors=""pt"")
labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
outputs = model(**inputs, labels=labels)

The outputs object is a [~modeling_outputs.SequenceClassifierOutput], as we can see in the
documentation of that class below, it means it has an optional loss, a logits, an optional hidden_states and
an optional attentions attribute. Here we have the loss since we passed along labels, but we don't have
hidden_states and attentions because we didn't pass output_hidden_states=True or
output_attentions=True.
You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get None. Here for instance outputs.loss is the loss computed by the model, and outputs.attentions is
None.
When considering our outputs object as tuple, it only considers the attributes that don't have None values.
Here for instance, it has two elements, loss then logits, so
python
outputs[:2]
will return the tuple (outputs.loss, outputs.logits) for instance.
When considering our outputs object as dictionary, it only considers the attributes that don't have None
values. Here for instance, it has two keys that are loss and logits.
We document here the generic model outputs that are used by more than one model type. Specific output types are
documented on their corresponding model page.
ModelOutput
[[autodoc]] utils.ModelOutput
    - to_tuple
BaseModelOutput
[[autodoc]] modeling_outputs.BaseModelOutput
BaseModelOutputWithPooling
[[autodoc]] modeling_outputs.BaseModelOutputWithPooling
BaseModelOutputWithCrossAttentions
[[autodoc]] modeling_outputs.BaseModelOutputWithCrossAttentions
BaseModelOutputWithPoolingAndCrossAttentions
[[autodoc]] modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
BaseModelOutputWithPast
[[autodoc]] modeling_outputs.BaseModelOutputWithPast
BaseModelOutputWithPastAndCrossAttentions
[[autodoc]] modeling_outputs.BaseModelOutputWithPastAndCrossAttentions
Seq2SeqModelOutput
[[autodoc]] modeling_outputs.Seq2SeqModelOutput
CausalLMOutput
[[autodoc]] modeling_outputs.CausalLMOutput
CausalLMOutputWithCrossAttentions
[[autodoc]] modeling_outputs.CausalLMOutputWithCrossAttentions
CausalLMOutputWithPast
[[autodoc]] modeling_outputs.CausalLMOutputWithPast
MaskedLMOutput
[[autodoc]] modeling_outputs.MaskedLMOutput
Seq2SeqLMOutput
[[autodoc]] modeling_outputs.Seq2SeqLMOutput
NextSentencePredictorOutput
[[autodoc]] modeling_outputs.NextSentencePredictorOutput
SequenceClassifierOutput
[[autodoc]] modeling_outputs.SequenceClassifierOutput
Seq2SeqSequenceClassifierOutput
[[autodoc]] modeling_outputs.Seq2SeqSequenceClassifierOutput
MultipleChoiceModelOutput
[[autodoc]] modeling_outputs.MultipleChoiceModelOutput
TokenClassifierOutput
[[autodoc]] modeling_outputs.TokenClassifierOutput
QuestionAnsweringModelOutput
[[autodoc]] modeling_outputs.QuestionAnsweringModelOutput
Seq2SeqQuestionAnsweringModelOutput
[[autodoc]] modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
Seq2SeqSpectrogramOutput
[[autodoc]] modeling_outputs.Seq2SeqSpectrogramOutput
SemanticSegmenterOutput
[[autodoc]] modeling_outputs.SemanticSegmenterOutput
ImageClassifierOutput
[[autodoc]] modeling_outputs.ImageClassifierOutput
ImageClassifierOutputWithNoAttention
[[autodoc]] modeling_outputs.ImageClassifierOutputWithNoAttention
DepthEstimatorOutput
[[autodoc]] modeling_outputs.DepthEstimatorOutput
Wav2Vec2BaseModelOutput
[[autodoc]] modeling_outputs.Wav2Vec2BaseModelOutput
XVectorOutput
[[autodoc]] modeling_outputs.XVectorOutput
Seq2SeqTSModelOutput
[[autodoc]] modeling_outputs.Seq2SeqTSModelOutput
Seq2SeqTSPredictionOutput
[[autodoc]] modeling_outputs.Seq2SeqTSPredictionOutput
SampleTSPredictionOutput
[[autodoc]] modeling_outputs.SampleTSPredictionOutput
TFBaseModelOutput
[[autodoc]] modeling_tf_outputs.TFBaseModelOutput
TFBaseModelOutputWithPooling
[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPooling
TFBaseModelOutputWithPoolingAndCrossAttentions
[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
TFBaseModelOutputWithPast
[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPast
TFBaseModelOutputWithPastAndCrossAttentions
[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions
TFSeq2SeqModelOutput
[[autodoc]] modeling_tf_outputs.TFSeq2SeqModelOutput
TFCausalLMOutput
[[autodoc]] modeling_tf_outputs.TFCausalLMOutput
TFCausalLMOutputWithCrossAttentions
[[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
TFCausalLMOutputWithPast
[[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithPast
TFMaskedLMOutput
[[autodoc]] modeling_tf_outputs.TFMaskedLMOutput
TFSeq2SeqLMOutput
[[autodoc]] modeling_tf_outputs.TFSeq2SeqLMOutput
TFNextSentencePredictorOutput
[[autodoc]] modeling_tf_outputs.TFNextSentencePredictorOutput
TFSequenceClassifierOutput
[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutput
TFSeq2SeqSequenceClassifierOutput
[[autodoc]] modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput
TFMultipleChoiceModelOutput
[[autodoc]] modeling_tf_outputs.TFMultipleChoiceModelOutput
TFTokenClassifierOutput
[[autodoc]] modeling_tf_outputs.TFTokenClassifierOutput
TFQuestionAnsweringModelOutput
[[autodoc]] modeling_tf_outputs.TFQuestionAnsweringModelOutput
TFSeq2SeqQuestionAnsweringModelOutput
[[autodoc]] modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
FlaxBaseModelOutput
[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput
FlaxBaseModelOutputWithPast
[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPast
FlaxBaseModelOutputWithPooling
[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPooling
FlaxBaseModelOutputWithPastAndCrossAttentions
[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions
FlaxSeq2SeqModelOutput
[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqModelOutput
FlaxCausalLMOutputWithCrossAttentions
[[autodoc]] modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
FlaxMaskedLMOutput
[[autodoc]] modeling_flax_outputs.FlaxMaskedLMOutput
FlaxSeq2SeqLMOutput
[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput
FlaxNextSentencePredictorOutput
[[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput
FlaxSequenceClassifierOutput
[[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput
FlaxSeq2SeqSequenceClassifierOutput
[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput
FlaxMultipleChoiceModelOutput
[[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput
FlaxTokenClassifierOutput
[[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput
FlaxQuestionAnsweringModelOutput
[[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
FlaxSeq2SeqQuestionAnsweringModelOutput
[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput"
"
General Utilities
This page lists all of Transformers general utility functions that are found in the file utils.py.
Most of those are only useful if you are studying the general code in the library.
Enums and namedtuples
[[autodoc]] utils.ExplicitEnum
[[autodoc]] utils.PaddingStrategy
[[autodoc]] utils.TensorType
Special Decorators
[[autodoc]] utils.add_start_docstrings
[[autodoc]] utils.add_start_docstrings_to_model_forward
[[autodoc]] utils.add_end_docstrings
[[autodoc]] utils.add_code_sample_docstrings
[[autodoc]] utils.replace_return_docstrings
Special Properties
[[autodoc]] utils.cached_property
Other Utilities
[[autodoc]] utils._LazyModule"
"docstyle-ignore
INSTALL_CONTENT = """"""
Transformers installation
! pip install transformers datasets
To install from source instead of the last release, comment the command above and uncomment the following one.
! pip install git+https://github.com/huggingface/transformers.git
""""""
notebook_first_cells = [{""type"": ""code"", ""content"": INSTALL_CONTENT}]
black_avoid_patterns = {
    ""{processor_class}"": ""FakeProcessorClass"",
    ""{model_class}"": ""FakeModelClass"",
    ""{object_class}"": ""FakeObjectClass"",  
}"
"
Share a model
The last two tutorials showed how you can fine-tune a model with PyTorch, Keras, and 🤗 Accelerate for distributed setups. The next step is to share your model with the community! At Hugging Face, we believe in openly sharing knowledge and resources to democratize artificial intelligence for everyone. We encourage you to consider sharing your model with the community to help others save time and resources.
In this tutorial, you will learn two methods for sharing a trained or fine-tuned model on the Model Hub:

Programmatically push your files to the Hub.
Drag-and-drop your files to the Hub with the web interface.

To share a model with the community, you need an account on huggingface.co. You can also join an existing organization or create a new one.

Repository features
Each repository on the Model Hub behaves like a typical GitHub repository. Our repositories offer versioning, commit history, and the ability to visualize differences.
The Model Hub's built-in versioning is based on git and git-lfs. In other words, you can treat one model as one repository, enabling greater access control and scalability. Version control allows revisions, a method for pinning a specific version of a model with a commit hash, tag or branch.
As a result, you can load a specific model version with the revision parameter:

model = AutoModel.from_pretrained(
     ""julien-c/EsperBERTo-small"", revision=""v2.0.1""  # tag name, or branch name, or commit hash
 )

Files are also easily edited in a repository, and you can view the commit history as well as the difference:

Setup
Before sharing a model to the Hub, you will need your Hugging Face credentials. If you have access to a terminal, run the following command in the virtual environment where 🤗 Transformers is installed. This will store your access token in your Hugging Face cache folder (~/.cache/ by default):

huggingface-cli login
If you are using a notebook like Jupyter or Colaboratory, make sure you have the huggingface_hub library installed. This library allows you to programmatically interact with the Hub.

pip install huggingface_hub
Then use notebook_login to sign-in to the Hub, and follow the link here to generate a token to login with:

from huggingface_hub import notebook_login
notebook_login()

Convert a model for all frameworks
To ensure your model can be used by someone working with a different framework, we recommend you convert and upload your model with both PyTorch and TensorFlow checkpoints. While users are still able to load your model from a different framework if you skip this step, it will be slower because 🤗 Transformers will need to convert the checkpoint on-the-fly.
Converting a checkpoint for another framework is easy. Make sure you have PyTorch and TensorFlow installed (see here for installation instructions), and then find the specific model for your task in the other framework. 

Specify from_tf=True to convert a checkpoint from TensorFlow to PyTorch:

pt_model = DistilBertForSequenceClassification.from_pretrained(""path/to/awesome-name-you-picked"", from_tf=True)
pt_model.save_pretrained(""path/to/awesome-name-you-picked"")
``
</pt>
<tf>
Specifyfrom_pt=True` to convert a checkpoint from PyTorch to TensorFlow:

tf_model = TFDistilBertForSequenceClassification.from_pretrained(""path/to/awesome-name-you-picked"", from_pt=True)

Then you can save your new TensorFlow model with it's new checkpoint:

tf_model.save_pretrained(""path/to/awesome-name-you-picked"")

If a model is available in Flax, you can also convert a checkpoint from PyTorch to Flax:

flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
     ""path/to/awesome-name-you-picked"", from_pt=True
 )

Push a model during training

Sharing a model to the Hub is as simple as adding an extra parameter or callback. Remember from the fine-tuning tutorial, the [TrainingArguments] class is where you specify hyperparameters and additional training options. One of these training options includes the ability to push a model directly to the Hub. Set push_to_hub=True in your [TrainingArguments]:

training_args = TrainingArguments(output_dir=""my-awesome-model"", push_to_hub=True)

Pass your training arguments as usual to [Trainer]:

trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=small_train_dataset,
     eval_dataset=small_eval_dataset,
     compute_metrics=compute_metrics,
 )

After you fine-tune your model, call [~transformers.Trainer.push_to_hub] on [Trainer] to push the trained model to the Hub. 🤗 Transformers will even automatically add training hyperparameters, training results and framework versions to your model card!

trainer.push_to_hub()
``
</pt>
<tf>
Share a model to the Hub with [PushToHubCallback]. In the [PushToHubCallback`] function, add:

An output directory for your model.
A tokenizer.
The hub_model_id, which is your Hub username and model name.

from transformers import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
     output_dir=""./your_model_save_path"", tokenizer=tokenizer, hub_model_id=""your-username/my-awesome-model""
 )

Add the callback to fit, and 🤗 Transformers will push the trained model to the Hub:

model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback)

Use the push_to_hub function
You can also call push_to_hub directly on your model to upload it to the Hub.
Specify your model name in push_to_hub:

pt_model.push_to_hub(""my-awesome-model"")

This creates a repository under your username with the model name my-awesome-model. Users can now load your model with the from_pretrained function:

from transformers import AutoModel
model = AutoModel.from_pretrained(""your_username/my-awesome-model"")

If you belong to an organization and want to push your model under the organization name instead, just add it to the repo_id:

pt_model.push_to_hub(""my-awesome-org/my-awesome-model"")

The push_to_hub function can also be used to add other files to a model repository. For example, add a tokenizer to a model repository:

tokenizer.push_to_hub(""my-awesome-model"")

Or perhaps you'd like to add the TensorFlow version of your fine-tuned PyTorch model:

tf_model.push_to_hub(""my-awesome-model"")

Now when you navigate to the your Hugging Face profile, you should see your newly created model repository. Clicking on the Files tab will display all the files you've uploaded to the repository.
For more details on how to create and upload files to a repository, refer to the Hub documentation here.
Upload with the web interface
Users who prefer a no-code approach are able to upload a model through the Hub's web interface. Visit huggingface.co/new to create a new repository:

From here, add some information about your model:

Select the owner of the repository. This can be yourself or any of the organizations you belong to.
Pick a name for your model, which will also be the repository name.
Choose whether your model is public or private.
Specify the license usage for your model.

Now click on the Files tab and click on the Add file button to upload a new file to your repository. Then drag-and-drop a file to upload and add a commit message.

Add a model card
To make sure users understand your model's capabilities, limitations, potential biases and ethical considerations, please add a model card to your repository. The model card is defined in the README.md file. You can add a model card by:

Manually creating and uploading a README.md file.
Clicking on the Edit model card button in your model repository.

Take a look at the DistilBert model card for a good example of the type of information a model card should include. For more details about other options you can control in the README.md file such as a model's carbon footprint or widget examples, refer to the documentation here."
"
Text generation strategies
Text generation is essential to many NLP tasks, such as open-ended text generation, summarization, translation, and
more. It also plays a role in a variety of mixed-modality applications that have text as an output like speech-to-text
and vision-to-text. Some of the models that can generate text include
GPT2, XLNet, OpenAI GPT, CTRL, TransformerXL, XLM, Bart, T5, GIT, Whisper.
Check out a few examples that use [~transformers.generation_utils.GenerationMixin.generate] method to produce
text outputs for different tasks:
* Text summarization
* Image captioning
* Audio transcription
Note that the inputs to the generate method depend on the model's modality. They are returned by the model's preprocessor
class, such as AutoTokenizer or AutoProcessor. If a model's preprocessor creates more than one kind of input, pass all
the inputs to generate(). You can learn more about the individual model's preprocessor in the corresponding model's documentation.
The process of selecting output tokens to generate text is known as decoding, and you can customize the decoding strategy
that the generate() method will use. Modifying a decoding strategy does not change the values of any trainable parameters.
However, it can have a noticeable impact on the quality of the generated output. It can help reduce repetition in the text
and make it more coherent.
This guide describes:
* default generation configuration
* common decoding strategies and their main parameters
* saving and sharing custom generation configurations with your fine-tuned model on 🤗 Hub
Default text generation configuration
A decoding strategy for a model is defined in its generation configuration. When using pre-trained models for inference
within a [pipeline], the models call the PreTrainedModel.generate() method that applies a default generation
configuration under the hood. The default configuration is also used when no custom configuration has been saved with
the model.
When you load a model explicitly, you can inspect the generation configuration that comes with it through
 model.generation_config:
thon

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(""distilgpt2"")
model.generation_config
GenerationConfig {
    ""_from_model_config"": true,
    ""bos_token_id"": 50256,
    ""eos_token_id"": 50256,
    ""transformers_version"": ""4.26.0.dev0""
}

Printing out the model.generation_config reveals only the values that are different from the default generation
configuration, and does not list any of the default values.
The default generation configuration limits the size of the output combined with the input prompt to a maximum of 20
tokens to avoid running into resource limitations. The default decoding strategy is greedy search, which is the simplest decoding strategy that picks a token with the highest probability as the next token. For many tasks
and small output sizes this works well. However, when used to generate longer outputs, greedy search can start
producing highly repetitive results.
Customize text generation
You can override any generation_config by passing the parameters and their values directly to the [generate] method:
thon

my_model.generate(**inputs, num_beams=4, do_sample=True)

Even if the default decoding strategy mostly works for your task, you can still tweak a few things. Some of the
commonly adjusted parameters include:

max_new_tokens: the maximum number of tokens to generate. In other words, the size of the output sequence, not
including the tokens in the prompt.
num_beams: by specifying a number of beams higher than 1, you are effectively switching from greedy search to
beam search. This strategy evaluates several hypotheses at each time step and eventually chooses the hypothesis that
has the overall highest probability for the entire sequence. This has the advantage of identifying high-probability
sequences that start with a lower probability initial tokens and would've been ignored by the greedy search.
do_sample: if set to True, this parameter enables decoding strategies such as multinomial sampling, beam-search
multinomial sampling, Top-K sampling and Top-p sampling. All these strategies select the next token from the probability
distribution over the entire vocabulary with various strategy-specific adjustments.
num_return_sequences: the number of sequence candidates to return for each input. This options is only available for
the decoding strategies that support multiple sequence candidates, e.g. variations of beam search and sampling. Decoding
strategies like greedy search and contrastive search return a single output sequence.

Save a custom decoding strategy with your model
If you would like to share your fine-tuned model with a specific generation configuration, you can:
* Create a [GenerationConfig] class instance
* Specify the decoding strategy parameters
* Save your generation configuration with [GenerationConfig.save_pretrained], making sure to leave its config_file_name argument empty
* Set push_to_hub to True to upload your config to the model's repo
thon

from transformers import AutoModelForCausalLM, GenerationConfig
model = AutoModelForCausalLM.from_pretrained(""my_account/my_model"")
generation_config = GenerationConfig(
     max_new_tokens=50, do_sample=True, top_k=50, eos_token_id=model.config.eos_token_id
 )
generation_config.save_pretrained(""my_account/my_model"", push_to_hub=True)

You can also store several generation configurations in a single directory, making use of the config_file_name
argument in [GenerationConfig.save_pretrained]. You can later instantiate them with [GenerationConfig.from_pretrained]. This is useful if you want to
store several generation configurations for a single model (e.g. one for creative text generation with sampling, and
one for summarization with beam search). You must have the right Hub permissions to add configuration files to a model.
thon

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig
tokenizer = AutoTokenizer.from_pretrained(""t5-small"")
model = AutoModelForSeq2SeqLM.from_pretrained(""t5-small"")
translation_generation_config = GenerationConfig(
     num_beams=4,
     early_stopping=True,
     decoder_start_token_id=0,
     eos_token_id=model.config.eos_token_id,
     pad_token=model.config.pad_token_id,
 )
translation_generation_config.save_pretrained(""t5-small"", ""translation_generation_config.json"", push_to_hub=True)
You could then use the named generation config file to parameterize generation
generation_config = GenerationConfig.from_pretrained(""t5-small"", ""translation_generation_config.json"")
inputs = tokenizer(""translate English to French: Configuration files are easy to use!"", return_tensors=""pt"")
outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['Les fichiers de configuration sont faciles à utiliser !']

Streaming
The generate() supports streaming, through its streamer input. The streamer input is compatible any instance
from a class that has the following methods: put() and end(). Internally, put() is used to push new tokens and
end() is used to flag the end of text generation.

The API for the streamer classes is still under development and may change in the future.

In practice, you can craft your own streaming class for all sorts of purposes! We also have basic streaming classes
ready for you to use. For example, you can use the [TextStreamer] class to stream the output of generate() into
your screen, one word at a time:
thon

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
tok = AutoTokenizer.from_pretrained(""gpt2"")
model = AutoModelForCausalLM.from_pretrained(""gpt2"")
inputs = tok([""An increasing sequence: one,""], return_tensors=""pt"")
streamer = TextStreamer(tok)
Despite returning the usual output, the streamer will also print the generated text to stdout.
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=20)
An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,

Decoding strategies
Certain combinations of the generate() parameters, and ultimately generation_config, can be used to enable specific
decoding strategies. If you are new to this concept, we recommend reading this blog post that illustrates how common decoding strategies work.
Here, we'll show some of the parameters that control the decoding strategies and illustrate how you can use them.
Greedy Search
[generate] uses greedy search decoding by default so you don't have to pass any parameters to enable it. This means the parameters num_beams is set to 1 and do_sample=False.
thon

from transformers import AutoModelForCausalLM, AutoTokenizer
prompt = ""I look forward to""
checkpoint = ""distilgpt2""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors=""pt"")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
outputs = model.generate(**inputs)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
['I look forward to seeing you all again!\n\n\n\n\n\n\n\n\n\n\n']

Contrastive search
The contrastive search decoding strategy was proposed in the 2022 paper A Contrastive Framework for Neural Text Generation.
It demonstrates superior results for generating non-repetitive yet coherent long outputs. To learn how contrastive search
works, check out this blog post.
The two main parameters that enable and control the behavior of contrastive search are penalty_alpha and top_k:
thon

from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = ""gpt2-large""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
prompt = ""Hugging Face Company is""
inputs = tokenizer(prompt, return_tensors=""pt"")
outputs = model.generate(**inputs, penalty_alpha=0.6, top_k=4, max_new_tokens=100)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Hugging Face Company is a family owned and operated business. \
We pride ourselves on being the best in the business and our customer service is second to none.\
\n\nIf you have any questions about our products or services, feel free to contact us at any time.\
 We look forward to hearing from you!']

Multinomial sampling
As opposed to greedy search that always chooses a token with the highest probability as the
next token, multinomial sampling (also called ancestral sampling) randomly selects the next token based on the probability distribution over the entire
vocabulary given by the model. Every token with a non-zero probability has a chance of being selected, thus reducing the
risk of repetition.
To enable multinomial sampling set do_sample=True and num_beams=1.
thon

from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = ""gpt2-large""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
prompt = ""Today was an amazing day because""
inputs = tokenizer(prompt, return_tensors=""pt"")
outputs = model.generate(**inputs, do_sample=True, num_beams=1, max_new_tokens=100)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Today was an amazing day because we are now in the final stages of our trip to New York City which was very tough. \
It is a difficult schedule and a challenging part of the year but still worth it. I have been taking things easier and \
I feel stronger and more motivated to be out there on their tour. Hopefully, that experience is going to help them with \
their upcoming events which are currently scheduled in Australia.\n\nWe love that they are here. They want to make a \
name for themselves and become famous for what they']

Beam-search decoding
Unlike greedy search, beam-search decoding keeps several hypotheses at each time step and eventually chooses
the hypothesis that has the overall highest probability for the entire sequence. This has the advantage of identifying high-probability
sequences that start with lower probability initial tokens and would've been ignored by the greedy search.
To enable this decoding strategy, specify the num_beams (aka number of hypotheses to keep track of) that is greater than 1.
thon

from transformers import AutoModelForCausalLM, AutoTokenizer
prompt = ""It is astonishing how one can""
checkpoint = ""gpt2-medium""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors=""pt"")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
outputs = model.generate(**inputs, num_beams=5, max_new_tokens=50)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
['It is astonishing how one can have such a profound impact on the lives of so many people in such a short period of \
time.""\n\nHe added: ""I am very proud of the work I have been able to do in the last few years.\n\n""I have']

Beam-search multinomial sampling
As the name implies, this decoding strategy combines beam search with multinomial sampling. You need to specify
the num_beams greater than 1, and set do_sample=True to use this decoding strategy.
thon

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
prompt = ""translate English to German: The house is wonderful.""
checkpoint = ""t5-small""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors=""pt"")
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
outputs = model.generate(**inputs, num_beams=5, do_sample=True)
tokenizer.decode(outputs[0], skip_special_tokens=True)
'Das Haus ist wunderbar.'

Diverse beam search decoding
The diverse beam search decoding strategy is an extension of the beam search strategy that allows for generating a more diverse
set of beam sequences to choose from. To learn how it works, refer to Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models.
This approach has three main parameters: num_beams, num_beam_groups, and diversity_penalty.
The diversily penalty ensures the outputs are distinct across groups, and beam search is used within each group.
thon

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
checkpoint = ""google/pegasus-xsum""
prompt = ""The Permaculture Design Principles are a set of universal design principles \
that can be applied to any location, climate and culture, and they allow us to design \
the most efficient and sustainable human habitation and food production systems. \
Permaculture is a design system that encompasses a wide variety of disciplines, such \
as ecology, landscape design, environmental science and energy conservation, and the \
Permaculture design principles are drawn from these various disciplines. Each individual \
design principle itself embodies a complete conceptual framework based on sound \
scientific principles. When we bring all these separate  principles together, we can \
create a design system that both looks at whole systems, the parts that these systems \
consist of, and how those parts interact with each other to create a complex, dynamic, \
living system. Each design principle serves as a tool that allows us to integrate all \
the separate parts of a design, referred to as elements, into a functional, synergistic, \
whole system, where the elements harmoniously interact and work together in the most \
efficient way possible.""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors=""pt"")
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
outputs = model.generate(**inputs, num_beams=5, num_beam_groups=5, max_new_tokens=30, diversity_penalty=1.0)
tokenizer.decode(outputs[0], skip_special_tokens=True)
'The aim of this project is to create a new type of living system, one that is more sustainable and efficient than the current one.'

This guide illustrates the main parameters that enable various decoding strategies. More advanced parameters exist for the
[generate] method, which gives you even further control over the [generate] method's behavior.
For the complete list of the available parameters, refer to the API documentation.
Assisted Decoding
Assisted decoding is a modification of the decoding strategies above that uses an assistant model with the same
tokenizer (ideally a much smaller model) to greedily generate a few candidate tokens. The main model then validates
the candidate tokens in a single forward pass, which speeds up the decoding process. Currently, only greedy search
and sampling are supported with assisted decoding, and doesn't support batched inputs. To learn more about assisted
decoding, check this blog post.
To enable assisted decoding, set the assistant_model argument with a model.
thon

from transformers import AutoModelForCausalLM, AutoTokenizer
prompt = ""Alice and Bob""
checkpoint = ""EleutherAI/pythia-1.4b-deduped""
assistant_checkpoint = ""EleutherAI/pythia-160m-deduped""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors=""pt"")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint)
outputs = model.generate(**inputs, assistant_model=assistant_model)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a']

When using assisted decoding with sampling methods, you can use the temperarure argument to control the randomness
just like in multinomial sampling. However, in assisted decoding, reducing the temperature will help improving latency.
thon

from transformers import AutoModelForCausalLM, AutoTokenizer
prompt = ""Alice and Bob""
checkpoint = ""EleutherAI/pythia-1.4b-deduped""
assistant_checkpoint = ""EleutherAI/pythia-160m-deduped""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors=""pt"")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint)
outputs = model.generate(**inputs, assistant_model=assistant_model, do_sample=True, temperature=0.5)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
[""Alice and Bob are sitting on the sofa. Alice says, 'I'm going to my room""]

"
"
Logging
🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.
Currently the default verbosity of the library is WARNING.
To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity
to the INFO level.
thon
import transformers
transformers.logging.set_verbosity_info()

You can also use the environment variable TRANSFORMERS_VERBOSITY to override the default verbosity. You can set it
to one of the following: debug, info, warning, error, critical. For example:

TRANSFORMERS_VERBOSITY=error ./myprogram.py
Additionally, some warnings can be disabled by setting the environment variable
TRANSFORMERS_NO_ADVISORY_WARNINGS to a true value, like 1. This will disable any warning that is logged using
[logger.warning_advice]. For example:

TRANSFORMERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py
Here is an example of how to use the same logger as the library in your own module or script:
thon
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(""transformers"")
logger.info(""INFO"")
logger.warning(""WARN"")

All the methods of this logging module are documented below, the main ones are
[logging.get_verbosity] to get the current level of verbosity in the logger and
[logging.set_verbosity] to set the verbosity to the level of your choice. In order (from the least
verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:

transformers.logging.CRITICAL or transformers.logging.FATAL (int value, 50): only report the most
  critical errors.
transformers.logging.ERROR (int value, 40): only report errors.
transformers.logging.WARNING or transformers.logging.WARN (int value, 30): only reports error and
  warnings. This the default level used by the library.
transformers.logging.INFO (int value, 20): reports error, warnings and basic information.
transformers.logging.DEBUG (int value, 10): report all information.

By default, tqdm progress bars will be displayed during model download. [logging.disable_progress_bar] and [logging.enable_progress_bar] can be used to suppress or unsuppress this behavior.
Base setters
[[autodoc]] logging.set_verbosity_error
[[autodoc]] logging.set_verbosity_warning
[[autodoc]] logging.set_verbosity_info
[[autodoc]] logging.set_verbosity_debug
Other functions
[[autodoc]] logging.get_verbosity
[[autodoc]] logging.set_verbosity
[[autodoc]] logging.get_logger
[[autodoc]] logging.enable_default_handler
[[autodoc]] logging.disable_default_handler
[[autodoc]] logging.enable_explicit_format
[[autodoc]] logging.reset_format
[[autodoc]] logging.enable_progress_bar
[[autodoc]] logging.disable_progress_bar"
"
Load pretrained instances with an AutoClass
With so many different Transformer architectures, it can be challenging to create one for your checkpoint. As a part of 🤗 Transformers core philosophy to make the library easy, simple and flexible to use, an AutoClass automatically infer and load the correct architecture from a given checkpoint. The from_pretrained() method lets you quickly load a pretrained model for any architecture so you don't have to devote time and resources to train a model from scratch. Producing this type of checkpoint-agnostic code means if your code works for one checkpoint, it will work with another checkpoint - as long as it was trained for a similar task - even if the architecture is different.

Remember, architecture refers to the skeleton of the model and checkpoints are the weights for a given architecture. For example, BERT is an architecture, while bert-base-uncased is a checkpoint. Model is a general term that can mean either architecture or checkpoint.

In this tutorial, learn to:

Load a pretrained tokenizer.
Load a pretrained image processor
Load a pretrained feature extractor.
Load a pretrained processor.
Load a pretrained model.

AutoTokenizer
Nearly every NLP task begins with a tokenizer. A tokenizer converts your input into a format that can be processed by the model.
Load a tokenizer with [AutoTokenizer.from_pretrained]:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""bert-base-uncased"")

Then tokenize your input as shown below:

sequence = ""In a hole in the ground there lived a hobbit.""
print(tokenizer(sequence))
{'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102], 
 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

AutoImageProcessor
For vision tasks, an image processor processes the image into the correct input format.

from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained(""google/vit-base-patch16-224"")

AutoFeatureExtractor
For audio tasks, a feature extractor processes the audio signal the correct input format.
Load a feature extractor with [AutoFeatureExtractor.from_pretrained]:

from transformers import AutoFeatureExtractor
feature_extractor = AutoFeatureExtractor.from_pretrained(
     ""ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition""
 )

AutoProcessor
Multimodal tasks require a processor that combines two types of preprocessing tools. For example, the LayoutLMV2 model requires an image processor to handle images and a tokenizer to handle text; a processor combines both of them.
Load a processor with [AutoProcessor.from_pretrained]:

from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained(""microsoft/layoutlmv2-base-uncased"")

AutoModel

Finally, the AutoModelFor classes let you load a pretrained model for a given task (see here for a complete list of available tasks). For example, load a model for sequence classification with [AutoModelForSequenceClassification.from_pretrained]:

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(""distilbert-base-uncased"")

Easily reuse the same checkpoint to load an architecture for a different task:

from transformers import AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained(""distilbert-base-uncased"")

For PyTorch models, the from_pretrained() method uses torch.load() which internally uses pickle and is known to be insecure. In general, never load a model that could have come from an untrusted source, or that could have been tampered with. This security risk is partially mitigated for public models hosted on the Hugging Face Hub, which are scanned for malware at each commit. See the Hub documentation for best practices like signed commit verification with GPG.
TensorFlow and Flax checkpoints are not affected, and can be loaded within PyTorch architectures using the from_tf and from_flax kwargs for the from_pretrained method to circumvent this issue.

Generally, we recommend using the AutoTokenizer class and the AutoModelFor class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next tutorial, learn how to use your newly loaded tokenizer, image processor, feature extractor and processor to preprocess a dataset for fine-tuning.

Finally, the TFAutoModelFor classes let you load a pretrained model for a given task (see here for a complete list of available tasks). For example, load a model for sequence classification with [TFAutoModelForSequenceClassification.from_pretrained]:

from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained(""distilbert-base-uncased"")

Easily reuse the same checkpoint to load an architecture for a different task:

from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained(""distilbert-base-uncased"")

Generally, we recommend using the AutoTokenizer class and the TFAutoModelFor class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next tutorial, learn how to use your newly loaded tokenizer, image processor, feature extractor and processor to preprocess a dataset for fine-tuning.

"
"
Utilities for Image Processors
This page lists all the utility functions used by the image processors, mainly the functional
transformations used to process the images.
Most of those are only useful if you are studying the code of the image processors in the library.
Image Transformations
[[autodoc]] image_transforms.center_crop
[[autodoc]] image_transforms.center_to_corners_format
[[autodoc]] image_transforms.corners_to_center_format
[[autodoc]] image_transforms.id_to_rgb
[[autodoc]] image_transforms.normalize
[[autodoc]] image_transforms.pad
[[autodoc]] image_transforms.rgb_to_id
[[autodoc]] image_transforms.rescale
[[autodoc]] image_transforms.resize
[[autodoc]] image_transforms.to_pil_image
ImageProcessingMixin
[[autodoc]] image_processing_utils.ImageProcessingMixin"
"
Philosophy
🤗 Transformers is an opinionated library built for:

machine learning researchers and educators seeking to use, study or extend large-scale Transformers models.
hands-on practitioners who want to fine-tune those models or serve them in production, or both.
engineers who just want to download a pretrained model and use it to solve a given machine learning task.

The library was designed with two strong goals in mind:

Be as easy and fast to use as possible:

We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
    just three standard classes required to use each model: configuration,
    models, and a preprocessing class (tokenizer for NLP, image processor for vision, feature extractor for audio, and processor for multimodal inputs).

All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
    from_pretrained() method which downloads (if needed), caches and
    loads the related class instance and associated data (configurations' hyperparameters, tokenizers' vocabulary,
    and models' weights) from a pretrained checkpoint provided on Hugging Face Hub or your own saved checkpoint.
On top of those three base classes, the library provides two APIs: [pipeline] for quickly
    using a model for inference on a given task and [Trainer] to quickly train or fine-tune a PyTorch model (all TensorFlow models are compatible with Keras.fit).

As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
    extend or build upon the library, just use regular Python, PyTorch, TensorFlow, Keras modules and inherit from the base
    classes of the library to reuse functionalities like model loading and saving. If you'd like to learn more about our coding philosophy for models, check out our Repeat Yourself blog post.

Provide state-of-the-art models with performances as close as possible to the original models:

We provide at least one example for each architecture which reproduces a result provided by the official authors
    of said architecture.

The code is usually as close to the original code base as possible which means some PyTorch code may be not as
    pytorchic as it could be as a result of being converted TensorFlow code and vice versa.

A few other goals:

Expose the models' internals as consistently as possible:

We give access, using a single API, to the full hidden-states and attention weights.

The preprocessing classes and base model APIs are standardized to easily switch between models.

Incorporate a subjective selection of promising tools for fine-tuning and investigating these models:

A simple and consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.

Simple ways to mask and prune Transformer heads.

Easily switch between PyTorch, TensorFlow 2.0 and Flax, allowing training with one framework and inference with another.

Main concepts
The library is built around three types of classes for each model:

Model classes can be PyTorch models (torch.nn.Module), Keras models (tf.keras.Model) or JAX/Flax models (flax.linen.Module) that work with the pretrained weights provided in the library.
Configuration classes store the hyperparameters required to build a model (such as the number of layers and hidden size). You don't always need to instantiate these yourself. In particular, if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model).
Preprocessing classes convert the raw data into a format accepted by the model. A tokenizer stores the vocabulary for each model and provide methods for encoding and decoding strings in a list of token embedding indices to be fed to a model. Image processors preprocess vision inputs, feature extractors preprocess audio inputs, and a processor handles multimodal inputs.

All these classes can be instantiated from pretrained instances, saved locally, and shared on the Hub with three methods:

from_pretrained() lets you instantiate a model, configuration, and preprocessing class from a pretrained version either
  provided by the library itself (the supported models can be found on the Model Hub) or
  stored locally (or on a server) by the user.
save_pretrained() lets you save a model, configuration, and preprocessing class locally so that it can be reloaded using
  from_pretrained().
push_to_hub() lets you share a model, configuration, and a preprocessing class to the Hub, so it is easily accessible to everyone.
"
"
Benchmarks

Hugging Face's Benchmarking tools are deprecated and it is advised to use external Benchmarking libraries to measure the speed 
and memory complexity of Transformer models.

[[open-in-colab]]
Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks.
A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found here.
How to benchmark 🤗 Transformers models
The classes [PyTorchBenchmark] and [TensorFlowBenchmark] allow to flexibly benchmark 🤗 Transformers models. The benchmark classes allow us to measure the peak memory usage and required time for both inference and training.

Hereby, inference is defined by a single forward pass, and training is defined by a single forward pass and
backward pass.

The benchmark classes [PyTorchBenchmark] and [TensorFlowBenchmark] expect an object of type [PyTorchBenchmarkArguments] and
[TensorFlowBenchmarkArguments], respectively, for instantiation. [PyTorchBenchmarkArguments] and [TensorFlowBenchmarkArguments] are data classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it is shown how a BERT model of type bert-base-cased can be benchmarked.

from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
args = PyTorchBenchmarkArguments(models=[""bert-base-uncased""], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
benchmark = PyTorchBenchmark(args)
</pt>
<tf>py
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
args = TensorFlowBenchmarkArguments(
     models=[""bert-base-uncased""], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
 )
benchmark = TensorFlowBenchmark(args)

Here, three arguments are given to the benchmark argument data classes, namely models, batch_sizes, and
sequence_lengths. The argument models is required and expects a list of model identifiers from the
model hub The list arguments batch_sizes and sequence_lengths define
the size of the input_ids on which the model is benchmarked. There are many more parameters that can be configured
via the benchmark argument data classes. For more detail on these one can either directly consult the files
src/transformers/benchmark/benchmark_args_utils.py, src/transformers/benchmark/benchmark_args.py (for PyTorch)
and src/transformers/benchmark/benchmark_args_tf.py (for Tensorflow). Alternatively, running the following shell
commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
respectively.

python examples/pytorch/benchmarking/run_benchmark.py --help
An instantiated benchmark object can then simply be run by calling benchmark.run().

results = benchmark.run()
print(results)
====================       INFERENCE - SPEED - RESULT       ====================

Model Name             Batch Size     Seq Length     Time in s
bert-base-uncased          8               8             0.006   
bert-base-uncased          8               32            0.006   
bert-base-uncased          8              128            0.018   
bert-base-uncased          8              512            0.088     

====================      INFERENCE - MEMORY - RESULT       ====================
Model Name             Batch Size     Seq Length    Memory in MB
bert-base-uncased          8               8             1227
bert-base-uncased          8               32            1281
bert-base-uncased          8              128            1307
bert-base-uncased          8              512            1539

====================        ENVIRONMENT INFORMATION         ====================

transformers_version: 2.11.0
framework: PyTorch
use_torchscript: False
framework_version: 1.4.0
python_version: 3.6.10
system: Linux
cpu: x86_64
architecture: 64bit
date: 2020-06-29
time: 08:58:43.371351
fp16: False
use_multiprocessing: True
only_pretrain_model: False
cpu_ram_mb: 32088
use_gpu: True
num_gpus: 1
gpu: TITAN RTX
gpu_ram_mb: 24217
gpu_power_watts: 280.0
gpu_performance_state: 2
use_tpu: False
</pt>
<tf>bash
python examples/tensorflow/benchmarking/run_benchmark_tf.py --help

An instantiated benchmark object can then simply be run by calling benchmark.run().

results = benchmark.run()
print(results)
results = benchmark.run()
print(results)
====================       INFERENCE - SPEED - RESULT       ====================

Model Name             Batch Size     Seq Length     Time in s
bert-base-uncased          8               8             0.005
bert-base-uncased          8               32            0.008
bert-base-uncased          8              128            0.022
bert-base-uncased          8              512            0.105

====================      INFERENCE - MEMORY - RESULT       ====================
Model Name             Batch Size     Seq Length    Memory in MB
bert-base-uncased          8               8             1330
bert-base-uncased          8               32            1330
bert-base-uncased          8              128            1330
bert-base-uncased          8              512            1770

====================        ENVIRONMENT INFORMATION         ====================

transformers_version: 2.11.0
framework: Tensorflow
use_xla: False
framework_version: 2.2.0
python_version: 3.6.10
system: Linux
cpu: x86_64
architecture: 64bit
date: 2020-06-29
time: 09:26:35.617317
fp16: False
use_multiprocessing: True
only_pretrain_model: False
cpu_ram_mb: 32088
use_gpu: True
num_gpus: 1
gpu: TITAN RTX
gpu_ram_mb: 24217
gpu_power_watts: 280.0
gpu_performance_state: 2
use_tpu: False

By default, the time and the required memory for inference are benchmarked. In the example output above the first
two sections show the result corresponding to inference time and inference memory. In addition, all relevant
information about the computing environment, e.g. the GPU type, the system, the library versions, etc are printed
out in the third section under ENVIRONMENT INFORMATION. This information can optionally be saved in a .csv file
when adding the argument save_to_csv=True to [PyTorchBenchmarkArguments] and
[TensorFlowBenchmarkArguments] respectively. In this case, every section is saved in a separate
.csv file. The path to each .csv file can optionally be defined via the argument data classes.
Instead of benchmarking pre-trained models via their model identifier, e.g. bert-base-uncased, the user can
alternatively benchmark an arbitrary configuration of any available model class. In this case, a list of
configurations must be inserted with the benchmark args as follows.

from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
args = PyTorchBenchmarkArguments(
     models=[""bert-base"", ""bert-384-hid"", ""bert-6-lay""], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
 )
config_base = BertConfig()
config_384_hid = BertConfig(hidden_size=384)
config_6_lay = BertConfig(num_hidden_layers=6)
benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
benchmark.run()
====================       INFERENCE - SPEED - RESULT       ====================

Model Name             Batch Size     Seq Length       Time in s
bert-base                  8              128            0.006
bert-base                  8              512            0.006
bert-base                  8              128            0.018   
bert-base                  8              512            0.088   
bert-384-hid              8               8             0.006   
bert-384-hid              8               32            0.006   
bert-384-hid              8              128            0.011   
bert-384-hid              8              512            0.054   
bert-6-lay                 8               8             0.003   
bert-6-lay                 8               32            0.004   
bert-6-lay                 8              128            0.009   
bert-6-lay                 8              512            0.044

====================      INFERENCE - MEMORY - RESULT       ====================
Model Name             Batch Size     Seq Length      Memory in MB
bert-base                  8               8             1277
bert-base                  8               32            1281
bert-base                  8              128            1307   
bert-base                  8              512            1539   
bert-384-hid              8               8             1005   
bert-384-hid              8               32            1027   
bert-384-hid              8              128            1035   
bert-384-hid              8              512            1255   
bert-6-lay                 8               8             1097   
bert-6-lay                 8               32            1101   
bert-6-lay                 8              128            1127   
bert-6-lay                 8              512            1359

====================        ENVIRONMENT INFORMATION         ====================

transformers_version: 2.11.0
framework: PyTorch
use_torchscript: False
framework_version: 1.4.0
python_version: 3.6.10
system: Linux
cpu: x86_64
architecture: 64bit
date: 2020-06-29
time: 09:35:25.143267
fp16: False
use_multiprocessing: True
only_pretrain_model: False
cpu_ram_mb: 32088
use_gpu: True
num_gpus: 1
gpu: TITAN RTX
gpu_ram_mb: 24217
gpu_power_watts: 280.0
gpu_performance_state: 2
use_tpu: False
</pt>
<tf>py

from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig

args = TensorFlowBenchmarkArguments(
     models=[""bert-base"", ""bert-384-hid"", ""bert-6-lay""], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
 )
config_base = BertConfig()
config_384_hid = BertConfig(hidden_size=384)
config_6_lay = BertConfig(num_hidden_layers=6)
benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
benchmark.run()
====================       INFERENCE - SPEED - RESULT       ====================

Model Name             Batch Size     Seq Length       Time in s
bert-base                  8               8             0.005
bert-base                  8               32            0.008
bert-base                  8              128            0.022
bert-base                  8              512            0.106
bert-384-hid              8               8             0.005
bert-384-hid              8               32            0.007
bert-384-hid              8              128            0.018
bert-384-hid              8              512            0.064
bert-6-lay                 8               8             0.002
bert-6-lay                 8               32            0.003
bert-6-lay                 8              128            0.0011
bert-6-lay                 8              512            0.074

====================      INFERENCE - MEMORY - RESULT       ====================
Model Name             Batch Size     Seq Length      Memory in MB
bert-base                  8               8             1330
bert-base                  8               32            1330
bert-base                  8              128            1330
bert-base                  8              512            1770
bert-384-hid              8               8             1330
bert-384-hid              8               32            1330
bert-384-hid              8              128            1330
bert-384-hid              8              512            1540
bert-6-lay                 8               8             1330
bert-6-lay                 8               32            1330
bert-6-lay                 8              128            1330
bert-6-lay                 8              512            1540

====================        ENVIRONMENT INFORMATION         ====================

transformers_version: 2.11.0
framework: Tensorflow
use_xla: False
framework_version: 2.2.0
python_version: 3.6.10
system: Linux
cpu: x86_64
architecture: 64bit
date: 2020-06-29
time: 09:38:15.487125
fp16: False
use_multiprocessing: True
only_pretrain_model: False
cpu_ram_mb: 32088
use_gpu: True
num_gpus: 1
gpu: TITAN RTX
gpu_ram_mb: 24217
gpu_power_watts: 280.0
gpu_performance_state: 2
use_tpu: False

Again, inference time and required memory for inference are measured, but this time for customized configurations
of the BertModel class. This feature can especially be helpful when deciding for which configuration the model
should be trained.
Benchmark best practices
This section lists a couple of best practices one should be aware of when benchmarking a model.

Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
  specifies on which device the code should be run by setting the CUDA_VISIBLE_DEVICES environment variable in the
  shell, e.g. export CUDA_VISIBLE_DEVICES=0 before running the code.
The option no_multi_processing should only be set to True for testing and debugging. To ensure accurate
  memory measurement it is recommended to run each memory benchmark in a separate process by making sure
  no_multi_processing is set to True.
One should always state the environment information when sharing the results of a model benchmark. Results can vary
  heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
  useful for the community.

Sharing your benchmark
Previously all available core models (10 at the time) have been benchmarked for inference time, across many different
settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
done across CPUs (except for TensorFlow XLA) and GPUs.
The approach is detailed in the following blogpost and the results are
available here.
With the new benchmark tools, it is easier than ever to share your benchmark results with the community

PyTorch Benchmarking Results.
TensorFlow Benchmarking Results.
"
"
Distributed training with 🤗 Accelerate
As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude. At Hugging Face, we created the 🤗 Accelerate library to help users easily train a 🤗 Transformers model on any type of distributed setup, whether it is multiple GPU's on one machine or multiple GPU's across several machines. In this tutorial, learn how to customize your native PyTorch training loop to enable training in a distributed environment.
Setup
Get started by installing 🤗 Accelerate:

pip install accelerate
Then import and create an [~accelerate.Accelerator] object. The [~accelerate.Accelerator] will automatically detect your type of distributed setup and initialize all the necessary components for training. You don't need to explicitly place your model on a device.

from accelerate import Accelerator
accelerator = Accelerator()

Prepare to accelerate
The next step is to pass all the relevant training objects to the [~accelerate.Accelerator.prepare] method. This includes your training and evaluation DataLoaders, a model and an optimizer:

train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
     train_dataloader, eval_dataloader, model, optimizer
 )

Backward
The last addition is to replace the typical loss.backward() in your training loop with 🤗 Accelerate's [~accelerate.Accelerator.backward]method:

for epoch in range(num_epochs):
     for batch in train_dataloader:
         outputs = model(**batch)
         loss = outputs.loss
         accelerator.backward(loss)

         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()
         progress_bar.update(1)

As you can see in the following code, you only need to add four additional lines of code to your training loop to enable distributed training!

+ from accelerate import Accelerator
  from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler

accelerator = Accelerator()

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
  optimizer = AdamW(model.parameters(), lr=3e-5)

device = torch.device(""cuda"") if torch.cuda.is_available() else torch.device(""cpu"")

model.to(device)

train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(

train_dataloader, eval_dataloader, model, optimizer
)

num_epochs = 3
  num_training_steps = num_epochs * len(train_dataloader)
  lr_scheduler = get_scheduler(
      ""linear"",
      optimizer=optimizer,
      num_warmup_steps=0,
      num_training_steps=num_training_steps
  )
progress_bar = tqdm(range(num_training_steps))
model.train()
  for epoch in range(num_epochs):
      for batch in train_dataloader:

          outputs = model(**batch)
          loss = outputs.loss

+         accelerator.backward(loss)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      progress_bar.update(1)

Train
Once you've added the relevant lines of code, launch your training in a script or a notebook like Colaboratory.
Train with a script
If you are running your training from a script, run the following command to create and save a configuration file:

accelerate config
Then launch your training with:

accelerate launch train.py
Train with a notebook
🤗 Accelerate can also run in a notebook if you're planning on using Colaboratory's TPUs. Wrap all the code responsible for training in a function, and pass it to [~accelerate.notebook_launcher]:

from accelerate import notebook_launcher
notebook_launcher(training_function)

For more information about 🤗 Accelerate and it's rich features, refer to the documentation."
"
Image captioning
[[open-in-colab]]
Image captioning is the task of predicting a caption for a given image. Common real world applications of it include
aiding visually impaired people that can help them navigate through different situations. Therefore, image captioning
helps to improve content accessibility for people by describing images to them.
This guide will show you how to:

Fine-tune an image captioning model.
Use the fine-tuned model for inference. 

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate -q
pip install jiwer -q
We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:
thon
from huggingface_hub import notebook_login
notebook_login()

Load the Pokémon BLIP captions dataset
Use the 🤗 Dataset library to load a dataset that consists of {image-caption} pairs. To create your own image captioning dataset
in PyTorch, you can follow this notebook. 
thon
from datasets import load_dataset
ds = load_dataset(""lambdalabs/pokemon-blip-captions"")
ds
bash
DatasetDict({
    train: Dataset({
        features: ['image', 'text'],
        num_rows: 833
    })
})

The dataset has two features, image and text.

Many image captioning datasets contain multiple captions per image. In those cases, a common strategy is to randomly sample a caption amongst the available ones during training. 

Split the dataset’s train split into a train and test set with the [~datasets.Dataset.train_test_split] method:
python
ds = ds[""train""].train_test_split(test_size=0.1)
train_ds = ds[""train""]
test_ds = ds[""test""]
Let's visualize a couple of samples from the training set. 
thon
from textwrap import wrap
import matplotlib.pyplot as plt
import numpy as np
def plot_images(images, captions):
    plt.figure(figsize=(20, 20))
    for i in range(len(images)):
        ax = plt.subplot(1, len(images), i + 1)
        caption = captions[i]
        caption = ""\n"".join(wrap(caption, 12))
        plt.title(caption)
        plt.imshow(images[i])
        plt.axis(""off"")
sample_images_to_visualize = [np.array(train_ds[i][""image""]) for i in range(5)]
sample_captions = [train_ds[i][""text""] for i in range(5)]
plot_images(sample_images_to_visualize, sample_captions)

Preprocess the dataset
Since the dataset has two modalities (image and text), the pre-processing pipeline will preprocess images and the captions.
To do so, load the processor class associated with the model you are about to fine-tune. 
thon
from transformers import AutoProcessor
checkpoint = ""microsoft/git-base""
processor = AutoProcessor.from_pretrained(checkpoint)

The processor will internally pre-process the image (which includes resizing, and pixel scaling) and tokenize the caption. 
thon
def transforms(example_batch):
    images = [x for x in example_batch[""image""]]
    captions = [x for x in example_batch[""text""]]
    inputs = processor(images=images, text=captions, padding=""max_length"")
    inputs.update({""labels"": inputs[""input_ids""]})
    return inputs
train_ds.set_transform(transforms)
test_ds.set_transform(transforms)

With the dataset ready, you can now set up the model for fine-tuning. 
Load a base model
Load the ""microsoft/git-base"" into a AutoModelForCausalLM object.
thon
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(checkpoint)

Evaluate
Image captioning models are typically evaluated with the Rouge Score or Word Error Rate. For this guide, you will use the Word Error Rate (WER). 
We use the 🤗 Evaluate library to do so. For potential limitations and other gotchas of the WER, refer to this guide. 
thon
from evaluate import load
import torch
wer = load(""wer"")
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predicted = logits.argmax(-1)
    decoded_labels = processor.batch_decode(labels, skip_special_tokens=True)
    decoded_predictions = processor.batch_decode(predicted, skip_special_tokens=True)
    wer_score = wer.compute(predictions=decoded_predictions, references=decoded_labels)
    return {""wer_score"": wer_score}

Train!
Now, you are ready to start fine-tuning the model. You will use the 🤗 [Trainer] for this. 
First, define the training arguments using [TrainingArguments].
thon
from transformers import TrainingArguments, Trainer
model_name = checkpoint.split(""/"")[1]
training_args = TrainingArguments(
    output_dir=f""{model_name}-pokemon"",
    learning_rate=5e-5,
    num_train_epochs=50,
    fp16=True,
    per_device_train_batch_size=32,
    per_device_eval_batch_size=32,
    gradient_accumulation_steps=2,
    save_total_limit=3,
    evaluation_strategy=""steps"",
    eval_steps=50,
    save_strategy=""steps"",
    save_steps=50,
    logging_steps=50,
    remove_unused_columns=False,
    push_to_hub=True,
    label_names=[""labels""],
    load_best_model_at_end=True,
)

Then pass them along with the datasets and the model to 🤗 Trainer. 
python
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_ds,
    eval_dataset=test_ds,
    compute_metrics=compute_metrics,
)
To start training, simply call [~Trainer.train] on the [Trainer] object.
python 
trainer.train()
You should see the training loss drop smoothly as training progresses.
Once training is completed, share your model to the Hub with the [~Trainer.push_to_hub] method so everyone can use your model:
python
trainer.push_to_hub()
Inference
Take a sample image from test_ds to test the model.
thon
from PIL import Image
import requests
url = ""https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png""
image = Image.open(requests.get(url, stream=True).raw)
image

Prepare image for the model.
thon
device = ""cuda"" if torch.cuda.is_available() else ""cpu""
inputs = processor(images=image, return_tensors=""pt"").to(device)
pixel_values = inputs.pixel_values

Call [generate] and decode the predictions. 
python
generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_caption)

a drawing of a pink and blue pokemon
Looks like the fine-tuned model generated a pretty good caption!"
"
Model training anatomy
To understand performance optimization techniques that one can apply to improve efficiency of model training 
speed and memory utilization, it's helpful to get familiar with how GPU is utilized during training, and how compute 
intensity varies depending on an operation performed.
Let's start by exploring a motivating example of GPU utilization and the training run of a model. For the demonstration, 
we'll need to install a few libraries: 

pip install transformers datasets accelerate nvidia-ml-py3
The nvidia-ml-py3 library allows us to monitor the memory usage of the models from within Python. You might be familiar 
with the nvidia-smi command in the terminal - this library allows to access the same information in Python directly.
Then, we create some dummy data: random token IDs between 100 and 30000 and binary labels for a classifier. 
In total, we get 512 sequences each with length 512 and store them in a [~datasets.Dataset] with PyTorch format.

import numpy as np
from datasets import Dataset
seq_len, dataset_size = 512, 512
dummy_data = {
     ""input_ids"": np.random.randint(100, 30000, (dataset_size, seq_len)),
     ""labels"": np.random.randint(0, 1, (dataset_size)),
 }
ds = Dataset.from_dict(dummy_data)
ds.set_format(""pt"")

To print summary statistics for the GPU utilization and the training run with the [Trainer] we define two helper functions:

from pynvml import *
def print_gpu_utilization():
     nvmlInit()
     handle = nvmlDeviceGetHandleByIndex(0)
     info = nvmlDeviceGetMemoryInfo(handle)
     print(f""GPU memory occupied: {info.used//1024**2} MB."")
def print_summary(result):
     print(f""Time: {result.metrics['train_runtime']:.2f}"")
     print(f""Samples/second: {result.metrics['train_samples_per_second']:.2f}"")
     print_gpu_utilization()

Let's verify that we start with a free GPU memory:

print_gpu_utilization()
GPU memory occupied: 0 MB.

That looks good: the GPU memory is not occupied as we would expect before we load any models. If that's not the case on 
your machine make sure to stop all processes that are using GPU memory. However, not all free GPU memory can be used by 
the user. When a model is loaded to the GPU also the kernels are loaded which can take up 1-2GB of memory. To see how 
much it is we load a tiny tensor into the GPU which triggers the kernels to be loaded as well.

import torch
torch.ones((1, 1)).to(""cuda"")
print_gpu_utilization()
GPU memory occupied: 1343 MB.

We see that the kernels alone take up 1.3GB of GPU memory. Now let's see how much space the model uses.
Load Model
First, we load the bert-large-uncased model. We load the model weights directly to the GPU so that we can check 
how much space just the weights use.

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(""bert-large-uncased"").to(""cuda"")
print_gpu_utilization()
GPU memory occupied: 2631 MB.

We can see that the model weights alone take up 1.3 GB of the GPU memory. The exact number depends on the specific 
GPU you are using. Note that on newer GPUs a model can sometimes take up more space since the weights are loaded in an 
optimized fashion that speeds up the usage of the model. Now we can also quickly check if we get the same result 
as with nvidia-smi CLI:

nvidia-smi
```bash
Tue Jan 11 08:58:05 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.91.03    Driver Version: 460.91.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla V100-SXM2  On   | 00000000:00:04.0 Off |                    0 |
| N/A   37C    P0    39W / 300W |   2631MiB / 16160MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      3721      C   nvs/codeparrot/bin/python     2629MiB |
+-----------------------------------------------------------------------------+

We get the same number as before and you can also see that we are using a V100 GPU with 16GB of memory. So now we can 
start training the model and see how the GPU memory consumption changes. First, we set up a few standard training 
arguments:
py
default_args = {
    ""output_dir"": ""tmp"",
    ""evaluation_strategy"": ""steps"",
    ""num_train_epochs"": 1,
    ""log_level"": ""error"",
    ""report_to"": ""none"",
}

If you plan to run multiple experiments, in order to properly clear the memory between experiments, restart the Python 
 kernel between experiments.

Memory utilization at vanilla training
Let's use the [Trainer] and train the model without using any GPU performance optimization techniques and a batch size of 4:

from transformers import TrainingArguments, Trainer, logging
logging.set_verbosity_error()
training_args = TrainingArguments(per_device_train_batch_size=4, **default_args)
trainer = Trainer(model=model, args=training_args, train_dataset=ds)
result = trainer.train()
print_summary(result)

Time: 57.82
Samples/second: 8.86
GPU memory occupied: 14949 MB.
We see that already a relatively small batch size almost fills up our GPU's entire memory. However, a larger batch size 
can often result in faster model convergence or better end performance. So ideally we want to tune the batch size to our
model's needs and not to the GPU limitations. What's interesting is that we use much more memory than the size of the model. 
To understand a bit better why this is the case let's have look at a model's operations and memory needs.
Anatomy of Model's Operations
Transformers architecture includes 3 main groups of operations grouped below by compute-intensity.

Tensor Contractions
Linear layers and components of Multi-Head Attention all do batched matrix-matrix multiplications. These operations are the most compute-intensive part of training a transformer.

Statistical Normalizations
Softmax and layer normalization are less compute-intensive than tensor contractions, and involve one or more reduction operations, the result of which is then applied via a map.

Element-wise Operators
These are the remaining operators: biases, dropout, activations, and residual connections. These are the least compute-intensive operations.

This knowledge can be helpful to know when analyzing performance bottlenecks.
This summary is derived from Data Movement Is All You Need: A Case Study on Optimizing Transformers 2020
Anatomy of Model's Memory
We've seen that training the model uses much more memory than just putting the model on the GPU. This is because there 
are many components during training that use GPU memory. The components on GPU memory are the following:

model weights
optimizer states
gradients
forward activations saved for gradient computation
temporary buffers
functionality-specific memory

A typical model trained in mixed precision with AdamW requires 18 bytes per model parameter plus activation memory. For 
inference there are no optimizer states and gradients, so we can subtract those. And thus we end up with 6 bytes per 
model parameter for mixed precision inference, plus activation memory.
Let's look at the details.
Model Weights:

4 bytes * number of parameters for fp32 training
6 bytes * number of parameters for mixed precision training (maintains a model in fp32 and one in fp16 in memory)

Optimizer States:

8 bytes * number of parameters for normal AdamW (maintains 2 states)
2 bytes * number of parameters for 8-bit AdamW optimizers like bitsandbytes
4 bytes * number of parameters for optimizers like SGD with momentum (maintains only 1 state)

Gradients

4 bytes * number of parameters for either fp32 or mixed precision training (gradients are always kept in fp32)

Forward Activations

size depends on many factors, the key ones being sequence length, hidden size and batch size.

There are the input and output that are being passed and returned by the forward and the backward functions and the 
forward activations saved for gradient computation.
Temporary Memory
Additionally, there are all kinds of temporary variables which get released once the calculation is done, but in the 
moment these could require additional memory and could push to OOM. Therefore, when coding it's crucial to think 
strategically about such temporary variables and sometimes to explicitly free those as soon as they are no longer needed.
Functionality-specific memory
Then, your software could have special memory needs. For example, when generating text using beam search, the software 
needs to maintain multiple copies of inputs and outputs.
forward vs backward Execution Speed
For convolutions and linear layers there are 2x flops in the backward compared to the forward, which generally translates 
into ~2x slower (sometimes more, because sizes in the backward tend to be more awkward). Activations are usually 
bandwidth-limited, and it’s typical for an activation to have to read more data in the backward than in the forward 
(e.g. activation forward reads once, writes once, activation backward reads twice, gradOutput and output of the forward, 
and writes once, gradInput).
As you can see, there are potentially a few places where we could save GPU memory or speed up operations. 
Now that you understand what affects GPU utilization and computation speed, refer to 
the Methods and tools for efficient training on a single GPU documentation page to learn about 
performance optimization techniques. "
"
Run training on Amazon SageMaker
The documentation has been moved to hf.co/docs/sagemaker. This page will be removed in transformers 5.0. 
Table of Content

Train Hugging Face models on Amazon SageMaker with the SageMaker Python SDK
Deploy Hugging Face models to Amazon SageMaker with the SageMaker Python SDK
Frequently Asked Questions
"
"
Performance and Scalability
Training large transformer models and deploying them to production present various challenges.
During training, the model may require more GPU memory than available or exhibit slow training speed. In the deployment 
phase, the model can struggle to handle the required throughput in a production environment.
This documentation aims to assist you in overcoming these challenges and finding the optimal setting for your use-case. 
The guides are divided into training and inference sections, as each comes with different challenges and solutions. 
Within each section you'll find separate guides for different hardware configurations, such as single GPU vs. multi-GPU 
for training or CPU vs. GPU for inference.
Use this document as your starting point to navigate further to the methods that match your scenario.
Training
Training large transformer models efficiently requires an accelerator such as a GPU or TPU. The most common case is where 
you have a single GPU. The methods that you can apply to improve training efficiency on a single GPU extend to other setups 
such as multiple GPU. However, there are also techniques that are specific to multi-GPU or CPU training. We cover them in 
separate sections.

Methods and tools for efficient training on a single GPU: start here to learn common approaches that can help optimize GPU memory utilization, speed up the training, or both. 
Multi-GPU training section: explore this section to learn about further optimization methods that apply to a multi-GPU settings, such as data, tensor, and pipeline parallelism.
CPU training section: learn about mixed precision training on CPU.
Efficient Training on Multiple CPUs: learn about distributed CPU training.
Training on TPU with TensorFlow: if you are new to TPUs, refer to this section for an opinionated introduction to training on TPUs and using XLA. 
Custom hardware for training: find tips and tricks when building your own deep learning rig.
Hyperparameter Search using Trainer API

Inference
Efficient inference with large models in a production environment can be as challenging as training them. In the following 
sections we go through the steps to run inference on CPU and single/multi-GPU setups.

Inference on a single CPU
Inference on a single GPU
Multi-GPU inference
XLA Integration for TensorFlow Models

Training and inference
Here you'll find techniques, tips and tricks that apply whether you are training a model, or running inference with it.

Instantiating a big model
Troubleshooting performance issues

Contribute
This document is far from being complete and a lot more needs to be added, so if you have additions or corrections to 
make please don't hesitate to open a PR or if you aren't sure start an Issue and we can discuss the details there.
When making contributions that A is better than B, please try to include a reproducible benchmark and/or a link to the 
source of that information (unless it comes directly from you)."
"
Tokenizer
A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
of the tokenizers are available in two flavors: a full python implementation and a ""Fast"" implementation based on the
Rust library 🤗 Tokenizers. The ""Fast"" implementations allows:

a significant speed-up in particular when doing batched tokenization and
additional methods to map between the original string (character and words) and the token space (e.g. getting the
   index of the token comprising a given character or the span of characters corresponding to a given token). 

The base classes [PreTrainedTokenizer] and [PreTrainedTokenizerFast]
implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and
""Fast"" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library
(downloaded from HuggingFace's AWS S3 repository). They both rely on
[~tokenization_utils_base.PreTrainedTokenizerBase] that contains the common methods, and
[~tokenization_utils_base.SpecialTokensMixin].
[PreTrainedTokenizer] and [PreTrainedTokenizerFast] thus implement the main
methods for using all the tokenizers:

Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and
  encoding/decoding (i.e., tokenizing and converting to integers).
Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece).
Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the
  tokenizer for easy access and making sure they are not split during tokenization.

[BatchEncoding] holds the output of the
[~tokenization_utils_base.PreTrainedTokenizerBase]'s encoding methods (__call__,
encode_plus and batch_encode_plus) and is derived from a Python dictionary. When the tokenizer is a pure python
tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
these methods (input_ids, attention_mask). When the tokenizer is a ""Fast"" tokenizer (i.e., backed by
HuggingFace tokenizers library), this class provides in addition
several advanced alignment methods which can be used to map between the original string (character and words) and the
token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding
to a given token).
PreTrainedTokenizer
[[autodoc]] PreTrainedTokenizer
    - call
    - batch_decode
    - decode
    - encode
    - push_to_hub
    - all
PreTrainedTokenizerFast
The [PreTrainedTokenizerFast] depend on the tokenizers library. The tokenizers obtained from the 🤗 tokenizers library can be
loaded very simply into 🤗 transformers. Take a look at the Using tokenizers from 🤗 tokenizers page to understand how this is done.
[[autodoc]] PreTrainedTokenizerFast
    - call
    - batch_decode
    - decode
    - encode
    - push_to_hub
    - all
BatchEncoding
[[autodoc]] BatchEncoding"
"
Text to speech
[[open-in-colab]]
Text-to-speech (TTS) is the task of creating natural-sounding speech from text, where the speech can be generated in multiple 
languages and for multiple speakers. The only text-to-speech model currently available in 🤗 Transformers 
is SpeechT5, though more will be added in the future. SpeechT5 is pre-trained on a combination of 
speech-to-text and text-to-speech data, allowing it to learn a unified space of hidden representations shared by both text 
and speech. This means that the same pre-trained model can be fine-tuned for different tasks. Furthermore, SpeechT5 
supports multiple speakers through x-vector speaker embeddings. 
This guide illustrates how to:

Fine-tune SpeechT5 that was originally trained on English speech on the Dutch (nl) language subset of the VoxPopuli dataset.
Use your fine-tuned model for inference.

Before you begin, make sure you have all the necessary libraries installed:

pip install datasets soundfile speechbrain accelerate
Install 🤗Transformers from source as not all the SpeechT5 features have been merged into an official release yet:

pip install git+https://github.com/huggingface/transformers.git

To follow this guide you will need a GPU. If you're working in a notebook, run the following line to check if a GPU is available: 

!nvidia-smi

We encourage you to log in to your Hugging Face account to upload and share your model with the community. When prompted, enter your token to log in:

from huggingface_hub import notebook_login
notebook_login()

Load the dataset
VoxPopuli is a large-scale multilingual speech corpus consisting of 
data sourced from 2009-2020 European Parliament event recordings. It contains labelled audio-transcription data for 15 
European languages. In this guide, we are using the Dutch language subset, feel free to pick another subset. 
Note that VoxPopuli or any other automated speech recognition (ASR) dataset may not be the most suitable 
option for training TTS models. The features that make it beneficial for ASR, such as excessive background noise, are 
typically undesirable in TTS. However, finding top-quality, multilingual, and multi-speaker TTS datasets can be quite 
challenging.
Let's load the data:

from datasets import load_dataset, Audio
dataset = load_dataset(""facebook/voxpopuli"", ""nl"", split=""train"")
len(dataset)
20968

20968 examples should be sufficient for fine-tuning. SpeechT5 expects audio data to have a sampling rate of 16 kHz, so 
make sure the examples in the dataset meet this requirement:
py
dataset = dataset.cast_column(""audio"", Audio(sampling_rate=16000))
Preprocess the data
Let's begin by defining the model checkpoint to use and loading the appropriate processor: 

from transformers import SpeechT5Processor
checkpoint = ""microsoft/speecht5_tts""
processor = SpeechT5Processor.from_pretrained(checkpoint)

Text cleanup for SpeechT5 tokenization
Start by cleaning up the text data. You'll need the tokenizer part of the processor to process the text:

tokenizer = processor.tokenizer

The dataset examples contain raw_text and normalized_text features. When deciding which feature to use as the text input, 
consider that the SpeechT5 tokenizer doesn't have any tokens for numbers. In normalized_text the numbers are written 
out as text. Thus, it is a better fit, and we recommend using    normalized_text as input text.
Because SpeechT5 was trained on the English language, it may not recognize certain characters in the Dutch dataset. If 
left as is, these characters will be converted to <unk> tokens. However, in Dutch, certain characters like à are 
used to stress syllables. In order to preserve the meaning of the text, we can replace this character with a regular a.
To identify unsupported tokens, extract all unique characters in the dataset using the SpeechT5Tokenizer which 
works with characters as tokens. To do this, write the extract_all_chars mapping function that concatenates 
the transcriptions from all examples into one string and converts it to a set of characters. 
Make sure to set batched=True and batch_size=-1 in dataset.map() so that all transcriptions are available at once for 
the mapping function.

def extract_all_chars(batch):
     all_text = "" "".join(batch[""normalized_text""])
     vocab = list(set(all_text))
     return {""vocab"": [vocab], ""all_text"": [all_text]}
vocabs = dataset.map(
     extract_all_chars,
     batched=True,
     batch_size=-1,
     keep_in_memory=True,
     remove_columns=dataset.column_names,
 )
dataset_vocab = set(vocabs[""vocab""][0])
tokenizer_vocab = {k for k, _ in tokenizer.get_vocab().items()}

Now you have two sets of characters: one with the vocabulary from the dataset and one with the vocabulary from the tokenizer. 
To identify any unsupported characters in the dataset, you can take the difference between these two sets. The resulting 
set will contain the characters that are in the dataset but not in the tokenizer.

dataset_vocab - tokenizer_vocab
{' ', 'à', 'ç', 'è', 'ë', 'í', 'ï', 'ö', 'ü'}

To handle the unsupported characters identified in the previous step, define a function that maps these characters to 
valid tokens. Note that spaces are already replaced by ▁ in the tokenizer and don't need to be handled separately.

replacements = [
     (""à"", ""a""),
     (""ç"", ""c""),
     (""è"", ""e""),
     (""ë"", ""e""),
     (""í"", ""i""),
     (""ï"", ""i""),
     (""ö"", ""o""),
     (""ü"", ""u""),
 ]
def cleanup_text(inputs):
     for src, dst in replacements:
         inputs[""normalized_text""] = inputs[""normalized_text""].replace(src, dst)
     return inputs
dataset = dataset.map(cleanup_text)

Now that you have dealt with special characters in the text, it's time to shift focus to the audio data.
Speakers
The VoxPopuli dataset includes speech from multiple speakers, but how many speakers are represented in the dataset? To 
determine this, we can count the number of unique speakers and the number of examples each speaker contributes to the dataset. 
With a total of 20,968 examples in the dataset, this information will give us a better understanding of the distribution of 
speakers and examples in the data.

from collections import defaultdict
speaker_counts = defaultdict(int)
for speaker_id in dataset[""speaker_id""]:
     speaker_counts[speaker_id] += 1

By plotting a histogram you can get a sense of how much data there is for each speaker.

import matplotlib.pyplot as plt
plt.figure()
plt.hist(speaker_counts.values(), bins=20)
plt.ylabel(""Speakers"")
plt.xlabel(""Examples"")
plt.show()

The histogram reveals that approximately one-third of the speakers in the dataset have fewer than 100 examples, while 
around ten speakers have more than 500 examples. To improve training efficiency and balance the dataset, we can limit 
the data to speakers with between 100 and 400 examples. 

def select_speaker(speaker_id):
     return 100 <= speaker_counts[speaker_id] <= 400
dataset = dataset.filter(select_speaker, input_columns=[""speaker_id""])

Let's check how many speakers remain: 

len(set(dataset[""speaker_id""]))
42

Let's see how many examples are left: 

len(dataset)
9973

You are left with just under 10,000 examples from approximately 40 unique speakers, which should be sufficient.
Note that some speakers with few examples may actually have more audio available if the examples are long. However, 
determining the total amount of audio for each speaker requires scanning through the entire dataset, which is a 
time-consuming process that involves loading and decoding each audio file. As such, we have chosen to skip this step here.
Speaker embeddings
To enable the TTS model to differentiate between multiple speakers, you'll need to create a speaker embedding for each example. 
The speaker embedding is an additional input into the model that captures a particular speaker's voice characteristics.
To generate these speaker embeddings, use the pre-trained spkrec-xvect-voxceleb 
model from SpeechBrain. 
Create a function create_speaker_embedding() that takes an input audio waveform and outputs a 512-element vector 
containing the corresponding speaker embedding.

import os
import torch
from speechbrain.pretrained import EncoderClassifier
spk_model_name = ""speechbrain/spkrec-xvect-voxceleb""
device = ""cuda"" if torch.cuda.is_available() else ""cpu""
speaker_model = EncoderClassifier.from_hparams(
     source=spk_model_name,
     run_opts={""device"": device},
     savedir=os.path.join(""/tmp"", spk_model_name),
 )
def create_speaker_embedding(waveform):
     with torch.no_grad():
         speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
         speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
         speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
     return speaker_embeddings

It's important to note that the speechbrain/spkrec-xvect-voxceleb model was trained on English speech from the VoxCeleb 
dataset, whereas the training examples in this guide are in Dutch. While we believe that this model will still generate 
reasonable speaker embeddings for our Dutch dataset, this assumption may not hold true in all cases.
For optimal results, we recommend training an X-vector model on the target speech first. This will ensure that the model 
is better able to capture the unique voice characteristics present in the Dutch language.
Processing the dataset
Finally, let's process the data into the format the model expects. Create a prepare_dataset function that takes in a 
single example and uses the SpeechT5Processor object to tokenize the input text and load the target audio into a log-mel spectrogram. 
It should also add the speaker embeddings as an additional input.

def prepare_dataset(example):
     audio = example[""audio""]

     example = processor(
         text=example[""normalized_text""],
         audio_target=audio[""array""],
         sampling_rate=audio[""sampling_rate""],
         return_attention_mask=False,
     )
     # strip off the batch dimension
     example[""labels""] = example[""labels""][0]
     # use SpeechBrain to obtain x-vector
     example[""speaker_embeddings""] = create_speaker_embedding(audio[""array""])
     return example

Verify the processing is correct by looking at a single example:

processed_example = prepare_dataset(dataset[0])
list(processed_example.keys())
['input_ids', 'labels', 'stop_labels', 'speaker_embeddings']

Speaker embeddings should be a 512-element vector:

processed_example[""speaker_embeddings""].shape
(512,)

The labels should be a log-mel spectrogram with 80 mel bins.

import matplotlib.pyplot as plt
plt.figure()
plt.imshow(processed_example[""labels""].T)
plt.show()

Side note: If you find this spectrogram confusing, it may be due to your familiarity with the convention of placing low frequencies 
at the bottom and high frequencies at the top of a plot. However, when plotting spectrograms as an image using the matplotlib library, 
the y-axis is flipped and the spectrograms appear upside down.
Now apply the processing function to the entire dataset. This will take between 5 and 10 minutes.

dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names)

You'll see a warning saying that some examples in the dataset are longer than the maximum input length the model can handle (600 tokens). 
Remove those examples from the dataset. Here we go even further and to allow for larger batch sizes we remove anything over 200 tokens.

def is_not_too_long(input_ids):
     input_length = len(input_ids)
     return input_length < 200
dataset = dataset.filter(is_not_too_long, input_columns=[""input_ids""])
len(dataset)
8259

Next, create a basic train/test split: 

dataset = dataset.train_test_split(test_size=0.1)

Data collator
In order to combine multiple examples into a batch, you need to define a custom data collator. This collator will pad shorter sequences with padding 
tokens, ensuring that all examples have the same length. For the spectrogram labels, the padded portions are replaced with the special value -100. This special value 
instructs the model to ignore that part of the spectrogram when calculating the spectrogram loss.

from dataclasses import dataclass
from typing import Any, Dict, List, Union
@dataclass
 class TTSDataCollatorWithPadding:
     processor: Any

     def call(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
         input_ids = [{""input_ids"": feature[""input_ids""]} for feature in features]
         label_features = [{""input_values"": feature[""labels""]} for feature in features]
         speaker_features = [feature[""speaker_embeddings""] for feature in features]
         # collate the inputs and targets into a batch
         batch = processor.pad(input_ids=input_ids, labels=label_features, return_tensors=""pt"")
         # replace padding with -100 to ignore loss correctly
         batch[""labels""] = batch[""labels""].masked_fill(batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100)
         # not used during fine-tuning
         del batch[""decoder_attention_mask""]
         # round down target lengths to multiple of reduction factor
         if model.config.reduction_factor > 1:
             target_lengths = torch.tensor([len(feature[""input_values""]) for feature in label_features])
             target_lengths = target_lengths.new(
                 [length - length % model.config.reduction_factor for length in target_lengths]
             )
             max_length = max(target_lengths)
             batch[""labels""] = batch[""labels""][:, :max_length]
         # also add in the speaker embeddings
         batch[""speaker_embeddings""] = torch.tensor(speaker_features)
         return batch

In SpeechT5, the input to the decoder part of the model is reduced by a factor 2. In other words, it throws away every 
other timestep from the target sequence. The decoder then predicts a sequence that is twice as long. Since the original 
target sequence length may be odd, the data collator makes sure to round the maximum length of the batch down to be a 
multiple of 2.
 

data_collator = TTSDataCollatorWithPadding(processor=processor)

Train the model
Load the pre-trained model from the same checkpoint as you used for loading the processor: 

from transformers import SpeechT5ForTextToSpeech
model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)

The use_cache=True option is incompatible with gradient checkpointing. Disable it for training.
 

model.config.use_cache = False

Define the training arguments. Here we are not computing any evaluation metrics during the training process. Instead, we'll 
only look at the loss:
thon

from transformers import Seq2SeqTrainingArguments
training_args = Seq2SeqTrainingArguments(
     output_dir=""speecht5_finetuned_voxpopuli_nl"",  # change to a repo name of your choice
     per_device_train_batch_size=4,
     gradient_accumulation_steps=8,
     learning_rate=1e-5,
     warmup_steps=500,
     max_steps=4000,
     gradient_checkpointing=True,
     fp16=True,
     evaluation_strategy=""steps"",
     per_device_eval_batch_size=2,
     save_steps=1000,
     eval_steps=1000,
     logging_steps=25,
     report_to=[""tensorboard""],
     load_best_model_at_end=True,
     greater_is_better=False,
     label_names=[""labels""],
     push_to_hub=True,
 )

Instantiate the Trainer object  and pass the model, dataset, and data collator to it.

from transformers import Seq2SeqTrainer
trainer = Seq2SeqTrainer(
     args=training_args,
     model=model,
     train_dataset=dataset[""train""],
     eval_dataset=dataset[""test""],
     data_collator=data_collator,
     tokenizer=processor,
 )

And with that, you're ready to start training! Training will take several hours. Depending on your GPU, 
it is possible that you will encounter a CUDA ""out-of-memory"" error when you start training. In this case, you can reduce 
the per_device_train_batch_size incrementally by factors of 2 and increase gradient_accumulation_steps by 2x to compensate.

trainer.train()

Push the final model to the 🤗 Hub:

trainer.push_to_hub()

Inference
Great, now that you've fine-tuned a model, you can use it for inference!
Load the model from the 🤗 Hub (make sure to use your account name in the following code snippet): 

model = SpeechT5ForTextToSpeech.from_pretrained(""YOUR_ACCOUNT/speecht5_finetuned_voxpopuli_nl"")

Pick an example, here we'll take one from the test dataset. Obtain a speaker embedding. 
 

example = dataset[""test""][304]
speaker_embeddings = torch.tensor(example[""speaker_embeddings""]).unsqueeze(0)

Define some input text and tokenize it.
 

text = ""hallo allemaal, ik praat nederlands. groetjes aan iedereen!""

Preprocess the input text: 

inputs = processor(text=text, return_tensors=""pt"")

Create a spectrogram with your model: 

spectrogram = model.generate_speech(inputs[""input_ids""], speaker_embeddings)

Visualize the spectrogram, if you'd like to: 

plt.figure()
plt.imshow(spectrogram.T)
plt.show()

Finally, use the vocoder to turn the spectrogram into sound.

with torch.no_grad():
     speech = vocoder(spectrogram)
from IPython.display import Audio
Audio(speech.numpy(), rate=16000)

In our experience, obtaining satisfactory results from this model can be challenging. The quality of the speaker 
embeddings appears to be a significant factor. Since SpeechT5 was pre-trained with English x-vectors, it performs best 
when using English speaker embeddings. If the synthesized speech sounds poor, try using a different speaker embedding.
Increasing the training duration is also likely to enhance the quality of the results. Even so, the speech clearly is Dutch instead of English, and it does 
capture the voice characteristics of the speaker (compare to the original audio in the example).
Another thing to experiment with is the model's configuration. For example, try using config.reduction_factor = 1 to 
see if this improves the results.
Finally, it is essential to consider ethical considerations. Although TTS technology has numerous useful applications, it 
may also be used for malicious purposes, such as impersonating someone's voice without their knowledge or consent. Please 
use TTS judiciously and responsibly."
"
Inference on Specialized Hardware
This document will be completed soon with information on how to infer on specialized hardware. In the meantime you can check out the guide for inference on CPUs."
"
Exporting 🤗 Transformers models to ONNX
🤗 Transformers provides a transformers.onnx package that enables you to
convert model checkpoints to an ONNX graph by leveraging configuration objects.
See the guide on exporting 🤗 Transformers models for more
details.
ONNX Configurations
We provide three abstract classes that you should inherit from, depending on the
type of model architecture you wish to export:

Encoder-based models inherit from [~onnx.config.OnnxConfig]
Decoder-based models inherit from [~onnx.config.OnnxConfigWithPast]
Encoder-decoder models inherit from [~onnx.config.OnnxSeq2SeqConfigWithPast]

OnnxConfig
[[autodoc]] onnx.config.OnnxConfig
OnnxConfigWithPast
[[autodoc]] onnx.config.OnnxConfigWithPast
OnnxSeq2SeqConfigWithPast
[[autodoc]] onnx.config.OnnxSeq2SeqConfigWithPast
ONNX Features
Each ONNX configuration is associated with a set of features that enable you
to export models for different types of topologies or tasks.
FeaturesManager
[[autodoc]] onnx.features.FeaturesManager"
"
Instantiating a big model
When you want to use a very big pretrained model, one challenge is to minimize the use of the RAM. The usual workflow
from PyTorch is:

Create your model with random weights.
Load your pretrained weights.
Put those pretrained weights in your random model.

Step 1 and 2 both require a full version of the model in memory, which is not a problem in most cases, but if your model starts weighing several GigaBytes, those two copies can make you got our of RAM. Even worse, if you are using torch.distributed to launch a distributed training, each process will load the pretrained model and store these two copies in RAM.

Note that the randomly created model is initialized with ""empty"" tensors, which take the space in memory without filling it (thus the random values are whatever was in this chunk of memory at a given time). The random initialization following the appropriate distribution for the kind of model/parameters instatiated (like a normal distribution for instance) is only performed after step 3 on the non-initialized weights, to be as fast as possible! 

In this guide, we explore the solutions Transformers offer to deal with this issue. Note that this is an area of active development, so the APIs explained here may change slightly in the future.
Sharded checkpoints
Since version 4.18.0, model checkpoints that end up taking more than 10GB of space are automatically sharded in smaller pieces. In terms of having one single checkpoint when you do model.save_pretrained(save_dir), you will end up with several partial checkpoints (each of which being of size < 10GB) and an index that maps parameter names to the files they are stored in.
You can control the maximum size before sharding with the max_shard_size parameter, so for the sake of an example, we'll use a normal-size models with a small shard size: let's take a traditional BERT model.

from transformers import AutoModel
model = AutoModel.from_pretrained(""bert-base-cased"")

If you save it using [~PreTrainedModel.save_pretrained], you will get a new folder with two files: the config of the model and its weights:

import os
import tempfile
with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(tmp_dir)
     print(sorted(os.listdir(tmp_dir)))
['config.json', 'pytorch_model.bin']

Now let's use a maximum shard size of 200MB:

with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(tmp_dir, max_shard_size=""200MB"")
     print(sorted(os.listdir(tmp_dir)))
['config.json', 'pytorch_model-00001-of-00003.bin', 'pytorch_model-00002-of-00003.bin', 'pytorch_model-00003-of-00003.bin', 'pytorch_model.bin.index.json']

On top of the configuration of the model, we see three different weights files, and an index.json file which is our index. A checkpoint like this can be fully reloaded using the [~PreTrainedModel.from_pretrained] method:

with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(tmp_dir, max_shard_size=""200MB"")
     new_model = AutoModel.from_pretrained(tmp_dir)

The main advantage of doing this for big models is that during step 2 of the workflow shown above, each shard of the checkpoint is loaded after the previous one, capping the memory usage in RAM to the model size plus the size of the biggest shard.
Behind the scenes, the index file is used to determine which keys are in the checkpoint, and where the corresponding weights are stored. We can load that index like any json and get a dictionary:

import json
with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(tmp_dir, max_shard_size=""200MB"")
     with open(os.path.join(tmp_dir, ""pytorch_model.bin.index.json""), ""r"") as f:
         index = json.load(f)
print(index.keys())
dict_keys(['metadata', 'weight_map'])

The metadata just consists of the total size of the model for now. We plan to add other information in the future:

index[""metadata""]
{'total_size': 433245184}

The weights map is the main part of this index, which maps each parameter name (as usually found in a PyTorch model state_dict) to the file it's stored in:

index[""weight_map""]
{'embeddings.LayerNorm.bias': 'pytorch_model-00001-of-00003.bin',
 'embeddings.LayerNorm.weight': 'pytorch_model-00001-of-00003.bin',
 

If you want to directly load such a sharded checkpoint inside a model without using [~PreTrainedModel.from_pretrained] (like you would do model.load_state_dict() for a full checkpoint) you should use [~modeling_utils.load_sharded_checkpoint]:

from transformers.modeling_utils import load_sharded_checkpoint
with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(tmp_dir, max_shard_size=""200MB"")
     load_sharded_checkpoint(model, tmp_dir)

Low memory loading
Sharded checkpoints reduce the memory usage during step 2 of the workflow mentioned above, but in order to use that model in a low memory setting, we recommend leveraging our tools based on the Accelerate library.
Please read the following guide for more information: Large model loading using Accelerate"
"
Time Series Utilities
This page lists all the utility functions and classes that can be used for Time Series based models.
Most of those are only useful if you are studying the code of the time series models or you wish to add to the collection of distributional output classes.
Distributional Output
[[autodoc]] time_series_utils.NormalOutput
[[autodoc]] time_series_utils.StudentTOutput
[[autodoc]] time_series_utils.NegativeBinomialOutput"
"
Trainer
The [Trainer] class provides an API for feature-complete training in PyTorch for most standard use cases. It's used in most of the example scripts.
Before instantiating your [Trainer], create a [TrainingArguments] to access all the points of customization during training.
The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch.
The [Trainer] contains the basic training loop which supports the above features. To inject custom behavior you can subclass them and override the following methods:

get_train_dataloader -- Creates the training DataLoader.
get_eval_dataloader -- Creates the evaluation DataLoader.
get_test_dataloader -- Creates the test DataLoader.
log -- Logs information on the various objects watching training.
create_optimizer_and_scheduler -- Sets up the optimizer and learning rate scheduler if they were not passed at
  init. Note, that you can also subclass or override the create_optimizer and create_scheduler methods
  separately.
create_optimizer -- Sets up the optimizer if it wasn't passed at init.
create_scheduler -- Sets up the learning rate scheduler if it wasn't passed at init.
compute_loss - Computes the loss on a batch of training inputs.
training_step -- Performs a training step.
prediction_step -- Performs an evaluation/test step.
evaluate -- Runs an evaluation loop and returns metrics.
predict -- Returns predictions (with metrics if labels are available) on a test set.

The [Trainer] class is optimized for 🤗 Transformers models and can have surprising behaviors
when you use it on other models. When using it on your own model, make sure:

your model always return tuples or subclasses of [~utils.ModelOutput].
your model can compute the loss if a labels argument is provided and that loss is returned as the first
  element of the tuple (if your model returns tuples)
your model can accept multiple label arguments (use the label_names in your [TrainingArguments] to indicate their name to the [Trainer]) but none of them should be named ""label"".

Here is an example of how to customize [Trainer] to use a weighted loss (useful when you have an unbalanced training set):
thon
from torch import nn
from transformers import Trainer
class CustomTrainer(Trainer):
    def compute_loss(self, model, inputs, return_outputs=False):
        labels = inputs.get(""labels"")
        # forward pass
        outputs = model(**inputs)
        logits = outputs.get(""logits"")
        # compute custom loss (suppose one has 3 labels with different weights)
        loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device))
        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
        return (loss, outputs) if return_outputs else loss

Another way to customize the training loop behavior for the PyTorch [Trainer] is to use callbacks that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms) and take decisions (like early stopping).
Trainer
[[autodoc]] Trainer
    - all
Seq2SeqTrainer
[[autodoc]] Seq2SeqTrainer
    - evaluate
    - predict
TrainingArguments
[[autodoc]] TrainingArguments
    - all
Seq2SeqTrainingArguments
[[autodoc]] Seq2SeqTrainingArguments
    - all
Checkpoints
By default, [Trainer] will save all checkpoints in the output_dir you set in the
[TrainingArguments] you are using. Those will go in subfolder named checkpoint-xxx with xxx
being the step at which the training was at.
Resuming training from a checkpoint can be done when calling [Trainer.train] with either:

resume_from_checkpoint=True which will resume training from the latest checkpoint
resume_from_checkpoint=checkpoint_dir which will resume training from the specific checkpoint in the directory
  passed.

In addition, you can easily save your checkpoints on the Model Hub when using push_to_hub=True. By default, all
the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
the hub-strategy value of your [TrainingArguments] to either:

""checkpoint"": the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
  resume training easily with trainer.train(resume_from_checkpoint=""output_dir/last-checkpoint"").
""all_checkpoints"": all checkpoints are pushed like they appear in the output folder (so you will get one
  checkpoint folder per folder in your final repository)

Logging
By default [Trainer] will use logging.INFO for the main process and logging.WARNING for the replicas if any.
These defaults can be overridden to use any of the 5 logging levels with [TrainingArguments]'s
arguments:

log_level - for the main process
log_level_replica - for the replicas

Further, if [TrainingArguments]'s log_on_each_node is set to False only the main node will
use the log level settings for its main process, all other nodes will use the log level settings for replicas.
Note that [Trainer] is going to set transformers's log level separately for each node in its
[Trainer.__init__]. So you may want to set this sooner (see the next example) if you tap into other
transformers functionality before creating the [Trainer] object.
Here is an example of how this can be used in an application:
thon
[]
logger = logging.getLogger(name)
Setup logging
logging.basicConfig(
    format=""%(asctime)s - %(levelname)s - %(name)s - %(message)s"",
    datefmt=""%m/%d/%Y %H:%M:%S"",
    handlers=[logging.StreamHandler(sys.stdout)],
)
set the main code and the modules it uses to the same log-level according to the node
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
trainer = Trainer()

And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated
warnings you could run it as:

my_app.py  --log_level warning --log_level_replica error
In the multi-node environment if you also don't want the logs to repeat for each node's main process, you will want to
change the above to:

my_app.py  --log_level warning --log_level_replica error --log_on_each_node 0
and then only the main process of the first node will log at the ""warning"" level, and all other processes on the main
node and all processes on other nodes will log at the ""error"" level.
If you need your application to be as quiet as possible you could do:

my_app.py  --log_level error --log_level_replica error --log_on_each_node 0
(add --log_on_each_node 0 if on multi-node environment)
Randomness
When resuming from a checkpoint generated by [Trainer] all efforts are made to restore the
python, numpy and pytorch RNG states to the same states as they were at the moment of saving that checkpoint,
which should make the ""stop and resume"" style of training as close as possible to non-stop training.
However, due to various default non-deterministic pytorch settings this might not fully work. If you want full
determinism please refer to Controlling sources of randomness. As explained in the document, that some of those settings
that make things deterministic (.e.g., torch.backends.cudnn.deterministic) may slow things down, therefore this
can't be done by default, but you can enable those yourself if needed.
Specific GPUs Selection
Let's discuss how you can tell your program which GPUs are to be used and in what order.
When using DistributedDataParallel to use only a subset of your GPUs, you simply specify the number of GPUs to use. For example, if you have 4 GPUs, but you wish to use the first 2 you can do:

python -m torch.distributed.launch --nproc_per_node=2  trainer-program.py 
if you have either accelerate or deepspeed installed you can also accomplish the same by using one of:

accelerate launch --num_processes 2 trainer-program.py 

deepspeed --num_gpus 2 trainer-program.py 
You don't need to use the Accelerate or the Deepspeed integration features to use these launchers.
Until now you were able to tell the program how many GPUs to use. Now let's discuss how to select specific GPUs and control their order.
The following environment variables help you control which GPUs to use and their order.
CUDA_VISIBLE_DEVICES
If you have multiple GPUs and you'd like to use only 1 or a few of those GPUs, set the environment variable CUDA_VISIBLE_DEVICES to a list of the GPUs to be used.
For example, let's say you have 4 GPUs: 0, 1, 2 and 3. To run only on the physical GPUs 0 and 2, you can do:

CUDA_VISIBLE_DEVICES=0,2 python -m torch.distributed.launch trainer-program.py 
So now pytorch will see only 2 GPUs, where your physical GPUs 0 and 2 are mapped to cuda:0 and cuda:1 correspondingly.
You can even change their order:

CUDA_VISIBLE_DEVICES=2,0 python -m torch.distributed.launch trainer-program.py 
Here your physical GPUs 0 and 2 are mapped to cuda:1 and cuda:0 correspondingly.
The above examples were all for DistributedDataParallel use pattern, but the same method works for DataParallel as well:

CUDA_VISIBLE_DEVICES=2,0 python trainer-program.py 
To emulate an environment without GPUs simply set this environment variable to an empty value like so:

CUDA_VISIBLE_DEVICES= python trainer-program.py 
As with any environment variable you can, of course, export those instead of adding these to the command line, as in:

export CUDA_VISIBLE_DEVICES=0,2
python -m torch.distributed.launch trainer-program.py 
but this approach can be confusing since you may forget you set up the environment variable earlier and not understand why the wrong GPUs are used. Therefore, it's a common practice to set the environment variable just for a specific run on the same command line as it's shown in most examples of this section.
CUDA_DEVICE_ORDER
There is an additional environment variable CUDA_DEVICE_ORDER that controls how the physical devices are ordered. The two choices are:

ordered by PCIe bus IDs (matches nvidia-smi's order) - this is the default.

export CUDA_DEVICE_ORDER=PCI_BUS_ID

ordered by GPU compute capabilities

export CUDA_DEVICE_ORDER=FASTEST_FIRST
Most of the time you don't need to care about this environment variable, but it's very helpful if you have a lopsided setup where you have an old and a new GPUs physically inserted in such a way so that the slow older card appears to be first. One way to fix that is to swap the cards. But if you can't swap the cards (e.g., if the cooling of the devices gets impacted) then setting CUDA_DEVICE_ORDER=FASTEST_FIRST will always put the newer faster card first. It'll be somewhat confusing though since nvidia-smi will still report them in the PCIe order.
The other solution to swapping the order is to use:

export CUDA_VISIBLE_DEVICES=1,0
In this example we are working with just 2 GPUs, but of course the same would apply to as many GPUs as your computer has.
Also if you do set this environment variable it's the best to set it in your ~/.bashrc file or some other startup config file and forget about it.
Trainer Integrations
The [Trainer] has been extended to support libraries that may dramatically improve your training
time and fit much bigger models.
Currently it supports third party solutions, DeepSpeed and PyTorch FSDP, which implement parts of the paper ZeRO: Memory Optimizations
Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He.
This provided support is new and experimental as of this writing. While the support for DeepSpeed and PyTorch FSDP is active and we welcome issues around it, we don't support the FairScale integration anymore since it has been integrated in PyTorch main (see the PyTorch FSDP integration)

CUDA Extension Installation Notes
As of this writing, Deepspeed require compilation of CUDA C++ code, before it can be used.
While all installation issues should be dealt with through the corresponding GitHub Issues of Deepspeed, there are a few common issues that one may encounter while building
any PyTorch extension that needs to build CUDA extensions.
Therefore, if you encounter a CUDA-related build issue while doing the following:

pip install deepspeed
please, read the following notes first.
In these notes we give examples for what to do when pytorch has been built with CUDA 10.2. If your situation is
different remember to adjust the version number to the one you are after.
Possible problem #1
While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA
installed system-wide.
For example, if you installed pytorch with cudatoolkit==10.2 in the Python environment, you also need to have
CUDA 10.2 installed system-wide.
The exact location may vary from system to system, but /usr/local/cuda-10.2 is the most common location on many
Unix systems. When CUDA is correctly set up and added to the PATH environment variable, one can find the
installation location by doing:

which nvcc
If you don't have CUDA installed system-wide, install it first. You will find the instructions by using your favorite
search engine. For example, if you're on Ubuntu you may want to search for: ubuntu cuda 10.2 install.
Possible problem #2
Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you
may have:

/usr/local/cuda-10.2
/usr/local/cuda-11.0
Now, in this situation you need to make sure that your PATH and LD_LIBRARY_PATH environment variables contain
the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the
last version was installed. If you encounter the problem, where the package build fails because it can't find the right
CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned
environment variables.
First, you may look at their contents:

echo $PATH
echo $LD_LIBRARY_PATH
so you get an idea of what is inside.
It's possible that LD_LIBRARY_PATH is empty.
PATH lists the locations of where executables can be found and LD_LIBRARY_PATH is for where shared libraries
are to looked for. In both cases, earlier entries have priority over the later ones. : is used to separate multiple
entries.
Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by
doing:

export PATH=/usr/local/cuda-10.2/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
Note that we aren't overwriting the existing values, but prepending instead.
Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do
exist. lib64 sub-directory is where the various CUDA .so objects, like libcudart.so reside, it's unlikely
that your system will have it named differently, but if it is adjust it to reflect your reality.
Possible problem #3
Some older CUDA versions may refuse to build with newer compilers. For example, you my have gcc-9 but it wants
gcc-7.
There are various ways to go about it.
If you can install the latest CUDA toolkit it typically should support the newer compiler.
Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may
already have it but it's not the default one, so the build system can't see it. If you have gcc-7 installed but the
build system complains it can't find it, the following might do the trick:

sudo ln -s /usr/bin/gcc-7  /usr/local/cuda-10.2/bin/gcc
sudo ln -s /usr/bin/g++-7  /usr/local/cuda-10.2/bin/g++
Here, we are making a symlink to gcc-7 from /usr/local/cuda-10.2/bin/gcc and since
/usr/local/cuda-10.2/bin/ should be in the PATH environment variable (see the previous problem's solution), it
should find gcc-7 (and g++7) and then the build will succeed.
As always make sure to edit the paths in the example to match your situation.
PyTorch Fully Sharded Data parallel
To accelerate training huge models on larger batch sizes, we can use a fully sharded data parallel model.
This type of data parallel paradigm enables fitting more data and larger models by sharding the optimizer states, gradients and parameters.
To read more about it and the benefits, check out the Fully Sharded Data Parallel blog.
We have integrated the latest PyTorch's Fully Sharded Data Parallel (FSDP) training feature.
All you need to do is enable it through the config.
Required PyTorch version for FSDP support: PyTorch Nightly (or 1.12.0 if you read this after it has been released)
as the model saving with FSDP activated is only available with recent fixes.
Usage:

Make sure you have added the distributed launcher
-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE if you haven't been using it already.

Sharding Strategy: 

FULL_SHARD : Shards optimizer states + gradients + model parameters across data parallel workers/GPUs.
    For this, add --fsdp full_shard to the command line arguments. 
SHARD_GRAD_OP : Shards optimizer states + gradients across data parallel workers/GPUs.
    For this, add --fsdp shard_grad_op to the command line arguments.
NO_SHARD : No sharding. For this, add --fsdp no_shard to the command line arguments.
To offload the parameters and gradients to the CPU, 
  add --fsdp ""full_shard offload"" or --fsdp ""shard_grad_op offload"" to the command line arguments.
To automatically recursively wrap layers with FSDP using default_auto_wrap_policy, 
  add --fsdp ""full_shard auto_wrap"" or --fsdp ""shard_grad_op auto_wrap"" to the command line arguments.
To enable both CPU offloading and auto wrapping, 
  add --fsdp ""full_shard offload auto_wrap"" or --fsdp ""shard_grad_op offload auto_wrap"" to the command line arguments.
Remaining FSDP config is passed via --fsdp_config <path_to_fsdp_config.json>. It is either a location of
  FSDP json config file (e.g., fsdp_config.json) or an already loaded json file as dict. 
If auto wrapping is enabled, you can either use transformer based auto wrap policy or size based auto wrap policy.
For transformer based auto wrap policy, it is recommended to specify fsdp_transformer_layer_cls_to_wrap in the config file. If not specified, the default value is model._no_split_modules when available.
  This specifies the list of transformer layer class name (case-sensitive) to wrap ,e.g, [BertLayer], [GPTJBlock], [T5Block] .
  This is important because submodules that share weights (e.g., embedding layer) should not end up in different FSDP wrapped units.
  Using this policy, wrapping happens for each block containing Multi-Head Attention followed by couple of MLP layers. 
  Remaining layers including the shared embeddings are conveniently wrapped in same outermost FSDP unit.
  Therefore, use this for transformer based models.
For size based auto wrap policy, please add fsdp_min_num_params in the config file. 
  It specifies FSDP's minimum number of parameters for auto wrapping.

fsdp_backward_prefetch can be specified in the config file. It controls when to prefetch next set of parameters. 
    backward_pre and backward_pos are available options. 
    For more information refer torch.distributed.fsdp.fully_sharded_data_parallel.BackwardPrefetch
fsdp_forward_prefetch can be specified in the config file. It controls when to prefetch next set of parameters. 
    If ""True"", FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass. 
limit_all_gathers can be specified in the config file. 
    If ""True"", FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers.

Few caveats to be aware of
- it is incompatible with generate, thus is incompatible with --predict_with_generate 
  in all seq2seq/clm scripts (translation/summarization/clm etc.).
  Please refer issue #21667
PyTorch/XLA Fully Sharded Data parallel
For all the TPU users, great news! PyTorch/XLA now supports FSDP.
All the latest Fully Sharded Data Parallel (FSDP) training are supported.
For more information refer to the Scaling PyTorch models on Cloud TPUs with FSDP and PyTorch/XLA implementation of FSDP
All you need to do is enable it through the config.
Required PyTorch/XLA version for FSDP support: >=2.0
Usage:
Pass --fsdp ""full shard"" along with following changes to be made in --fsdp_config <path_to_fsdp_config.json>:
- xla should be set to True to enable PyTorch/XLA FSDP.
- xla_fsdp_settings The value is a dictionary which stores the XLA FSDP wrapping parameters.
  For a complete list of options, please see here.
- xla_fsdp_grad_ckpt. When True, uses gradient checkpointing over each nested XLA FSDP wrapped layer. 
  This setting can only be used when the xla flag is set to true, and an auto wrapping policy is specified through
  fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap. 
- You can either use transformer based auto wrap policy or size based auto wrap policy.
  - For transformer based auto wrap policy, it is recommended to specify fsdp_transformer_layer_cls_to_wrap in the config file. If not specified, the default value is model._no_split_modules when available.
    This specifies the list of transformer layer class name (case-sensitive) to wrap ,e.g, [BertLayer], [GPTJBlock], [T5Block] .
    This is important because submodules that share weights (e.g., embedding layer) should not end up in different FSDP wrapped units.
    Using this policy, wrapping happens for each block containing Multi-Head Attention followed by couple of MLP layers. 
    Remaining layers including the shared embeddings are conveniently wrapped in same outermost FSDP unit.
    Therefore, use this for transformer based models.
  - For size based auto wrap policy, please add fsdp_min_num_params in the config file. 
    It specifies FSDP's minimum number of parameters for auto wrapping.
Using Trainer for accelerated PyTorch Training on Mac
With PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. 
This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac.
Apple's Metal Performance Shaders (MPS) as a backend for PyTorch enables this and can be used via the new ""mps"" device. 
This will map computational graphs and primitives on the MPS Graph framework and tuned kernels provided by MPS.
For more information please refer official documents Introducing Accelerated PyTorch Training on Mac
and MPS BACKEND. 

We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing) on your MacOS machine. 
It has major fixes related to model correctness and performance improvements for transformer based models.
Please refer to https://github.com/pytorch/pytorch/issues/82707 for more details.

Benefits of Training and Inference using Apple Silicon Chips

Enables users to train larger networks or batch sizes locally
Reduces data retrieval latency and provides the GPU with direct access to the full memory store due to unified memory architecture. 
Therefore, improving end-to-end performance.
Reduces costs associated with cloud-based development or the need for additional local GPUs.

Pre-requisites: To install torch with mps support, 
please follow this nice medium article GPU-Acceleration Comes to PyTorch on M1 Macs.
Usage:
mps device will be used by default if available similar to the way cuda device is used.
Therefore, no action from user is required. 
For example, you can run the official Glue text classififcation task (from the root folder) using Apple Silicon GPU with below command:
```bash
export TASK_NAME=mrpc
python examples/pytorch/text-classification/run_glue.py \
  --model_name_or_path bert-base-cased \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir /tmp/$TASK_NAME/ \
  --overwrite_output_dir

A few caveats to be aware of

Some PyTorch operations have not been implemented in mps and will throw an error. 
One way to get around that is to set the environment variable PYTORCH_ENABLE_MPS_FALLBACK=1, 
which will fallback to CPU for these operations. It still throws a UserWarning however.
Distributed setups gloo and nccl are not working with mps device. 
This means that currently only single GPU of mps device type can be used.

Finally, please, remember that, 🤗 Trainer only integrates MPS backend, therefore if you
have any problems or questions with regards to MPS backend usage, please, 
file an issue with PyTorch GitHub.
Using Accelerate Launcher with Trainer
Accelerate now powers Trainer. In terms of what users should expect:
- They can keep using the Trainer ingterations such as FSDP, DeepSpeed vis trainer arguments without any changes on their part.
- They can now use Accelerate Launcher with Trainer (recommended).
Steps to use Accelerate Launcher with Trainer:
1. Make sure 🤗 Accelerate is installed, you can't use the Trainer without it anyway. If not pip install accelerate. You may also need to update your version of Accelerate: pip install accelerate --upgrade
2. Run accelerate config and fill the questionnaire. Below are example accelerate configs:
  a. DDP Multi-node Multi-GPU config:
    yaml
    compute_environment: LOCAL_MACHINE                                                                                             
    distributed_type: MULTI_GPU                                                                                                    
    downcast_bf16: 'no'
    gpu_ids: all
    machine_rank: 0 #change rank as per the node
    main_process_ip: 192.168.20.1
    main_process_port: 9898
    main_training_function: main
    mixed_precision: fp16
    num_machines: 2
    num_processes: 8
    rdzv_backend: static
    same_network: true
    tpu_env: []
    tpu_use_cluster: false
    tpu_use_sudo: false
    use_cpu: false
b. FSDP config:
    yaml
    compute_environment: LOCAL_MACHINE
    distributed_type: FSDP
    downcast_bf16: 'no'
    fsdp_config:
      fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
      fsdp_backward_prefetch_policy: BACKWARD_PRE
      fsdp_forward_prefetch: true
      fsdp_offload_params: false
      fsdp_sharding_strategy: 1
      fsdp_state_dict_type: FULL_STATE_DICT
      fsdp_sync_module_states: true
      fsdp_transformer_layer_cls_to_wrap: BertLayer
      fsdp_use_orig_params: true
    machine_rank: 0
    main_training_function: main
    mixed_precision: bf16
    num_machines: 1
    num_processes: 2
    rdzv_backend: static
    same_network: true
    tpu_env: []
    tpu_use_cluster: false
    tpu_use_sudo: false
    use_cpu: false
  c. DeepSpeed config pointing to a file:
    yaml
    compute_environment: LOCAL_MACHINE
    deepspeed_config:
      deepspeed_config_file: /home/user/configs/ds_zero3_config.json
      zero3_init_flag: true
    distributed_type: DEEPSPEED
    downcast_bf16: 'no'
    machine_rank: 0
    main_training_function: main
    num_machines: 1
    num_processes: 4
    rdzv_backend: static
    same_network: true
    tpu_env: []
    tpu_use_cluster: false
    tpu_use_sudo: false
    use_cpu: false
d. DeepSpeed config using accelerate plugin:
    yaml
    compute_environment: LOCAL_MACHINE                                                                                             
    deepspeed_config:                                                                                                              
      gradient_accumulation_steps: 1
      gradient_clipping: 0.7
      offload_optimizer_device: cpu
      offload_param_device: cpu
      zero3_init_flag: true
      zero_stage: 2
    distributed_type: DEEPSPEED
    downcast_bf16: 'no'
    machine_rank: 0
    main_training_function: main
    mixed_precision: bf16
    num_machines: 1
    num_processes: 4
    rdzv_backend: static
    same_network: true
    tpu_env: []
    tpu_use_cluster: false
    tpu_use_sudo: false
    use_cpu: false

Run the Trainer script with args other than the ones handled above by accelerate config or launcher args.
Below is an example to run run_glue.py using accelerate launcher with FSDP config from above. 

```bash
cd transformers
accelerate launch \
./examples/pytorch/text-classification/run_glue.py \
--model_name_or_path bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 16 \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir

You can also directly use the cmd args for accelerate launch. Above example would map to:

```bash
cd transformers
accelerate launch --num_processes=2 \
--use_fsdp \
--mixed_precision=bf16 \
--fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP  \
--fsdp_transformer_layer_cls_to_wrap=""BertLayer"" \
--fsdp_sharding_strategy=1 \
--fsdp_state_dict_type=FULL_STATE_DICT \
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 16 \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir

For more information, please refer the 🤗 Accelerate CLI guide: Launching your 🤗 Accelerate scripts.
Sections that were moved:
[ DeepSpeed
| Installation
| Deployment with multiple GPUs
| Deployment with one GPU
| Deployment in Notebooks
| Configuration
| Passing Configuration
| Shared Configuration
| ZeRO
| ZeRO-2 Config
| ZeRO-3 Config
| NVMe Support
| ZeRO-2 vs ZeRO-3 Performance
| ZeRO-2 Example
| ZeRO-3 Example
| Optimizer
| Scheduler
| fp32 Precision
| Automatic Mixed Precision
| Batch Size
| Gradient Accumulation
| Gradient Clipping
| Getting The Model Weights Out
]"
"
Export to ONNX
Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into 
a serialized format that can be loaded and executed on specialized runtimes and hardware.
🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch or TensorFlow to serialized formats 
such as ONNX and TFLite through its exporters module. 🤗 Optimum also provides a set of performance optimization tools to train 
and run models on targeted hardware with maximum efficiency.
This guide demonstrates how you can export 🤗 Transformers models to ONNX with 🤗 Optimum, for the guide on exporting models to TFLite, 
please refer to the Export to TFLite page.
Export to ONNX
ONNX (Open Neural Network eXchange) is an open standard that defines a common set of operators and a 
common file format to represent deep learning models in a wide variety of frameworks, including PyTorch and
TensorFlow. When a model is exported to the ONNX format, these operators are used to
construct a computational graph (often called an intermediate representation) which
represents the flow of data through the neural network.
By exposing a graph with standardized operators and data types, ONNX makes it easy to
switch between frameworks. For example, a model trained in PyTorch can be exported to
ONNX format and then imported in TensorFlow (and vice versa).
Once exported to ONNX format, a model can be:
- optimized for inference via techniques such as graph optimization and quantization. 
- run with ONNX Runtime via ORTModelForXXX classes,
which follow the same AutoModel API as the one you are used to in 🤗 Transformers.
- run with optimized inference pipelines,
which has the same API as the [pipeline] function in 🤗 Transformers. 
🤗 Optimum provides support for the ONNX export by leveraging configuration objects. These configuration objects come 
ready-made for a number of model architectures, and are designed to be easily extendable to other architectures.
For the list of ready-made configurations, please refer to 🤗 Optimum documentation.
There are two ways to export a 🤗 Transformers model to ONNX, here we show both:

export with 🤗 Optimum via CLI.
export with 🤗 Optimum with optimum.onnxruntime.

Exporting a 🤗 Transformers model to ONNX with CLI
To export a 🤗 Transformers model to ONNX, first install an extra dependency:

pip install optimum[exporters]
To check out all available arguments, refer to the 🤗 Optimum docs, 
or view help in command line:

optimum-cli export onnx --help
To export a model's checkpoint from the 🤗 Hub, for example, distilbert-base-uncased-distilled-squad, run the following command: 

optimum-cli export onnx --model distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
You should see the logs indicating progress and showing where the resulting model.onnx is saved, like this:

Validating ONNX model distilbert_base_uncased_squad_onnx/model.onnx
    -[✓] ONNX model output names match reference model (start_logits, end_logits)
    - Validating ONNX Model output ""start_logits"":
        -[✓] (2, 16) matches (2, 16)
        -[✓] all values close (atol: 0.0001)
    - Validating ONNX Model output ""end_logits"":
        -[✓] (2, 16) matches (2, 16)
        -[✓] all values close (atol: 0.0001)
The ONNX export succeeded and the exported model was saved at: distilbert_base_uncased_squad_onnx
The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you 
saved both the model's weights and tokenizer files in the same directory (local_path). When using CLI, pass the 
local_path to the model argument instead of the checkpoint name on 🤗 Hub and provide the --task argument. 
You can review the list of supported tasks in the 🤗 Optimum documentation.
If task argument is not provided, it will default to the model architecture without any task specific head.

optimum-cli export onnx --model local_path --task question-answering distilbert_base_uncased_squad_onnx/
The resulting model.onnx file can then be run on one of the many
accelerators that support the ONNX
standard. For example, we can load and run the model with ONNX
Runtime as follows:
thon

from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained(""distilbert_base_uncased_squad_onnx"")
model = ORTModelForQuestionAnswering.from_pretrained(""distilbert_base_uncased_squad_onnx"")
inputs = tokenizer(""What am I using?"", ""Using DistilBERT with ONNX Runtime!"", return_tensors=""pt"")
outputs = model(**inputs)

The process is identical for TensorFlow checkpoints on the Hub. For instance, here's how you would
export a pure TensorFlow checkpoint from the Keras organization:

optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_squad_onnx/
Exporting a 🤗 Transformers model to ONNX with optimum.onnxruntime
Alternative to CLI, you can export a 🤗 Transformers model to ONNX programmatically like so: 
thon

from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
model_checkpoint = ""distilbert_base_uncased_squad""
save_directory = ""onnx/""
Load a model from transformers and export it to ONNX
ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
Save the onnx model and tokenizer
ort_model.save_pretrained(save_directory)
tokenizer.save_pretrained(save_directory)

Exporting a model for an unsupported architecture
If you wish to contribute by adding support for a model that cannot be currently exported, you should first check if it is
supported in optimum.exporters.onnx,
and if it is not, contribute to 🤗 Optimum
directly.
Exporting a model with transformers.onnx

tranformers.onnx is no longer maintained, please export models with 🤗 Optimum as described above. This section will be removed in the future versions.

To export a 🤗 Transformers model to ONNX with tranformers.onnx, install extra dependencies:

pip install transformers[onnx]
Use transformers.onnx package as a Python module to export a checkpoint using a ready-made configuration:

python -m transformers.onnx --model=distilbert-base-uncased onnx/
This exports an ONNX graph of the checkpoint defined by the --model argument. Pass any checkpoint on the 🤗 Hub or one that's stored locally.
The resulting model.onnx file can then be run on one of the many accelerators that support the ONNX standard. For example, 
load and run the model with ONNX Runtime as follows:
thon

from transformers import AutoTokenizer
from onnxruntime import InferenceSession
tokenizer = AutoTokenizer.from_pretrained(""distilbert-base-uncased"")
session = InferenceSession(""onnx/model.onnx"")
ONNX Runtime expects NumPy arrays as input
inputs = tokenizer(""Using DistilBERT with ONNX Runtime!"", return_tensors=""np"")
outputs = session.run(output_names=[""last_hidden_state""], input_feed=dict(inputs))

The required output names (like [""last_hidden_state""]) can be obtained by taking a look at the ONNX configuration of 
each model. For example, for DistilBERT we have:
thon

from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig
config = DistilBertConfig()
onnx_config = DistilBertOnnxConfig(config)
print(list(onnx_config.outputs.keys()))
[""last_hidden_state""]

The process is identical for TensorFlow checkpoints on the Hub. For example, export a pure TensorFlow checkpoint like so:

python -m transformers.onnx --model=keras-io/transformers-qa onnx/
To export a model that's stored locally, save the model's weights and tokenizer files in the same directory (e.g. local-pt-checkpoint), 
then export it to ONNX by pointing the --model argument of the transformers.onnx package to the desired directory:

python -m transformers.onnx --model=local-pt-checkpoint onnx/"
"
🤗 Transformers Notebooks
You can find here a list of the official notebooks provided by Hugging Face.
Also, we would like to list here interesting content created by the community.
If you wrote some notebook(s) leveraging 🤗 Transformers and would like be listed here, please open a
Pull Request so it can be included under the Community notebooks.
Hugging Face's notebooks 🤗
Documentation notebooks
You can open any page of the documentation as a notebook in Colab (there is a button directly on said pages) but they are also listed here if you need them:
| Notebook     |      Description      |   |   |
|:----------|:-------------|:-------------|------:|
| Quicktour of the library  | A presentation of the various APIs in Transformers || |
| Summary of the tasks  | How to run the models of the Transformers library task by task || |
| Preprocessing data  | How to use a tokenizer to preprocess your data || |
| Fine-tuning a pretrained model  | How to use the Trainer to fine-tune a pretrained model || |
| Summary of the tokenizers  | The differences between the tokenizers algorithm || |
| Multilingual models  | How to use the multilingual models of the library || |
PyTorch Examples
Natural Language Processing[[pytorch-nlp]]
| Notebook     |      Description      |   |   |
|:----------|:-------------|:-------------|------:|
| Train your tokenizer  | How to train and use your very own tokenizer  || |
| Train your language model   | How to easily start using transformers  || |
| How to fine-tune a model on text classification| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | | |
| How to fine-tune a model on language modeling| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | | |
| How to fine-tune a model on token classification| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | | |
| How to fine-tune a model on question answering| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | | |
| How to fine-tune a model on multiple choice| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | | |
| How to fine-tune a model on translation| Show how to preprocess the data and fine-tune a pretrained model on WMT. | | |
| How to fine-tune a model on summarization| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | | |
| How to train a language model from scratch| Highlight all the steps to effectively train Transformer model on custom data | | |
| How to generate text| How to use different decoding methods for language generation with transformers | | |
| How to generate text (with constraints)| How to guide language generation with user-provided constraints | | |
| Reformer| How Reformer pushes the limits of language modeling | | |
Computer Vision[[pytorch-cv]]
| Notebook                                                                                                                                                                   | Description                                                                                                            |                                                                                                                                                                                                            |   |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------:|
| How to fine-tune a model on image classification (Torchvision)                   | Show how to preprocess the data using Torchvision and fine-tune any pretrained Vision model on Image Classification    |                  | |
| How to fine-tune a model on image classification (Albumentations) | Show how to preprocess the data using Albumentations and fine-tune any pretrained Vision model on Image Classification |   | |
| How to fine-tune a model on image classification (Kornia)                 | Show how to preprocess the data using Kornia and fine-tune any pretrained Vision model on Image Classification         |           | |
| How to perform zero-shot object detection with OWL-ViT          | Show how to perform zero-shot object detection on images with text queries                                             | | |
| How to fine-tune an image captioning model                                      | Show how to fine-tune BLIP for image captioning on a custom dataset                                                    |                 | |
| How to build an image similarity system with Transformers                            | Show how to build an image similarity system                                                                           |                      | |
| How to fine-tune a SegFormer model on semantic segmentation                     | Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation                    |                 | |
| How to fine-tune a VideoMAE model on video classification          | Show how to preprocess the data and fine-tune a pretrained VideoMAE model on Video Classification                      |                 | |
Audio[[pytorch-audio]]
| Notebook     |      Description      |   |   |
|:----------|:-------------|:-------------|------:|
| How to fine-tune a speech recognition model in English| Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT | | |
| How to fine-tune a speech recognition model in any language| Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice | | |
| How to fine-tune a model on audio classification| Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting | | |
Biological Sequences[[pytorch-bio]]
| Notebook     | Description                                                                             |   |   |
|:----------|:----------------------------------------------------------------------------------------|:-------------|------:|
| How to fine-tune a pre-trained protein model | See how to tokenize proteins and fine-tune a large pre-trained protein ""language"" model |  |  |
| How to generate protein folds | See how to go from protein sequence to a full protein model and PDB file                |  |  |
| How to fine-tune a Nucleotide Transformer model | See how to tokenize DNA and fine-tune a large pre-trained DNA ""language"" model |  |  |
| Fine-tune a Nucleotide Transformer model with LoRA | Train even larger DNA models in a memory-efficient way |  |  |
Other modalities[[pytorch-other]]
| Notebook     | Description                                                                             |   |   |
|:----------|:----------------------------------------------------------------------------------------|:-------------|------:|
| Probabilistic Time Series Forecasting | See how to train Time Series Transformer on a custom dataset                            |  |  |
Utility notebooks[[pytorch-utility]]
| Notebook     |      Description      |   |   |
|:----------|:-------------|:-------------|------:|
| How to export model to ONNX| Highlight how to export and run inference workloads through ONNX |
| How to use Benchmarks| How to benchmark models with transformers | | |
TensorFlow Examples
Natural Language Processing[[tensorflow-nlp]]
| Notebook     |      Description      |   |   |
|:----------|:-------------|:-------------|------:|
| Train your tokenizer  | How to train and use your very own tokenizer  || |
| Train your language model   | How to easily start using transformers  || |
| How to fine-tune a model on text classification| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | | |
| How to fine-tune a model on language modeling| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | | |
| How to fine-tune a model on token classification| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | | |
| How to fine-tune a model on question answering| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | | |
| How to fine-tune a model on multiple choice| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | | |
| How to fine-tune a model on translation| Show how to preprocess the data and fine-tune a pretrained model on WMT. | | |
| How to fine-tune a model on summarization| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | | |
Computer Vision[[tensorflow-cv]]
| Notebook                                                                                                                                                 | Description                                                                                         |   |   |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-------------|------:|
| How to fine-tune a model on image classification            | Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification   | | |
| How to fine-tune a SegFormer model on semantic segmentation | Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation | | |
Biological Sequences[[tensorflow-bio]]
| Notebook     |      Description      |   |   |
|:----------|:-------------|:-------------|------:|
| How to fine-tune a pre-trained protein model | See how to tokenize proteins and fine-tune a large pre-trained protein ""language"" model |  |  |
Utility notebooks[[tensorflow-utility]]
| Notebook     |      Description      |   |                                                                                                                                                                                      |
|:----------|:-------------|:-------------|------:|
| How to train TF/Keras models on TPU | See how to train at high speed on Google's TPU hardware |  |  |
Optimum notebooks
🤗  Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardwares.
| Notebook     |      Description      |   |   |
|:----------|:-------------|:-------------|------:|
| How to quantize a model with ONNX Runtime for text classification| Show how to apply static and dynamic quantization on a model using ONNX Runtime for any GLUE task. | | |
| How to quantize a model with Intel Neural Compressor for text classification| Show how to apply static, dynamic and aware training quantization on a model using Intel Neural Compressor (INC) for any GLUE task. | | |
| How to fine-tune a model on text classification with ONNX Runtime| Show how to preprocess the data and fine-tune a model on any GLUE task using ONNX Runtime. | | |
| How to fine-tune a model on summarization with ONNX Runtime| Show how to preprocess the data and fine-tune a model on XSUM using ONNX Runtime. | | |
Community notebooks:
More notebooks developed by the community are available here."
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

from huggingface_hub import notebook_login
notebook_login()

Load IMDb dataset
Start by loading the IMDb dataset from the 🤗 Datasets library:

from datasets import load_dataset
imdb = load_dataset(""imdb"")

Then take a look at an example:

imdb[""test""][0]
{
    ""label"": 0,
    ""text"": ""I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \""Gene Roddenberry's Earth\"" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again."",
}

There are two fields in this dataset:

text: the movie review text.
label: a value that is either 0 for a negative review or 1 for a positive review.

Preprocess
The next step is to load a DistilBERT tokenizer to preprocess the text field:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""distilbert-base-uncased"")

Create a preprocessing function to tokenize text and truncate sequences to be no longer than DistilBERT's maximum input length:

def preprocess_function(examples):
     return tokenizer(examples[""text""], truncation=True)

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up map by setting batched=True to process multiple elements of the dataset at once:
py
tokenized_imdb = imdb.map(preprocess_function, batched=True)
Now create a batch of examples using [DataCollatorWithPadding]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.

from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
</pt>
<tf>py
from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=""tf"")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
accuracy = evaluate.load(""accuracy"")

Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the accuracy:

import numpy as np
def compute_metrics(eval_pred):
     predictions, labels = eval_pred
     predictions = np.argmax(predictions, axis=1)
     return accuracy.compute(predictions=predictions, references=labels)

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train
Before you start training your model, create a map of the expected ids to their labels with id2label and label2id:

id2label = {0: ""NEGATIVE"", 1: ""POSITIVE""}
label2id = {""NEGATIVE"": 0, ""POSITIVE"": 1}

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load DistilBERT with [AutoModelForSequenceClassification] along with the number of expected labels, and the label mappings:

from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
model = AutoModelForSequenceClassification.from_pretrained(
     ""distilbert-base-uncased"", num_labels=2, id2label=id2label, label2id=label2id
 )

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the accuracy and save the training checkpoint.
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_model"",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
     num_train_epochs=2,
     weight_decay=0.01,
     evaluation_strategy=""epoch"",
     save_strategy=""epoch"",
     load_best_model_at_end=True,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_imdb[""train""],
     eval_dataset=tokenized_imdb[""test""],
     tokenizer=tokenizer,
     data_collator=data_collator,
     compute_metrics=compute_metrics,
 )
trainer.train()

[Trainer] applies dynamic padding by default when you pass tokenizer to it. In this case, you don't need to specify a data collator explicitly.

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer
import tensorflow as tf
batch_size = 16
num_epochs = 5
batches_per_epoch = len(tokenized_imdb[""train""]) // batch_size
total_train_steps = int(batches_per_epoch * num_epochs)
optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)

Then you can load DistilBERT with [TFAutoModelForSequenceClassification] along with the number of expected labels, and the label mappings:

from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained(
     ""distilbert-base-uncased"", num_labels=2, id2label=id2label, label2id=label2id
 )

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     tokenized_imdb[""train""],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_validation_set = model.prepare_tf_dataset(
     tokenized_imdb[""test""],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

import tensorflow as tf
model.compile(optimizer=optimizer)  # No loss argument!

The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks.
Pass your compute_metrics function to [~transformers.KerasMetricCallback]:

from transformers.keras_callbacks import KerasMetricCallback
metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)

Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
     output_dir=""my_awesome_model"",
     tokenizer=tokenizer,
 )

Then bundle your callbacks together:

callbacks = [metric_callback, push_to_hub_callback]

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for text classification, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Grab some text you'd like to run inference on:

text = ""This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three.""

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for sentiment analysis with your model, and pass your text to it:

from transformers import pipeline
classifier = pipeline(""sentiment-analysis"", model=""stevhliu/my_awesome_model"")
classifier(text)
[{'label': 'POSITIVE', 'score': 0.9994940757751465}]

You can also manually replicate the results of the pipeline if you'd like:

Tokenize the text and return PyTorch tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""stevhliu/my_awesome_model"")
inputs = tokenizer(text, return_tensors=""pt"")

Pass your inputs to the model and return the logits:

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(""stevhliu/my_awesome_model"")
with torch.no_grad():
     logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label:

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]
'POSITIVE'

Tokenize the text and return TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""stevhliu/my_awesome_model"")
inputs = tokenizer(text, return_tensors=""tf"")

Pass your inputs to the model and return the logits:

from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained(""stevhliu/my_awesome_model"")
logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label:

predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
model.config.id2label[predicted_class_id]
'POSITIVE'

"
"
How 🤗 Transformers solve tasks
In What 🤗 Transformers can do, you learned about natural language processing (NLP), speech and audio, computer vision tasks, and some important applications of them. This page will look closely at how models solve these tasks and explain what's happening under the hood. There are many ways to solve a given task, some models may implement certain techniques or even approach the task from a new angle, but for Transformer models, the general idea is the same. Owing to its flexible architecture, most models are a variant of an encoder, decoder, or encoder-decoder structure. In addition to Transformer models, our library also has several convolutional neural networks (CNNs), which are still used today for computer vision tasks. We'll also explain how a modern CNN works.
To explain how tasks are solved, we'll walk through what goes on inside the model to output useful predictions.

Wav2Vec2 for audio classification and automatic speech recognition (ASR)
Vision Transformer (ViT) and ConvNeXT for image classification
DETR for object detection
Mask2Former for image segmentation
GLPN for depth estimation
BERT for NLP tasks like text classification, token classification and question answering that use an encoder
GPT2 for NLP tasks like text generation that use a decoder
BART for NLP tasks like summarization and translation that use an encoder-decoder

Before you go further, it is good to have some basic knowledge of the original Transformer architecture. Knowing how encoders, decoders, and attention work will aid you in understanding how different Transformer models work. If you're just getting started or need a refresher, check out our course for more information! 

Speech and audio
Wav2Vec2 is a self-supervised model pretrained on unlabeled speech data and finetuned on labeled data for audio classification and automatic speech recognition. 

This model has four main components:

A feature encoder takes the raw audio waveform, normalizes it to zero mean and unit variance, and converts it into a sequence of feature vectors that are each 20ms long.

Waveforms are continuous by nature, so they can't be divided into separate units like a sequence of text can be split into words. That's why the feature vectors are passed to a quantization module, which aims to learn discrete speech units. The speech unit is chosen from a collection of codewords, known as a codebook (you can think of this as the vocabulary). From the codebook, the vector or speech unit, that best represents the continuous audio input is chosen and forwarded through the model.

About half of the feature vectors are randomly masked, and the masked feature vector is fed to a context network, which is a Transformer encoder that also adds relative positional embeddings.

The pretraining objective of the context network is a contrastive task. The model has to predict the true quantized speech representation of the masked prediction from a set of false ones, encouraging the model to find the most similar context vector and quantized speech unit (the target label).

Now that wav2vec2 is pretrained, you can finetune it on your data for audio classification or automatic speech recognition!
Audio classification
To use the pretrained model for audio classification, add a sequence classification head on top of the base Wav2Vec2 model. The classification head is a linear layer that accepts the encoder's hidden states. The hidden states represent the learned features from each audio frame which can have varying lengths. To create one vector of fixed-length, the hidden states are pooled first and then transformed into logits over the class labels. The cross-entropy loss is calculated between the logits and target to find the most likely class.
Ready to try your hand at audio classification? Check out our complete audio classification guide to learn how to finetune Wav2Vec2 and use it for inference!
Automatic speech recognition
To use the pretrained model for automatic speech recognition, add a language modeling head on top of the base Wav2Vec2 model for connectionist temporal classification (CTC). The language modeling head is a linear layer that accepts the encoder's hidden states and transforms them into logits. Each logit represents a token class (the number of tokens comes from the task vocabulary). The CTC loss is calculated between the logits and targets to find the most likely sequence of tokens, which are then decoded into a transcription.
Ready to try your hand at automatic speech recognition? Check out our complete automatic speech recognition guide to learn how to finetune Wav2Vec2 and use it for inference!
Computer vision
There are two ways to approach computer vision tasks:

Split an image into a sequence of patches and process them in parallel with a Transformer.
Use a modern CNN, like ConvNeXT, which relies on convolutional layers but adopts modern network designs.

A third approach mixes Transformers with convolutions (for example, Convolutional Vision Transformer or LeViT). We won't discuss those because they just combine the two approaches we examine here.

ViT and ConvNeXT are commonly used for image classification, but for other vision tasks like object detection, segmentation, and depth estimation, we'll look at DETR, Mask2Former and GLPN, respectively; these models are better suited for those tasks.
Image classification
ViT and ConvNeXT can both be used for image classification; the main difference is that ViT uses an attention mechanism while ConvNeXT uses convolutions.
Transformer
ViT replaces convolutions entirely with a pure Transformer architecture. If you're familiar with the original Transformer, then you're already most of the way toward understanding ViT.

The main change ViT introduced was in how images are fed to a Transformer:

An image is split into square non-overlapping patches, each of which gets turned into a vector or patch embedding. The patch embeddings are generated from a convolutional 2D layer which creates the proper input dimensions (which for a base Transformer is 768 values for each patch embedding). If you had a 224x224 pixel image, you could split it into 196 16x16 image patches. Just like how text is tokenized into words, an image is ""tokenized"" into a sequence of patches.

A learnable embedding - a special [CLS] token - is added to the beginning of the patch embeddings just like BERT. The final hidden state of the [CLS] token is used as the input to the attached classification head; other outputs are ignored. This token helps the model learn how to encode a representation of the image.

The last thing to add to the patch and learnable embeddings are the position embeddings because the model doesn't know how the image patches are ordered. The position embeddings are also learnable and have the same size as the patch embeddings. Finally, all of the embeddings are passed to the Transformer encoder.

The output, specifically only the output with the [CLS] token, is passed to a multilayer perceptron head (MLP). ViT's pretraining objective is simply classification. Like other classification heads, the MLP head converts the output into logits over the class labels and calculates the cross-entropy loss to find the most likely class.

Ready to try your hand at image classification? Check out our complete image classification guide to learn how to finetune ViT and use it for inference!
CNN

This section briefly explains convolutions, but it'd be helpful to have a prior understanding of how they change an image's shape and size. If you're unfamiliar with convolutions, check out the Convolution Neural Networks chapter from the fastai book!

ConvNeXT is a CNN architecture that adopts new and modern network designs to improve performance. However, convolutions are still at the core of the model. From a high-level perspective, a convolution is an operation where a smaller matrix (kernel) is multiplied by a small window of the image pixels. It computes some features from it, such as a particular texture or curvature of a line. Then it slides over to the next window of pixels; the distance the convolution travels is known as the stride. 

A basic convolution without padding or stride, taken from A guide to convolution arithmetic for deep learning.
You can feed this output to another convolutional layer, and with each successive layer, the network learns more complex and abstract things like hotdogs or rockets. Between convolutional layers, it is common to add a pooling layer to reduce dimensionality and make the model more robust to variations of a feature's position.

ConvNeXT modernizes a CNN in five ways:

Change the number of blocks in each stage and ""patchify"" an image with a larger stride and corresponding kernel size. The non-overlapping sliding window makes this patchifying strategy similar to how ViT splits an image into patches.

A bottleneck layer shrinks the number of channels and then restores it because it is faster to do a 1x1 convolution, and you can increase the depth. An inverted bottleneck does the opposite by expanding the number of channels and shrinking them, which is more memory efficient.

Replace the typical 3x3 convolutional layer in the bottleneck layer with depthwise convolution, which applies a convolution to each input channel separately and then stacks them back together at the end. This widens the network width for improved performance.

ViT has a global receptive field which means it can see more of an image at once thanks to its attention mechanism. ConvNeXT attempts to replicate this effect by increasing the kernel size to 7x7.

ConvNeXT also makes several layer design changes that imitate Transformer models. There are fewer activation and normalization layers,  the activation function is switched to GELU instead of ReLU, and it uses LayerNorm instead of BatchNorm.

The output from the convolution blocks is passed to a classification head which converts the outputs into logits and calculates the cross-entropy loss to find the most likely label.
Object detection
DETR, DEtection TRansformer, is an end-to-end object detection model that combines a CNN with a Transformer encoder-decoder.

A pretrained CNN backbone takes an image, represented by its pixel values, and creates a low-resolution feature map of it. A 1x1 convolution is applied to the feature map to reduce dimensionality and it creates a new feature map with a high-level image representation. Since the Transformer is a sequential model, the feature map is flattened into a sequence of feature vectors that are combined with positional embeddings.

The feature vectors are passed to the encoder, which learns the image representations using its attention layers. Next, the encoder hidden states are combined with object queries in the decoder. Object queries are learned embeddings that focus on the different regions of an image, and they're updated as they progress through each attention layer. The decoder hidden states are passed to a feedforward network that predicts the bounding box coordinates and class label for each object query, or no object if there isn't one.
DETR decodes each object query in parallel to output N final predictions, where N is the number of queries. Unlike a typical autoregressive model that predicts one element at a time, object detection is a set prediction task (bounding box, class label) that makes N predictions in a single pass.

DETR uses a bipartite matching loss during training to compare a fixed number of predictions with a fixed set of ground truth labels. If there are fewer ground truth labels in the set of N labels, then they're padded with a no object class. This loss function encourages DETR to find a one-to-one assignment between the predictions and ground truth labels. If either the bounding boxes or class labels aren't correct, a loss is incurred. Likewise, if DETR predicts an object that doesn't exist, it is penalized. This encourages DETR to find other objects in an image instead of focusing on one really prominent object.

An object detection head is added on top of DETR to find the class label and the coordinates of the bounding box. There are two components to the object detection head: a linear layer to transform the decoder hidden states into logits over the class labels, and a MLP to predict the bounding box.
Ready to try your hand at object detection? Check out our complete object detection guide to learn how to finetune DETR and use it for inference!
Image segmentation
Mask2Former is a universal architecture for solving all types of image segmentation tasks. Traditional segmentation models are typically tailored towards a particular subtask of image segmentation, like instance, semantic or panoptic segmentation. Mask2Former frames each of those tasks as a mask classification problem. Mask classification groups pixels into N segments, and predicts N masks and their corresponding class label for a given image. We'll explain how Mask2Former works in this section, and then you can try finetuning SegFormer at the end.

There are three main components to Mask2Former:

A Swin backbone accepts an image and creates a low-resolution image feature map from 3 consecutive 3x3 convolutions.

The feature map is passed to a pixel decoder which gradually upsamples the low-resolution features into high-resolution per-pixel embeddings. The pixel decoder actually generates multi-scale features (contains both low- and high-resolution features) with resolutions 1/32, 1/16, and 1/8th of the original image.

Each of these feature maps of differing scales is fed successively to one Transformer decoder layer at a time in order to capture small objects from the high-resolution features. The key to Mask2Former is the masked attention mechanism in the decoder. Unlike cross-attention which can attend to the entire image, masked attention only focuses on a certain area of the image. This is faster and leads to better performance because the local features of an image are enough for the model to learn from.

Like DETR, Mask2Former also uses learned object queries and combines them with the image features from the pixel decoder to make a set prediction (class label, mask prediction). The decoder hidden states are passed into a linear layer and transformed into logits over the class labels. The cross-entropy loss is calculated between the logits and class label to find the most likely one.
The mask predictions are generated by combining the pixel-embeddings with the final decoder hidden states. The sigmoid cross-entropy and dice loss is calculated between the logits and the ground truth mask to find the most likely mask.

Ready to try your hand at object detection? Check out our complete image segmentation guide to learn how to finetune SegFormer and use it for inference!
Depth estimation
GLPN, Global-Local Path Network, is a Transformer for depth estimation that combines a SegFormer encoder with a lightweight decoder.

Like ViT, an image is split into a sequence of patches, except these image patches are smaller. This is better for dense prediction tasks like segmentation or depth estimation. The image patches are transformed into patch embeddings (see the image classification section for more details about how patch embeddings are created), which are fed to the encoder.

The encoder accepts the patch embeddings, and passes them through several encoder blocks. Each block consists of attention and Mix-FFN layers. The purpose of the latter is to provide positional information. At the end of each encoder block is a patch merging layer for creating hierarchical representations. The features of each group of neighboring patches are concatenated, and a linear layer is applied to the concatenated features to reduce the number of patches to a resolution of 1/4. This becomes the input to the next encoder block, where this whole process is repeated until you have image features with resolutions of 1/8, 1/16, and 1/32.

A lightweight decoder takes the last feature map (1/32 scale) from the encoder and upsamples it to 1/16 scale. From here, the feature is passed into a Selective Feature Fusion (SFF) module, which selects and combines local and global features from an attention map for each feature and then upsamples it to 1/8th. This process is repeated until the decoded features are the same size as the original image. The output is passed through two convolution layers and then a sigmoid activation is applied to predict the depth of each pixel.

Natural language processing
The Transformer was initially designed for machine translation, and since then, it has practically become the default architecture for solving all NLP tasks. Some tasks lend themselves to the Transformer's encoder structure, while others are better suited for the decoder. Still, other tasks make use of both the Transformer's encoder-decoder structure.
Text classification
BERT is an encoder-only model and is the first model to effectively implement deep bidirectionality to learn richer representations of the text by attending to words on both sides.

BERT uses WordPiece tokenization to generate a token embedding of the text. To tell the difference between a single sentence and a pair of sentences, a special [SEP] token is added to differentiate them. A special [CLS] token is added to the beginning of every sequence of text. The final output with the [CLS] token is used as the input to the classification head for classification tasks. BERT also adds a segment embedding to denote whether a token belongs to the first or second sentence in a pair of sentences.

BERT is pretrained with two objectives: masked language modeling and next-sentence prediction. In masked language modeling, some percentage of the input tokens are randomly masked, and the model needs to predict these. This solves the issue of bidirectionality, where the model could cheat and see all the words and ""predict"" the next word. The final hidden states of the predicted mask tokens are passed to a feedforward network with a softmax over the vocabulary to predict the masked word.
The second pretraining object is next-sentence prediction. The model must predict whether sentence B follows sentence A. Half of the time sentence B is the next sentence, and the other half of the time, sentence B is a random sentence. The prediction, whether it is the next sentence or not, is passed to a feedforward network with a softmax over the two classes (IsNext and NotNext).

The input embeddings are passed through multiple encoder layers to output some final hidden states.

To use the pretrained model for text classification, add a sequence classification head on top of the base BERT model. The sequence classification head is a linear layer that accepts the final hidden states and performs a linear transformation to convert them into logits. The cross-entropy loss is calculated between the logits and target to find the most likely label.
Ready to try your hand at text classification? Check out our complete text classification guide to learn how to finetune DistilBERT and use it for inference!
Token classification
To use BERT for token classification tasks like named entity recognition (NER), add a token classification head on top of the base BERT model. The token classification head is a linear layer that accepts the final hidden states and performs a linear transformation to convert them into logits. The cross-entropy loss is calculated between the logits and each token to find the most likely label.
Ready to try your hand at token classification? Check out our complete token classification guide to learn how to finetune DistilBERT and use it for inference!
Question answering
To use BERT for question answering, add a span classification head on top of the base BERT model. This linear layer accepts the final hidden states and performs a linear transformation to compute the span start and end logits corresponding to the answer. The cross-entropy loss is calculated between the logits and the label position to find the most likely span of text corresponding to the answer.
Ready to try your hand at question answering? Check out our complete question answering guide to learn how to finetune DistilBERT and use it for inference!

💡 Notice how easy it is to use BERT for different tasks once it's been pretrained. You only need to add a specific head to the pretrained model to manipulate the hidden states into your desired output!

Text generation
GPT-2 is a decoder-only model pretrained on a large amount of text. It can generate convincing (though not always true!) text given a prompt and complete other NLP tasks like question answering despite not being explicitly trained to.

GPT-2 uses byte pair encoding (BPE) to tokenize words and generate a token embedding. Positional encodings are added to the token embeddings to indicate the position of each token in the sequence. The input embeddings are passed through multiple decoder blocks to output some final hidden state. Within each decoder block, GPT-2 uses a masked self-attention layer which means GPT-2 can't attend to future tokens. It is only allowed to attend to tokens on the left. This is different from BERT's [mask] token because, in masked self-attention, an attention mask is used to set the score to 0 for future tokens.

The output from the decoder is passed to a language modeling head, which performs a linear transformation to convert the hidden states into logits. The label is the next token in the sequence, which are created by shifting the logits to the right by one. The cross-entropy loss is calculated between the shifted logits and the labels to output the next most likely token.

GPT-2's pretraining objective is based entirely on causal language modeling, predicting the next word in a sequence. This makes GPT-2 especially good at tasks that involve generating text.
Ready to try your hand at text generation? Check out our complete causal language modeling guide to learn how to finetune DistilGPT-2 and use it for inference!

For more information about text generation, check out the text generation strategies guide!

Summarization
Encoder-decoder models like BART and T5 are designed for the sequence-to-sequence pattern of a summarization task. We'll explain how BART works in this section, and then you can try finetuning T5 at the end.

BART's encoder architecture is very similar to BERT and accepts a token and positional embedding of the text. BART is pretrained by corrupting the input and then reconstructing it with the decoder. Unlike other encoders with specific corruption strategies, BART can apply any type of corruption. The text infilling corruption strategy works the best though. In text infilling, a number of text spans are replaced with a single [mask] token. This is important because the model has to predict the masked tokens, and it teaches the model to predict the number of missing tokens. The input embeddings and masked spans are passed through the encoder to output some final hidden states, but unlike BERT, BART doesn't add a final feedforward network at the end to predict a word.

The encoder's output is passed to the decoder, which must predict the masked tokens and any uncorrupted tokens from the encoder's output. This gives additional context to help the decoder restore the original text. The output from the decoder is passed to a language modeling head, which performs a linear transformation to convert the hidden states into logits. The cross-entropy loss is calculated between the logits and the label, which is just the token shifted to the right.

Ready to try your hand at summarization? Check out our complete summarization guide to learn how to finetune T5 and use it for inference!

For more information about text generation, check out the text generation strategies guide!

Translation
Translation is another example of a sequence-to-sequence task, which means you can use an encoder-decoder model like BART or T5 to do it. We'll explain how BART works in this section, and then you can try finetuning T5 at the end.
BART adapts to translation by adding a separate randomly initialized encoder to map a source language to an input that can be decoded into the target language. This new encoder's embeddings are passed to the pretrained encoder instead of the original word embeddings. The source encoder is trained by updating the source encoder, positional embeddings, and input embeddings with the cross-entropy loss from the model output. The model parameters are frozen in this first step, and all the model parameters are trained together in the second step.
BART has since been followed up by a multilingual version, mBART, intended for translation and pretrained on many different languages.
Ready to try your hand at translation? Check out our complete translation guide to learn how to finetune T5 and use it for inference!

For more information about text generation, check out the text generation strategies guide!
"
"
How to create a custom pipeline?
In this guide, we will see how to create a custom pipeline and share it on the Hub or add it to the
🤗 Transformers library.
First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes,
dictionaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as possible
as it makes compatibility easier (even through other languages via JSON). Those will be the inputs of the
pipeline (preprocess).
Then define the outputs. Same policy as the inputs. The simpler, the better. Those will be the outputs of
postprocess method.
Start by inheriting the base class Pipeline with the 4 methods needed to implement preprocess,
_forward, postprocess, and _sanitize_parameters.
thon
from transformers import Pipeline
class MyPipeline(Pipeline):
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        if ""maybe_arg"" in kwargs:
            preprocess_kwargs[""maybe_arg""] = kwargs[""maybe_arg""]
        return preprocess_kwargs, {}, {}
def preprocess(self, inputs, maybe_arg=2):
    model_input = Tensor(inputs[""input_ids""])
    return {""model_input"": model_input}

def _forward(self, model_inputs):
    # model_inputs == {""model_input"": model_input}
    outputs = self.model(**model_inputs)
    # Maybe {""logits"": Tensor()}
    return outputs

def postprocess(self, model_outputs):
    best_class = model_outputs[""logits""].softmax(-1)
    return best_class

The structure of this breakdown is to support relatively seamless support for CPU/GPU, while supporting doing
pre/postprocessing on the CPU on different threads
preprocess will take the originally defined inputs, and turn them into something feedable to the model. It might
contain more information and is usually a Dict.
_forward is the implementation detail and is not meant to be called directly. forward is the preferred
called method as it contains safeguards to make sure everything is working on the expected device. If anything is
linked to a real model it belongs in the _forward method, anything else is in the preprocess/postprocess.
postprocess methods will take the output of _forward and turn it into the final output that was decided
earlier.
_sanitize_parameters exists to allow users to pass any parameters whenever they wish, be it at initialization
time pipeline(., maybe_arg=4) or at call time pipe = pipeline(); output = pipe(., maybe_arg=4).
The returns of _sanitize_parameters are the 3 dicts of kwargs that will be passed directly to preprocess,
_forward, and postprocess. Don't fill anything if the caller didn't call with any extra parameter. That
allows to keep the default arguments in the function definition which is always more ""natural"".
A classic example would be a top_k argument in the post processing in classification tasks.
thon

pipe = pipeline(""my-new-task"")
pipe(""This is a test"")
[{""label"": ""1-star"", ""score"": 0.8}, {""label"": ""2-star"", ""score"": 0.1}, {""label"": ""3-star"", ""score"": 0.05}
{""label"": ""4-star"", ""score"": 0.025}, {""label"": ""5-star"", ""score"": 0.025}]
pipe(""This is a test"", top_k=2)
[{""label"": ""1-star"", ""score"": 0.8}, {""label"": ""2-star"", ""score"": 0.1}]

In order to achieve that, we'll update our postprocess method with a default parameter to 5. and edit
_sanitize_parameters to allow this new parameter.
thon
def postprocess(self, model_outputs, top_k=5):
    best_class = model_outputs[""logits""].softmax(-1)
    # Add logic to handle top_k
    return best_class
def _sanitize_parameters(self, **kwargs):
    preprocess_kwargs = {}
    if ""maybe_arg"" in kwargs:
        preprocess_kwargs[""maybe_arg""] = kwargs[""maybe_arg""]
postprocess_kwargs = {}
if ""top_k"" in kwargs:
    postprocess_kwargs[""top_k""] = kwargs[""top_k""]
return preprocess_kwargs, {}, postprocess_kwargs

Try to keep the inputs/outputs very simple and ideally JSON-serializable as it makes the pipeline usage very easy
without requiring users to understand new kind of objects. It's also relatively common to support many different types
of arguments for ease of use (audio files, can be filenames, URLs or pure bytes)
Adding it to the list of supported tasks
To register your new-task to the list of supported tasks, you have to add it to the PIPELINE_REGISTRY:
thon
from transformers.pipelines import PIPELINE_REGISTRY
PIPELINE_REGISTRY.register_pipeline(
    ""new-task"",
    pipeline_class=MyPipeline,
    pt_model=AutoModelForSequenceClassification,
)

You can specify a default model if you want, in which case it should come with a specific revision (which can be the name of a branch or a commit hash, here we took ""abcdef"") as well as the type:
python
PIPELINE_REGISTRY.register_pipeline(
    ""new-task"",
    pipeline_class=MyPipeline,
    pt_model=AutoModelForSequenceClassification,
    default={""pt"": (""user/awesome_model"", ""abcdef"")},
    type=""text"",  # current support type: text, audio, image, multimodal
)
Share your pipeline on the Hub
To share your custom pipeline on the Hub, you just have to save the custom code of your Pipeline subclass in a
python file. For instance, let's say we want to use a custom pipeline for sentence pair classification like this:

import numpy as np
from transformers import Pipeline
def softmax(outputs):
    maxes = np.max(outputs, axis=-1, keepdims=True)
    shifted_exp = np.exp(outputs - maxes)
    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
class PairClassificationPipeline(Pipeline):
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        if ""second_text"" in kwargs:
            preprocess_kwargs[""second_text""] = kwargs[""second_text""]
        return preprocess_kwargs, {}, {}
def preprocess(self, text, second_text=None):
    return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework)

def _forward(self, model_inputs):
    return self.model(**model_inputs)

def postprocess(self, model_outputs):
    logits = model_outputs.logits[0].numpy()
    probabilities = softmax(logits)

    best_class = np.argmax(probabilities)
    label = self.model.config.id2label[best_class]
    score = probabilities[best_class].item()
    logits = logits.tolist()
    return {""label"": label, ""score"": score, ""logits"": logits}

The implementation is framework agnostic, and will work for PyTorch and TensorFlow models. If we have saved this in
a file named pair_classification.py, we can then import it and register it like this:

from pair_classification import PairClassificationPipeline
from transformers.pipelines import PIPELINE_REGISTRY
from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
PIPELINE_REGISTRY.register_pipeline(
    ""pair-classification"",
    pipeline_class=PairClassificationPipeline,
    pt_model=AutoModelForSequenceClassification,
    tf_model=TFAutoModelForSequenceClassification,
)

Once this is done, we can use it with a pretrained model. For instance sgugger/finetuned-bert-mrpc has been
fine-tuned on the MRPC dataset, which classifies pairs of sentences as paraphrases or not.

from transformers import pipeline
classifier = pipeline(""pair-classification"", model=""sgugger/finetuned-bert-mrpc"")

Then we can share it on the Hub by using the save_pretrained method in a Repository:

from huggingface_hub import Repository
repo = Repository(""test-dynamic-pipeline"", clone_from=""{your_username}/test-dynamic-pipeline"")
classifier.save_pretrained(""test-dynamic-pipeline"")
repo.push_to_hub()

This will copy the file where you defined PairClassificationPipeline inside the folder ""test-dynamic-pipeline"",
along with saving the model and tokenizer of the pipeline, before pushing everything in the repository
{your_username}/test-dynamic-pipeline. After that anyone can use it as long as they provide the option
trust_remote_code=True:

from transformers import pipeline
classifier = pipeline(model=""{your_username}/test-dynamic-pipeline"", trust_remote_code=True)

Add the pipeline to 🤗 Transformers
If you want to contribute your pipeline to 🤗 Transformers, you will need to add a new module in the pipelines submodule
with the code of your pipeline, then add it in the list of tasks defined in pipelines/__init__.py.
Then you will need to add tests. Create a new file tests/test_pipelines_MY_PIPELINE.py with example with the other tests.
The run_pipeline_test function will be very generic and run on small random models on every possible
architecture as defined by model_mapping and tf_model_mapping.
This is very important to test future compatibility, meaning if someone adds a new model for
XXXForQuestionAnswering then the pipeline test will attempt to run on it. Because the models are random it's
impossible to check for actual values, that's why there is a helper ANY that will simply attempt to match the
output of the pipeline TYPE.
You also need to implement 2 (ideally 4) tests.

test_small_model_pt : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
  and test the pipeline outputs. The results should be the same as test_small_model_tf.
test_small_model_tf : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
  and test the pipeline outputs. The results should be the same as test_small_model_pt.
test_large_model_pt (optional): Tests the pipeline on a real pipeline where the results are supposed to
  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
  sure there is no drift in future releases.
test_large_model_tf (optional): Tests the pipeline on a real pipeline where the results are supposed to
  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
  sure there is no drift in future releases.
"
"
Agents & Tools

Transformers Agent is an experimental API which is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.

To learn more about agents and tools make sure to read the introductory guide. This page
contains the API docs for the underlying classes.
Agents
We provide three types of agents: [HfAgent] uses inference endpoints for opensource models, [LocalAgent] uses a model of your choice locally and [OpenAiAgent] uses OpenAI closed models.
HfAgent
[[autodoc]] HfAgent
LocalAgent
[[autodoc]] LocalAgent
OpenAiAgent
[[autodoc]] OpenAiAgent
AzureOpenAiAgent
[[autodoc]] AzureOpenAiAgent
Agent
[[autodoc]] Agent
    - chat
    - run
    - prepare_for_new_chat
Tools
load_tool
[[autodoc]] load_tool
Tool
[[autodoc]] Tool
PipelineTool
[[autodoc]] PipelineTool
RemoteTool
[[autodoc]] RemoteTool
launch_gradio_demo
[[autodoc]] launch_gradio_demo
Agent Types
Agents can handle any type of object in-between tools; tools, being completely multimodal, can accept and return
text, image, audio, video, among other types. In order to increase compatibility between tools, as well as to 
correctly render these returns in ipython (jupyter, colab, ipython notebooks, ), we implement wrapper classes
around these types.
The wrapped objects should continue behaving as initially; a text object should still behave as a string, an image
object should still behave as a PIL.Image.
These types have three specific purposes:

Calling to_raw on the type should return the underlying object
Calling to_string on the type should return the object as a string: that can be the string in case of an AgentText
  but will be the path of the serialized version of the object in other instances
Displaying it in an ipython kernel should display the object correctly

AgentText
[[autodoc]] transformers.tools.agent_types.AgentText
AgentImage
[[autodoc]] transformers.tools.agent_types.AgentImage
AgentAudio
[[autodoc]] transformers.tools.agent_types.AgentAudio"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate
We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:

from huggingface_hub import notebook_login
notebook_login()

Load ELI5 dataset
Start by loading a smaller subset of the r/askscience subset of the ELI5 dataset from the 🤗 Datasets library.
 This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.

from datasets import load_dataset
eli5 = load_dataset(""eli5"", split=""train_asks[:5000]"")

Split the dataset's train_asks split into a train and test set with the [~datasets.Dataset.train_test_split] method:

eli5 = eli5.train_test_split(test_size=0.2)

Then take a look at an example:

eli5[""train""][0]
{'answers': {'a_id': ['c3d1aib', 'c3d4lya'],
  'score': [6, 3],
  'text': [""The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up."",
   ""Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?""]},
 'answers_urls': {'url': []},
 'document': '',
 'q_id': 'nyxfp',
 'selftext': 'URL_0\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']},
 'subreddit': 'askscience',
 'title': 'Few questions about this space walk photograph.',
 'title_urls': {'url': []}}

While this may look like a lot, you're only really interested in the text field. What's cool about language modeling
tasks is you don't need labels (also known as an unsupervised task) because the next word is the label.
Preprocess

The next step is to load a DistilGPT2 tokenizer to process the text subfield:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""distilgpt2"")

You'll notice from the example above, the text field is actually nested inside answers. This means you'll need to
extract the text subfield from its nested structure with the flatten method:

eli5 = eli5.flatten()
eli5[""train""][0]
{'answers.a_id': ['c3d1aib', 'c3d4lya'],
 'answers.score': [6, 3],
 'answers.text': [""The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up."",
  ""Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?""],
 'answers_urls.url': [],
 'document': '',
 'q_id': 'nyxfp',
 'selftext': 'URL_0\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'],
 'subreddit': 'askscience',
 'title': 'Few questions about this space walk photograph.',
 'title_urls.url': []}

Each subfield is now a separate column as indicated by the answers prefix, and the text field is a list now. Instead
of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them.
Here is a first preprocessing function to join the list of strings for each example and tokenize the result:

def preprocess_function(examples):
     return tokenizer(["" "".join(x) for x in examples[""answers.text""]])

To apply this preprocessing function over the entire dataset, use the 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once, and increasing the number of processes with num_proc. Remove any columns you don't need:

tokenized_eli5 = eli5.map(
     preprocess_function,
     batched=True,
     num_proc=4,
     remove_columns=eli5[""train""].column_names,
 )

This dataset contains the token sequences, but some of these are longer than the maximum input length for the model.
You can now use a second preprocessing function to
- concatenate all the sequences
- split the concatenated sequences into shorter chunks defined by block_size, which should be both shorter than the maximum input length and short enough for your GPU RAM. 

block_size = 128
def group_texts(examples):
     # Concatenate all texts.
     concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
     total_length = len(concatenated_examples[list(examples.keys())[0]])
     # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
     # customize this part to your needs.
     if total_length >= block_size:
         total_length = (total_length // block_size) * block_size
     # Split by chunks of block_size.
     result = {
         k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
         for k, t in concatenated_examples.items()
     }
     result[""labels""] = result[""input_ids""].copy()
     return result

Apply the group_texts function over the entire dataset:

lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)

Now create a batch of examples using [DataCollatorForLanguageModeling]. It's more efficient to dynamically pad the
sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.

Use the end-of-sequence token as the padding token and set mlm=False. This will use the inputs as labels shifted to the right by one element:

from transformers import DataCollatorForLanguageModeling
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

Use the end-of-sequence token as the padding token and set mlm=False. This will use the inputs as labels shifted to the right by one element:

from transformers import DataCollatorForLanguageModeling
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors=""tf"")

Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial!

You're ready to start training your model now! Load DistilGPT2 with [AutoModelForCausalLM]:

from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
model = AutoModelForCausalLM.from_pretrained(""distilgpt2"")

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model).
Pass the training arguments to [Trainer] along with the model, datasets, and data collator.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_eli5_clm-model"",
     evaluation_strategy=""epoch"",
     learning_rate=2e-5,
     weight_decay=0.01,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=lm_dataset[""train""],
     eval_dataset=lm_dataset[""test""],
     data_collator=data_collator,
 )
trainer.train()

Once training is completed, use the [~transformers.Trainer.evaluate] method to evaluate your model and get its perplexity:

import math
eval_results = trainer.evaluate()
print(f""Perplexity: {math.exp(eval_results['eval_loss']):.2f}"")
Perplexity: 49.61

Then share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)

Then you can load DistilGPT2 with [TFAutoModelForCausalLM]:

from transformers import TFAutoModelForCausalLM
model = TFAutoModelForCausalLM.from_pretrained(""distilgpt2"")

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     lm_dataset[""train""],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_test_set = model.prepare_tf_dataset(
     lm_dataset[""test""],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

import tensorflow as tf
model.compile(optimizer=optimizer)  # No loss argument!

This can be done by specifying where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
callback = PushToHubCallback(
     output_dir=""my_awesome_eli5_clm-model"",
     tokenizer=tokenizer,
 )

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callback to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for causal language modeling, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with a prompt you'd like to generate text from:

prompt = ""Somatic hypermutation allows the immune system to""

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for text generation with your model, and pass your text to it:

from transformers import pipeline
generator = pipeline(""text-generation"", model=""my_awesome_eli5_clm-model"")
generator(prompt)
[{'generated_text': ""Somatic hypermutation allows the immune system to be able to effectively reverse the damage caused by an infection.\n\n\nThe damage caused by an infection is caused by the immune system's ability to perform its own self-correcting tasks.""}]

Tokenize the text and return the input_ids as PyTorch tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""my_awesome_eli5_clm-model"")
inputs = tokenizer(prompt, return_tensors=""pt"").input_ids

Use the [~transformers.generation_utils.GenerationMixin.generate] method to generate text.
For more details about the different text generation strategies and parameters for controlling generation, check out the Text generation strategies page.

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(""my_awesome_eli5_clm-model"")
outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)

Decode the generated token ids back into text:

tokenizer.batch_decode(outputs, skip_special_tokens=True)
[""Somatic hypermutation allows the immune system to react to drugs with the ability to adapt to a different environmental situation. In other words, a system of 'hypermutation' can help the immune system to adapt to a different environmental situation or in some cases even a single life. In contrast, researchers at the University of Massachusetts-Boston have found that 'hypermutation' is much stronger in mice than in humans but can be found in humans, and that it's not completely unknown to the immune system. A study on how the immune system""]
``
</pt>
<tf>
Tokenize the text and return theinput_ids` as TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""my_awesome_eli5_clm-model"")
inputs = tokenizer(prompt, return_tensors=""tf"").input_ids

Use the [~transformers.generation_tf_utils.TFGenerationMixin.generate] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the Text generation strategies page.

from transformers import TFAutoModelForCausalLM
model = TFAutoModelForCausalLM.from_pretrained(""my_awesome_eli5_clm-model"")
outputs = model.generate(input_ids=inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)

Decode the generated token ids back into text:

tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Somatic hypermutation allows the immune system to detect the presence of other viruses as they become more prevalent. Therefore, researchers have identified a high proportion of human viruses. The proportion of virus-associated viruses in our study increases with age. Therefore, we propose a simple algorithm to detect the presence of these new viruses in our samples as a sign of improved immunity. A first study based on this algorithm, which will be published in Science on Friday, aims to show that this finding could translate into the development of a better vaccine that is more effective for']

"
"
Export to TorchScript

This is the very beginning of our experiments with TorchScript and we are still
exploring its capabilities with variable-input-size models. It is a focus of interest to
us and we will deepen our analysis in upcoming releases, with more code examples, a more
flexible implementation, and benchmarks comparing Python-based codes with compiled
TorchScript.

According to the TorchScript documentation:

TorchScript is a way to create serializable and optimizable models from PyTorch code.

There are two PyTorch modules, JIT and
TRACE, that allow developers to export their
models to be reused in other programs like efficiency-oriented C++ programs.
We provide an interface that allows you to export 🤗 Transformers models to TorchScript
so they can be reused in a different environment than PyTorch-based Python programs.
Here, we explain how to export and use our models using TorchScript.
Exporting a model requires two things:

model instantiation with the torchscript flag
a forward pass with dummy inputs

These necessities imply several things developers should be careful about as detailed
below.
TorchScript flag and tied weights
The torchscript flag is necessary because most of the 🤗 Transformers language models
have tied weights between their Embedding layer and their Decoding layer.
TorchScript does not allow you to export models that have tied weights, so it is
necessary to untie and clone the weights beforehand.
Models instantiated with the torchscript flag have their Embedding layer and
Decoding layer separated, which means that they should not be trained down the line.
Training would desynchronize the two layers, leading to unexpected results.
This is not the case for models that do not have a language model head, as those do not
have tied weights. These models can be safely exported without the torchscript flag.
Dummy inputs and standard lengths
The dummy inputs are used for a models forward pass. While the inputs' values are
propagated through the layers, PyTorch keeps track of the different operations executed
on each tensor. These recorded operations are then used to create the trace of the
model.
The trace is created relative to the inputs' dimensions. It is therefore constrained by
the dimensions of the dummy input, and will not work for any other sequence length or
batch size. When trying with a different size, the following error is raised:
`The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2`
We recommended you trace the model with a dummy input size at least as large as the
largest input that will be fed to the model during inference. Padding can help fill the
missing values. However, since the model is traced with a larger input size, the
dimensions of the matrix will also be large, resulting in more calculations.
Be careful of the total number of operations done on each input and follow the
performance closely when exporting varying sequence-length models.
Using TorchScript in Python
This section demonstrates how to save and load models as well as how to use the trace
for inference.
Saving a model
To export a BertModel with TorchScript, instantiate BertModel from the BertConfig
class and then save it to disk under the filename traced_bert.pt:
thon
from transformers import BertModel, BertTokenizer, BertConfig
import torch
enc = BertTokenizer.from_pretrained(""bert-base-uncased"")
Tokenizing input text
text = ""[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]""
tokenized_text = enc.tokenize(text)
Masking one of the input tokens
masked_index = 8
tokenized_text[masked_index] = ""[MASK]""
indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
Creating a dummy input
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
dummy_input = [tokens_tensor, segments_tensors]
Initializing the model with the torchscript flag
Flag set to True even though it is not necessary as this model does not have an LM Head.
config = BertConfig(
    vocab_size_or_config_json_file=32000,
    hidden_size=768,
    num_hidden_layers=12,
    num_attention_heads=12,
    intermediate_size=3072,
    torchscript=True,
)
Instantiating the model
model = BertModel(config)
The model needs to be in evaluation mode
model.eval()
If you are instantiating the model with from_pretrained you can also easily set the TorchScript flag
model = BertModel.from_pretrained(""bert-base-uncased"", torchscript=True)
Creating the trace
traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
torch.jit.save(traced_model, ""traced_bert.pt"")

Loading a model
Now you can load the previously saved BertModel, traced_bert.pt, from disk and use
it on the previously initialised dummy_input:
thon
loaded_model = torch.jit.load(""traced_bert.pt"")
loaded_model.eval()
all_encoder_layers, pooled_output = loaded_model(*dummy_input)

Using a traced model for inference
Use the traced model for inference by using its __call__ dunder method:
python
traced_model(tokens_tensor, segments_tensors)
Deploy Hugging Face TorchScript models to AWS with the Neuron SDK
AWS introduced the Amazon EC2 Inf1
instance family for low cost, high performance machine learning inference in the cloud.
The Inf1 instances are powered by the AWS Inferentia chip, a custom-built hardware
accelerator, specializing in deep learning inferencing workloads. AWS
Neuron is the SDK for
Inferentia that supports tracing and optimizing transformers models for deployment on
Inf1. The Neuron SDK provides:

Easy-to-use API with one line of code change to trace and optimize a TorchScript
   model for inference in the cloud.
Out of the box performance optimizations for improved
   cost-performance.
Support for Hugging Face transformers models built with either
   PyTorch
   or
   TensorFlow.

Implications
Transformers models based on the BERT (Bidirectional Encoder Representations from
Transformers)
architecture, or its variants such as
distilBERT and
roBERTa run best on
Inf1 for non-generative tasks such as extractive question answering, sequence
classification, and token classification. However, text generation tasks can still be
adapted to run on Inf1 according to this AWS Neuron MarianMT
tutorial.
More information about models that can be converted out of the box on Inferentia can be
found in the Model Architecture
Fit
section of the Neuron documentation.
Dependencies
Using AWS Neuron to convert models requires a Neuron SDK
environment
which comes preconfigured on AWS Deep Learning
AMI.
Converting a model for AWS Neuron
Convert a model for AWS NEURON using the same code from Using TorchScript in
Python to trace a BertModel. Import the
torch.neuron framework extension to access the components of the Neuron SDK through a
Python API:
python
from transformers import BertModel, BertTokenizer, BertConfig
import torch
import torch.neuron
You only need to modify the following line:
diff
- torch.jit.trace(model, [tokens_tensor, segments_tensors])
+ torch.neuron.trace(model, [token_tensor, segments_tensors])
This enables the Neuron SDK to trace the model and optimize it for Inf1 instances.
To learn more about AWS Neuron SDK features, tools, example tutorials and latest
updates, please see the AWS NeuronSDK
documentation."
"
Methods and tools for efficient training on a single GPU
This guide demonstrates practical techniques that you can use to increase the efficiency of your model's training by 
optimizing memory utilization, speeding up the training, or both. If you'd like to understand how GPU is utilized during 
training, please refer to the Model training anatomy conceptual guide first. This guide 
focuses on practical techniques.  

If you have access to a machine with multiple GPUs, these approaches are still valid, plus you can leverage additional methods outlined in the multi-GPU section.

When training large models, there are two aspects that should be considered at the same time: 

Data throughput/training time
Model performance

Maximizing the throughput (samples/second) leads to lower training cost. This is generally achieved by utilizing the GPU 
as much as possible and thus filling GPU memory to its limit. If the desired batch size exceeds the limits of the GPU memory, 
the memory optimization techniques, such as gradient accumulation, can help.
However, if the preferred batch size fits into memory, there's no reason to apply memory-optimizing techniques because they can 
slow down the training. Just because one can use a large batch size, does not necessarily mean they should. As part of 
hyperparameter tuning, you should determine which batch size yields the best results and then optimize resources accordingly.
The methods and tools covered in this guide can be classified based on the effect they have on the training process:
| Method/tool                                                | Improves training speed | Optimizes memory utilization |
|:-----------------------------------------------------------|:------------------------|:-----------------------------|
| Batch size choice                    | Yes                     | Yes                          |
| Gradient accumulation            | No                      | Yes                          |
| Gradient checkpointing          | No                      | Yes                          |
| Mixed precision training      | Yes                     | (No)                         |
| Optimizer choice                      | Yes                     | Yes                          |
| Data preloading                        | Yes                     | No                           |
| DeepSpeed Zero                          | No                      | Yes                          |
| torch.compile                       | Yes                     | No                           |

Note: when using mixed precision with a small model and a large batch size, there will be some memory savings but with a 
large model and a small batch size, the memory use will be larger.

You can combine the above methods to get a cumulative effect. These techniques are available to you whether you are 
training your model with [Trainer] or writing a pure PyTorch loop, in which case you can configure these optimizations 
with 🤗 Accelerate.
If these methods do not result in sufficient gains, you can explore the following options: 
* Look into building your own custom Docker container with efficient softare prebuilds
* Consider a model that uses Mixture of Experts (MoE)
* Convert your model to BetterTransformer to leverage PyTorch native attention
Finally, if all of the above is still not enough, even after switching to a server-grade GPU like A100, consider moving 
to a multi-GPU setup. All these approaches are still valid in a multi-GPU setup, plus you can leverage additional parallelism 
techniques outlined in the multi-GPU section. 
Batch size choice
To achieve optimal performance, start by identifying the appropriate batch size. It is recommended to use batch sizes and 
input/output neuron counts that are of size 2^N. Often it's a multiple of 8, but it can be 
higher depending on the hardware being used and the model's dtype.
For reference, check out NVIDIA's recommendation for input/output neuron counts and 
batch size for 
fully connected layers (which are involved in GEMMs (General Matrix Multiplications)).
Tensor Core Requirements 
define the multiplier based on the dtype and the hardware. For instance, for fp16 data type a multiple of 8 is recommended, unless 
it's an A100 GPU, in which case use multiples of 64.
For parameters that are small, consider also Dimension Quantization Effects. 
This is where tiling happens and the right multiplier can have a significant speedup.
Gradient Accumulation
The gradient accumulation method aims to calculate gradients in smaller increments instead of computing them for the 
entire batch at once. This approach involves iteratively calculating gradients in smaller batches by performing forward 
and backward passes through the model and accumulating the gradients during the process. Once a sufficient number of 
gradients have been accumulated, the model's optimization step is executed. By employing gradient accumulation, it 
becomes possible to increase the effective batch size beyond the limitations imposed by the GPU's memory capacity. 
However, it is important to note that the additional forward and backward passes introduced by gradient accumulation can 
slow down the training process.
You can enable gradient accumulation by adding the gradient_accumulation_steps argument to  [TrainingArguments]: 
py
training_args = TrainingArguments(per_device_train_batch_size=1, gradient_accumulation_steps=4, **default_args)
In the above example, your effective batch size becomes 4. 
Alternatively, use 🤗 Accelerate to gain full control over the training loop. Find the 🤗 Accelerate example 
further down in this guide.
While it is advised to max out GPU usage as much as possible, a high number of gradient accumulation steps can 
result in a more pronounced training slowdown. Consider the following example. Let's say, the per_device_train_batch_size=4 
without gradient accumulation hits the GPU's limit. If you would like to train with batches of size 64, do not set the 
per_device_train_batch_size to 1 and gradient_accumulation_steps to 64. Instead, keep per_device_train_batch_size=4 
and set gradient_accumulation_steps=16. This results in the same effective batch size while making better use of 
the available GPU resources.
For additional information, please refer to batch size and gradient accumulation benchmarks for RTX-3090
and A100.
Gradient Checkpointing
Some large models may still face memory issues even when the batch size is set to 1 and gradient accumulation is used. 
This is because there are other components that also require memory storage.
Saving all activations from the forward pass in order to compute the gradients during the backward pass can result in 
significant memory overhead. The alternative approach of discarding the activations and recalculating them when needed 
during the backward pass, would introduce a considerable computational overhead and slow down the training process.
Gradient checkpointing offers a compromise between these two approaches and saves strategically selected activations 
throughout the computational graph so only a fraction of the activations need to be re-computed for the gradients. For 
an in-depth explanation of gradient checkpointing, refer to this great article.
To enable gradient checkpointing in the [Trainer], pass the corresponding a flag to [TrainingArguments]:
py
training_args = TrainingArguments(
    per_device_train_batch_size=1, gradient_accumulation_steps=4, gradient_checkpointing=True, **default_args
)
Alternatively, use 🤗 Accelerate - find the 🤗 Accelerate example further in this guide. 

While gradient checkpointing may improve memory efficiency, it slows training by approximately 20%.

Mixed precision training
Mixed precision training is a technique that aims to optimize the computational efficiency of training models by 
utilizing lower-precision numerical formats for certain variables. Traditionally, most models use 32-bit floating point 
precision (fp32 or float32) to represent and process variables. However, not all variables require this high precision 
level to achieve accurate results. By reducing the precision of certain variables to lower numerical formats like 16-bit 
floating point (fp16 or float16), we can speed up the computations. Because in this approach some computations are performed 
in half-precision, while some are still in full precision, the approach is called mixed precision training.
Most commonly mixed precision training is achieved by using fp16 (float16) data types, however, some GPU architectures 
(such as the Ampere architecture) offer bf16 and tf32 (CUDA internal data type) data types. Check 
out the NVIDIA Blog to learn more about 
the differences between these data types.
fp16
The main advantage of mixed precision training comes from saving the activations in half precision (fp16). 
Although the gradients are also computed in half precision they are converted back to full precision for the optimization 
step so no memory is saved here. 
While mixed precision training results in faster computations, it can also lead to more GPU memory being utilized, especially for small batch sizes.
This is because the model is now present on the GPU in both 16-bit and 32-bit precision (1.5x the original model on the GPU).
To enable mixed precision training, set the fp16 flag to True:
py
training_args = TrainingArguments(per_device_train_batch_size=4, fp16=True, **default_args)
If you prefer to use 🤗 Accelerate, find the 🤗 Accelerate example further in this guide. 
BF16
If you have access to an Ampere or newer hardware you can use bf16 for mixed precision training and evaluation. While 
bf16 has a worse precision than fp16, it has a much bigger dynamic range. In fp16 the biggest number you can have 
is 65535 and any number above that will result in an overflow. A bf16 number can be as large as 3.39e+38 (!) which 
is about the same as fp32 - because both have 8-bits used for the numerical range.
You can enable BF16 in the 🤗 Trainer with:
python
training_args = TrainingArguments(bf16=True, **default_args)
TF32
The Ampere hardware uses a magical data type called tf32. It has the same numerical range as fp32 (8-bits), but instead 
of 23 bits precision it has only 10 bits (same as fp16) and uses only 19 bits in total. It's ""magical"" in the sense that 
you can use the normal fp32 training and/or inference code and by enabling tf32 support you can get up to 3x throughput 
improvement. All you need to do is to add the following to your code:
import torch
torch.backends.cuda.matmul.allow_tf32 = True
CUDA will automatically switch to using tf32 instead of fp32 where possible, assuming that the used GPU is from the Ampere series.
According to NVIDIA research, the 
majority of machine learning training workloads show the same perplexity and convergence with tf32 training as with fp32. 
If you're already using fp16 or bf16 mixed precision it may help with the throughput as well.
You can enable this mode in the 🤗 Trainer:
python
TrainingArguments(tf32=True, **default_args)

tf32 can't be accessed directly via tensor.to(dtype=torch.tf32) because it is an internal CUDA data type. You need torch>=1.7 to use tf32 data types.

For additional information on tf32 vs other precisions, please refer to the following benchmarks: 
RTX-3090 and
A100.
Optimizer choice
The most common optimizer used to train transformer models is Adam or AdamW (Adam with weight decay). Adam achieves 
good convergence by storing the rolling average of the previous gradients; however, it adds an additional memory 
footprint of the order of the number of model parameters. To remedy this, you can use an alternative optimizer. 
For example if you have NVIDIA/apex installed, adamw_apex_fused will give you the 
fastest training experience among all supported AdamW optimizers.
[Trainer] integrates a variety of optimizers that can be used out of box: adamw_hf, adamw_torch, adamw_torch_fused, 
adamw_apex_fused, adamw_anyprecision or adafactor. More optimizers can be plugged in via a third-party implementation.
Let's take a closer look at two alternatives to AdamW optimizer - Adafactor (available in Trainer), and 8bit BNB quantized 
optimizer (third-party implementation).
For comparison, for a 3B-parameter model, like “t5-3b”: 
* A standard AdamW optimizer will need 24GB of GPU memory because it uses 8 bytes for each parameter (83 => 24GB)
* Adafactor optimizer will need more than 12GB. It uses slightly more than 4 bytes for each parameter, so 43 and then some extra.
* 8bit BNB quantized optimizer will use only (2*3) 6GB if all optimizer states are quantized.
Adafactor
Adafactor doesn't store rolling averages for each element in weight matrices. Instead, it keeps aggregated information 
(sums of rolling averages row- and column-wise), significantly reducing its footprint. However, compared to Adam, 
Adafactor may have slower convergence in certain cases.
You can switch to Adafactor by setting optim=""adafactor"" in [TrainingArguments]:
py
training_args = TrainingArguments(per_device_train_batch_size=4, optim=""adafactor"", **default_args)
Combined with other approaches (gradient accumulation, gradient checkpointing, and mixed precision training) 
you can notice up to 3x improvement while maintaining the throughput! However, as mentioned before, the convergence of 
Adafactor can be worse than Adam. 
8-bit Adam
Instead of aggregating optimizer states like Adafactor, 8-bit Adam keeps the full state and quantizes it. Quantization 
means that it stores the state with lower precision and dequantizes it only for the optimization. This is similar to the 
idea behind mixed precision training.
To use the 8-bit optimizer, you need to install it separately and then pass it as a custom optimizer to the [Trainer]. 
First, follow the installation guide in the GitHub repo to install the bitsandbytes library 
that implements the 8-bit Adam optimizer.
Next you need to initialize the optimizer. This involves two steps: 
* First, group the model's parameters into two groups - one where weight decay should be applied, and the other one where it should not. Usually, biases and layer norm parameters are not weight decayed. 
* Then do some argument housekeeping to use the same parameters as the previously used AdamW optimizer.

import bitsandbytes as bnb
from torch import nn
from transformers.trainer_pt_utils import get_parameter_names
training_args = TrainingArguments(per_device_train_batch_size=4, **default_args)
decay_parameters = get_parameter_names(model, [nn.LayerNorm])
decay_parameters = [name for name in decay_parameters if ""bias"" not in name]
optimizer_grouped_parameters = [
    {
        ""params"": [p for n, p in model.named_parameters() if n in decay_parameters],
        ""weight_decay"": training_args.weight_decay,
    },
    {
        ""params"": [p for n, p in model.named_parameters() if n not in decay_parameters],
        ""weight_decay"": 0.0,
    },
]
optimizer_kwargs = {
    ""betas"": (training_args.adam_beta1, training_args.adam_beta2),
    ""eps"": training_args.adam_epsilon,
}
optimizer_kwargs[""lr""] = training_args.learning_rate
adam_bnb_optim = bnb.optim.Adam8bit(
    optimizer_grouped_parameters,
    betas=(training_args.adam_beta1, training_args.adam_beta2),
    eps=training_args.adam_epsilon,
    lr=training_args.learning_rate,
)

To use the 8-bit optimizer with an existing pretrained model, you need to make a change to the embedding layer.
Read this issue for more information.

Finally, pass the custom optimizer as an argument to the Trainer:
py
trainer = Trainer(model=model, args=training_args, train_dataset=ds, optimizers=(adam_bnb_optim, None))
Combined with other approaches (gradient accumulation, gradient checkpointing, and mixed precision training), 
you can expect to get about a 3x memory improvement and even slightly higher throughput as using Adafactor. 
multi_tensor
pytorch-nightly introduced torch.optim._multi_tensor which should significantly speed up the optimizers for situations 
with lots of small feature tensors. It should eventually become the default, but if you want to experiment with it sooner, take a look at this GitHub issue.
Data preloading
One of the important requirements to reach great training speed is the ability to feed the GPU at the maximum speed it 
can handle. By default, everything happens in the main process, and it might not be able to read the data from disk fast 
enough, and thus create a bottleneck, leading to GPU under-utilization. Configure the following arguments to reduce the bottleneck:

DataLoader(pin_memory=True, ) - ensures the data gets preloaded into the pinned memory on CPU and typically leads to much faster transfers from CPU to GPU memory.
DataLoader(num_workers=4, ) - spawn several workers to preload data faster. During training, watch the GPU utilization stats; if it's far from 100%, experiment with increasing the number of workers. Of course, the problem could be elsewhere, so many workers won't necessarily lead to better performance.

When using [Trainer], the corresponding [TrainingArguments] are: dataloader_pin_memory (True by default), and dataloader_num_workers (defaults to 0).
DeepSpeed ZeRO
DeepSpeed is an open-source deep learning optimization library that is integrated with 🤗 Transformers and 🤗 Accelerate.
It provides a wide range of features and optimizations designed to improve the efficiency and scalability of large-scale 
deep learning training.
If your model fits onto a single GPU and you have enough space to fit a small batch size, you don't need to use DeepSpeed
as it'll only slow things down. However, if the model doesn't fit onto a single GPU or you can't fit a small batch, you can 
leverage DeepSpeed ZeRO + CPU Offload, or NVMe Offload for much larger models. In this case, you need to separately
install the library, then follow one of the guides to create a configuration file 
and launch DeepSpeed: 

For an in-depth guide on DeepSpeed integration with [Trainer], review the corresponding documentation, specifically the 
section for a single GPU. Some adjustments are required to use DeepSpeed in a notebook; please take a look at the corresponding guide.
If you prefer to use 🤗 Accelerate, refer to 🤗 Accelerate DeepSpeed guide.

Using torch.compile
PyTorch 2.0 introduced a new compile function that doesn't require any modification to existing PyTorch code but can 
optimize your code by adding a single line of code: model = torch.compile(model).
If using [Trainer], you only need to pass the torch_compile option in the [TrainingArguments]: 
python
training_args = TrainingArguments(torch_compile=True, **default_args)
torch.compile uses Python's frame evaluation API to automatically create a graph from existing PyTorch programs. After 
capturing the graph, different backends can be deployed to lower the graph to an optimized engine. 
You can find more details and benchmarks in PyTorch documentation.
torch.compile has a growing list of backends, which can be found in by calling torchdynamo.list_backends(), each of which with its optional dependencies.
Choose which backend to use by specifying it via torch_compile_backend in the [TrainingArguments].  Some of the most commonly used backends are:
Debugging backends:
* dynamo.optimize(""eager"") - Uses PyTorch to run the extracted GraphModule. This is quite useful in debugging TorchDynamo issues.
* dynamo.optimize(""aot_eager"") - Uses AotAutograd with no compiler, i.e, just using PyTorch eager for the AotAutograd's extracted forward and backward graphs. This is useful for debugging, and unlikely to give speedups.
Training & inference backends:
* dynamo.optimize(""inductor"") - Uses TorchInductor backend with AotAutograd and cudagraphs by leveraging codegened Triton kernels  Read more
* dynamo.optimize(""nvfuser"") -  nvFuser with TorchScript. Read more
* dynamo.optimize(""aot_nvfuser"") -  nvFuser with AotAutograd. Read more
* dynamo.optimize(""aot_cudagraphs"") - cudagraphs with AotAutograd. Read more
Inference-only backends:
* dynamo.optimize(""ofi"") -  Uses Torchscript optimize_for_inference.  Read more
* dynamo.optimize(""fx2trt"") -  Uses Nvidia TensorRT for inference optimizations.  Read more
* dynamo.optimize(""onnxrt"") -  Uses ONNXRT for inference on CPU/GPU.  Read more
* dynamo.optimize(""ipex"") -  Uses IPEX for inference on CPU.  Read more
For an example of using torch.compile with 🤗 Transformers, check out this blog post on fine-tuning a BERT model for Text Classification using the newest PyTorch 2.0 features
Using 🤗 Accelerate
With 🤗 Accelerate you can use the above methods while gaining full 
control over the training loop and can essentially write the loop in pure PyTorch with some minor modifications. 
Suppose you have combined the methods in the [TrainingArguments] like so:
py
training_args = TrainingArguments(
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    gradient_checkpointing=True,
    fp16=True,
    **default_args,
)
The full example training loop with 🤗 Accelerate is only a handful of lines of code long:

from accelerate import Accelerator
from torch.utils.data.dataloader import DataLoader
dataloader = DataLoader(ds, batch_size=training_args.per_device_train_batch_size)
if training_args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
accelerator = Accelerator(fp16=training_args.fp16)
model, optimizer, dataloader = accelerator.prepare(model, adam_bnb_optim, dataloader)
model.train()
for step, batch in enumerate(dataloader, start=1):
    loss = model(**batch).loss
    loss = loss / training_args.gradient_accumulation_steps
    accelerator.backward(loss)
    if step % training_args.gradient_accumulation_steps == 0:
        optimizer.step()
        optimizer.zero_grad()

First we wrap the dataset in a DataLoader. 
Then we can enable gradient checkpointing by calling the model's [~PreTrainedModel.gradient_checkpointing_enable] method. 
When we initialize the Accelerator 
we can specify if we want to use mixed precision training and it will take care of it for us in the [prepare] call. 
During the prepare 
call the dataloader will also be distributed across workers should we use multiple GPUs. We use the same 8-bit optimizer from the earlier example.
Finally, we can add the main training loop. Note that the backward call is handled by 🤗 Accelerate. We can also see
how gradient accumulation works: we normalize the loss, so we get the average at the end of accumulation and once we have 
enough steps we run the optimization. 
Implementing these optimization techniques with 🤗 Accelerate only takes a handful of lines of code and comes with the 
benefit of more flexibility in the training loop. For a full documentation of all features have a look at the 
Accelerate documentation.
Efficient Software Prebuilds
PyTorch's pip and conda builds come prebuilt with the cuda toolkit 
which is enough to run PyTorch, but it is insufficient if you need to build cuda extensions.
At times, additional efforts may be required to pre-build some components. For instance, if you're using libraries like apex that 
don't come pre-compiled. In other situations figuring out how to install the right cuda toolkit system-wide can be complicated. 
To address these scenarios PyTorch and NVIDIA released a new version of NGC docker container which already comes with 
everything prebuilt. You just need to install your programs on it, and it will run out of the box.
This approach is also useful if you want to tweak the pytorch source and/or make a new customized build.
To find the docker image version you want start with PyTorch release notes, 
choose one of the latest monthly releases. Go into the release's notes for the desired release, check that the environment's 
components are matching your needs (including NVIDIA Driver requirements!) and then at the very top of that document go 
to the corresponding NGC page. If for some reason you get lost, here is the index of all PyTorch NGC images.
Next follow the instructions to download and deploy the docker image.
Mixture of Experts
Some recent papers reported a 4-5x training speedup and a faster inference by integrating
Mixture of Experts (MoE) into the Transformer models.
Since it has been discovered that more parameters lead to better performance, this technique allows to increase the 
number of parameters by an order of magnitude without increasing training costs.
In this approach every other FFN layer is replaced with a MoE Layer which consists of many experts, with a gated function 
that trains each expert in a balanced way depending on the input token's position in a sequence.

(source: GLAM)
You can find exhaustive details and comparison tables in the papers listed at the end of this section.
The main drawback of this approach is that it requires staggering amounts of GPU memory - almost an order of magnitude 
larger than its dense equivalent. Various distillation and approaches are proposed to how to overcome the much higher memory requirements.
There is direct trade-off though, you can use just a few experts with a 2-3x smaller base model instead of dozens or 
hundreds experts leading to a 5x smaller model and thus increase the training speed moderately while increasing the 
memory requirements moderately as well.
Most related papers and implementations are built around Tensorflow/TPUs:

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
GLaM: Generalist Language Model (GLaM)

And for Pytorch DeepSpeed has built one as well: DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale, Mixture of Experts - blog posts:  1, 2 and specific deployment with large transformer-based natural language generation models: blog post, Megatron-Deepspeed branch.
Using PyTorch native attention
PyTorch 2.0 released the native torch.nn.functional.scaled_dot_product_attention (SDPA), 
that allows to use fused GPU kernels as memory-efficient attention and flash attention.
After installing the optimum package, the relevant internal modules can be 
replaced to use PyTorch's native attention with:
python
model = model.to_bettertransformer()
Once converted, train the model as usual."
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate jiwer
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

from huggingface_hub import notebook_login
notebook_login()

Load MInDS-14 dataset
Start by loading a smaller subset of the MInDS-14 dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.

from datasets import load_dataset, Audio
minds = load_dataset(""PolyAI/minds14"", name=""en-US"", split=""train[:100]"")

Split the dataset's train split into a train and test set with the [~Dataset.train_test_split] method:

minds = minds.train_test_split(test_size=0.2)

Then take a look at the dataset:

minds
DatasetDict({
    train: Dataset({
        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
        num_rows: 16
    })
    test: Dataset({
        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
        num_rows: 4
    })
})

While the dataset contains a lot of useful information, like lang_id and english_transcription, you'll focus on the audio and transcription in this guide. Remove the other columns with the [~datasets.Dataset.remove_columns] method:

minds = minds.remove_columns([""english_transcription"", ""intent_class"", ""lang_id""])

Take a look at the example again:

minds[""train""][0]
{'audio': {'array': array([-0.00024414,  0.        ,  0.        , ,  0.00024414,
          0.00024414,  0.00024414], dtype=float32),
  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
  'sampling_rate': 8000},
 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
 'transcription': ""hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing""}

There are two fields:

audio: a 1-dimensional array of the speech signal that must be called to load and resample the audio file.
transcription: the target text.

Preprocess
The next step is to load a Wav2Vec2 processor to process the audio signal:

from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained(""facebook/wav2vec2-base"")

The MInDS-14 dataset has a sampling rate of 8000kHz (you can find this information in its dataset card), which means you'll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model:

minds = minds.cast_column(""audio"", Audio(sampling_rate=16_000))
minds[""train""][0]
{'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ,
          2.78103951e-04,  2.38446111e-04,  1.18740834e-04], dtype=float32),
  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
  'sampling_rate': 16000},
 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
 'transcription': ""hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing""}

As you can see in the transcription above, the text contains a mix of upper and lowercase characters. The Wav2Vec2 tokenizer is only trained on uppercase characters so you'll need to make sure the text matches the tokenizer's vocabulary:

def uppercase(example):
     return {""transcription"": example[""transcription""].upper()}
minds = minds.map(uppercase)

Now create a preprocessing function that:

Calls the audio column to load and resample the audio file.
Extracts the input_values from the audio file and tokenize the transcription column with the processor.

def prepare_dataset(batch):
     audio = batch[""audio""]
     batch = processor(audio[""array""], sampling_rate=audio[""sampling_rate""], text=batch[""transcription""])
     batch[""input_length""] = len(batch[""input_values""][0])
     return batch

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up map by increasing the number of processes with the num_proc parameter. Remove the columns you don't need with the [~datasets.Dataset.remove_columns] method:

encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names[""train""], num_proc=4)

🤗 Transformers doesn't have a data collator for ASR, so you'll need to adapt the [DataCollatorWithPadding] to create a batch of examples. It'll also dynamically pad your text and labels to the length of the longest element in its batch (instead of the entire dataset) so they are a uniform length. While it is possible to pad your text in the tokenizer function by setting padding=True, dynamic padding is more efficient.
Unlike other data collators, this specific data collator needs to apply a different padding method to input_values and labels:

import torch
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
@dataclass
 class DataCollatorCTCWithPadding:
     processor: AutoProcessor
     padding: Union[bool, str] = ""longest""

     def call(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
         # split inputs and labels since they have to be of different lengths and need
         # different padding methods
         input_features = [{""input_values"": feature[""input_values""][0]} for feature in features]
         label_features = [{""input_ids"": feature[""labels""]} for feature in features]
         batch = self.processor.pad(input_features, padding=self.padding, return_tensors=""pt"")
         labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors=""pt"")
         # replace padding with -100 to ignore loss correctly
         labels = labels_batch[""input_ids""].masked_fill(labels_batch.attention_mask.ne(1), -100)
         batch[""labels""] = labels
         return batch

Now instantiate your DataCollatorForCTCWithPadding:

data_collator = DataCollatorCTCWithPadding(processor=processor, padding=""longest"")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the word error rate (WER) metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
wer = evaluate.load(""wer"")

Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the WER:

import numpy as np
def compute_metrics(pred):
     pred_logits = pred.predictions
     pred_ids = np.argmax(pred_logits, axis=-1)

     pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
     pred_str = processor.batch_decode(pred_ids)
     label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
     wer = wer.compute(predictions=pred_str, references=label_str)
     return {""wer"": wer}

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load Wav2Vec2 with [AutoModelForCTC]. Specify the reduction to apply with the ctc_loss_reduction parameter. It is often better to use the average instead of the default summation:

from transformers import AutoModelForCTC, TrainingArguments, Trainer
model = AutoModelForCTC.from_pretrained(
     ""facebook/wav2vec2-base"",
     ctc_loss_reduction=""mean"",
     pad_token_id=processor.tokenizer.pad_token_id,
 )

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the WER and save the training checkpoint.
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_asr_mind_model"",
     per_device_train_batch_size=8,
     gradient_accumulation_steps=2,
     learning_rate=1e-5,
     warmup_steps=500,
     max_steps=2000,
     gradient_checkpointing=True,
     fp16=True,
     group_by_length=True,
     evaluation_strategy=""steps"",
     per_device_eval_batch_size=8,
     save_steps=1000,
     eval_steps=1000,
     logging_steps=25,
     load_best_model_at_end=True,
     metric_for_best_model=""wer"",
     greater_is_better=False,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=encoded_minds[""train""],
     eval_dataset=encoded_minds[""test""],
     tokenizer=processor,
     data_collator=data_collator,
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

For a more in-depth example of how to finetune a model for automatic speech recognition, take a look at this blog post for English ASR and this post for multilingual ASR.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to!

from datasets import load_dataset, Audio
dataset = load_dataset(""PolyAI/minds14"", ""en-US"", split=""train"")
dataset = dataset.cast_column(""audio"", Audio(sampling_rate=16000))
sampling_rate = dataset.features[""audio""].sampling_rate
audio_file = dataset[0][""audio""][""path""]

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for automatic speech recognition with your model, and pass your audio file to it:

from transformers import pipeline
transcriber = pipeline(""automatic-speech-recognition"", model=""stevhliu/my_awesome_asr_minds_model"")
transcriber(audio_file)
{'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'}

The transcription is decent, but it could be better! Try finetuning your model on more examples to get even better results!

You can also manually replicate the results of the pipeline if you'd like:

Load a processor to preprocess the audio file and transcription and return the input as PyTorch tensors:

from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained(""stevhliu/my_awesome_asr_mind_model"")
inputs = processor(dataset[0][""audio""][""array""], sampling_rate=sampling_rate, return_tensors=""pt"")

Pass your inputs to the model and return the logits:

from transformers import AutoModelForCTC
model = AutoModelForCTC.from_pretrained(""stevhliu/my_awesome_asr_mind_model"")
with torch.no_grad():
     logits = model(**inputs).logits

Get the predicted input_ids with the highest probability, and use the processor to decode the predicted input_ids back into text:

import torch
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
transcription
['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER']

"
"
Efficient Training on Multiple CPUs
When training on a single CPU is too slow, we can use multiple CPUs. This guide focuses on PyTorch-based DDP enabling distributed CPU training efficiently.
Intel® oneCCL Bindings for PyTorch
Intel® oneCCL (collective communications library) is a library for efficient distributed deep learning training implementing such collectives like allreduce, allgather, alltoall. For more information on oneCCL, please refer to the oneCCL documentation and oneCCL specification.
Module oneccl_bindings_for_pytorch (torch_ccl before version 1.12)  implements PyTorch C10D ProcessGroup API and can be dynamically loaded as external ProcessGroup and only works on Linux platform now
Check more detailed information for oneccl_bind_pt.
Intel® oneCCL Bindings for PyTorch installation:
Wheel files are available for the following Python versions:
| Extension Version | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Python 3.10 |
| :---------------: | :--------: | :--------: | :--------: | :--------: | :---------: |
| 1.13.0            |            | √          | √          | √          | √           |
| 1.12.100          |            | √          | √          | √          | √           |
| 1.12.0            |            | √          | √          | √          | √           |
| 1.11.0            |            | √          | √          | √          | √           |
| 1.10.0            | √          | √          | √          | √          |             |
pip install oneccl_bind_pt=={pytorch_version} -f https://developer.intel.com/ipex-whl-stable-cpu
where {pytorch_version} should be your PyTorch version, for instance 1.13.0.
Check more approaches for oneccl_bind_pt installation.
Versions of oneCCL and PyTorch must match.

oneccl_bindings_for_pytorch 1.12.0 prebuilt wheel does not work with PyTorch 1.12.1 (it is for PyTorch 1.12.0)
PyTorch 1.12.1 should work with oneccl_bindings_for_pytorch 1.12.100

Intel® MPI library
Use this standards-based MPI implementation to deliver flexible, efficient, scalable cluster messaging on Intel® architecture. This component is part of the Intel® oneAPI HPC Toolkit.
oneccl_bindings_for_pytorch is installed along with the MPI tool set. Need to source the environment before using it.
for Intel® oneCCL >= 1.12.0
oneccl_bindings_for_pytorch_path=$(python -c ""from oneccl_bindings_for_pytorch import cwd; print(cwd)"")
source $oneccl_bindings_for_pytorch_path/env/setvars.sh
for Intel® oneCCL whose version < 1.12.0
torch_ccl_path=$(python -c ""import torch; import torch_ccl; import os;  print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))"")
source $torch_ccl_path/env/setvars.sh
IPEX installation:
IPEX provides performance optimizations for CPU training with both Float32 and BFloat16, you could refer single CPU section.
The following ""Usage in Trainer"" takes mpirun in Intel® MPI library as an example.
Usage in Trainer
To enable multi CPU distributed training in the Trainer with the ccl backend, users should add --ddp_backend ccl in the command arguments.
Let's see an example with the question-answering example
The following command enables training with 2 processes on one Xeon node, with one process running per one socket. The variables OMP_NUM_THREADS/CCL_WORKER_COUNT can be tuned for optimal performance.
shell script
 export CCL_WORKER_COUNT=1
 export MASTER_ADDR=127.0.0.1
 mpirun -n 2 -genv OMP_NUM_THREADS=23 \
 python3 run_qa.py \
 --model_name_or_path bert-large-uncased \
 --dataset_name squad \
 --do_train \
 --do_eval \
 --per_device_train_batch_size 12  \
 --learning_rate 3e-5  \
 --num_train_epochs 2  \
 --max_seq_length 384 \
 --doc_stride 128  \
 --output_dir /tmp/debug_squad/ \
 --no_cuda \
 --ddp_backend ccl \
 --use_ipex
The following command enables training with a total of four processes on two Xeons (node0 and node1, taking node0 as the main process), ppn (processes per node) is set to 2, with one process running per one socket. The variables OMP_NUM_THREADS/CCL_WORKER_COUNT can be tuned for optimal performance.
In node0, you need to create a configuration file which contains the IP addresses of each node (for example hostfile) and pass that configuration file path as an argument.
shell script
 cat hostfile
 xxx.xxx.xxx.xxx #node0 ip
 xxx.xxx.xxx.xxx #node1 ip
Now, run the following command in node0 and 4DDP will be enabled in node0 and node1 with BF16 auto mixed precision:
shell script
 export CCL_WORKER_COUNT=1
 export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip
 mpirun -f hostfile -n 4 -ppn 2 \
 -genv OMP_NUM_THREADS=23 \
 python3 run_qa.py \
 --model_name_or_path bert-large-uncased \
 --dataset_name squad \
 --do_train \
 --do_eval \
 --per_device_train_batch_size 12  \
 --learning_rate 3e-5  \
 --num_train_epochs 2  \
 --max_seq_length 384 \
 --doc_stride 128  \
 --output_dir /tmp/debug_squad/ \
 --no_cuda \
 --ddp_backend ccl \
 --use_ipex \
 --bf16"
"
Community
This page regroups resources around 🤗 Transformers developed by the community.
Community resources:
| Resource     |      Description      |      Author      |
|:----------|:-------------|------:|
| Hugging Face Transformers Glossary Flashcards | A set of flashcards based on the Transformers Docs Glossary that has been put into a form which can be easily learnt/revised using Anki  an open source, cross platform app specifically designed for long term knowledge retention. See this Introductory video on how to use the flashcards. | Darigov Research |
Community notebooks:
| Notebook     |      Description      |      Author      |      |
|:----------|:-------------|:-------------|------:|
| Fine-tune a pre-trained Transformer to generate lyrics | How to generate lyrics in the style of your favorite artist by fine-tuning a GPT-2 model |  Aleksey Korshuk |  |
| Train T5 in Tensorflow 2  | How to train T5 for any task using Tensorflow 2. This notebook demonstrates a Question & Answer task implemented in Tensorflow 2 using SQUAD | Muhammad Harris | |
| Train T5 on TPU  | How to train T5 on SQUAD with Transformers and Nlp | Suraj Patil | |
| Fine-tune T5 for Classification and Multiple Choice  | How to fine-tune T5 for classification and multiple choice tasks using a text-to-text format with PyTorch Lightning |  Suraj Patil |  |
| Fine-tune DialoGPT on New Datasets and Languages  | How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots |  Nathan Cooper |  |
| Long Sequence Modeling with Reformer  | How to train on sequences as long as 500,000 tokens with Reformer |  Patrick von Platen |   |
| Fine-tune BART for Summarization | How to fine-tune BART for summarization with fastai using blurr | Wayde Gilliam |  |
| Fine-tune a pre-trained Transformer on anyone's tweets | How to generate tweets in the style of your favorite Twitter account by fine-tuning a GPT-2 model |  Boris Dayma |  |
| Optimize 🤗 Hugging Face models with Weights & Biases | A complete tutorial showcasing W&B integration with Hugging Face | Boris Dayma |  |
| Pretrain Longformer  | How to build a ""long"" version of existing pretrained models |  Iz Beltagy |  |
| Fine-tune Longformer for QA | How to fine-tune longformer model for QA task | Suraj Patil |  |
| Evaluate Model with 🤗nlp | How to evaluate longformer on TriviaQA with nlp | Patrick von Platen |  |
| Fine-tune T5 for Sentiment Span Extraction  | How to fine-tune T5 for sentiment span extraction using a text-to-text format with PyTorch Lightning |  Lorenzo Ampil |  |
| Fine-tune DistilBert for Multiclass Classification | How to fine-tune DistilBert for multiclass classification with PyTorch | Abhishek Kumar Mishra | |
|Fine-tune BERT for Multi-label Classification|How to fine-tune BERT for multi-label classification using PyTorch|Abhishek Kumar Mishra ||
|Fine-tune T5 for Summarization|How to fine-tune T5 for summarization in PyTorch and track experiments with WandB|Abhishek Kumar Mishra ||
|Speed up Fine-Tuning in Transformers with Dynamic Padding / Bucketing|How to speed up fine-tuning by a factor of 2 using dynamic padding / bucketing|Michael Benesty ||
|Pretrain Reformer for Masked Language Modeling| How to train a Reformer model with bi-directional self-attention layers | Patrick von Platen | |
|Expand and Fine Tune Sci-BERT| How to increase vocabulary of a pretrained SciBERT model from AllenAI on the CORD dataset and pipeline it. | Tanmay Thakur | |
|Fine Tune BlenderBotSmall for Summarization using the Trainer API| How to fine tune BlenderBotSmall for summarization on a custom dataset, using the Trainer API. | Tanmay Thakur | |
|Fine-tune Electra and interpret with Integrated Gradients | How to fine-tune Electra for sentiment analysis and interpret predictions with Captum Integrated Gradients | Eliza Szczechla | |
|fine-tune a non-English GPT-2 Model with Trainer class | How to fine-tune a non-English GPT-2 Model with Trainer class | Philipp Schmid | |
|Fine-tune a DistilBERT Model for Multi Label Classification task | How to fine-tune a DistilBERT Model for Multi Label Classification task | Dhaval Taunk | |
|Fine-tune ALBERT for sentence-pair classification | How to fine-tune an ALBERT model or another BERT-based model for the sentence-pair classification task | Nadir El Manouzi | |
|Fine-tune Roberta for sentiment analysis | How to fine-tune a Roberta model for sentiment analysis | Dhaval Taunk | |
|Evaluating Question Generation Models | How accurate are the answers to questions generated by your seq2seq transformer model? | Pascal Zoleko | |
|Classify text with DistilBERT and Tensorflow | How to fine-tune DistilBERT for text classification in TensorFlow | Peter Bayerle | |
|Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail | How to warm-start a EncoderDecoderModel with a bert-base-uncased checkpoint for summarization on CNN/Dailymail | Patrick von Platen | |
|Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum | How to warm-start a shared EncoderDecoderModel with a roberta-base checkpoint for summarization on BBC/XSum | Patrick von Platen | |
|Fine-tune TAPAS on Sequential Question Answering (SQA) | How to fine-tune TapasForQuestionAnswering with a tapas-base checkpoint on the Sequential Question Answering (SQA) dataset | Niels Rogge | |
|Evaluate TAPAS on Table Fact Checking (TabFact) | How to evaluate a fine-tuned TapasForSequenceClassification with a tapas-base-finetuned-tabfact checkpoint using a combination of the 🤗 datasets and 🤗 transformers libraries | Niels Rogge | |
|Fine-tuning mBART for translation | How to fine-tune mBART using Seq2SeqTrainer for Hindi to English translation | Vasudev Gupta | |
|Fine-tune LayoutLM on FUNSD (a form understanding dataset) | How to fine-tune LayoutLMForTokenClassification on the FUNSD dataset for information extraction from scanned documents | Niels Rogge | |
|Fine-Tune DistilGPT2 and Generate Text | How to fine-tune DistilGPT2 and generate text | Aakash Tripathi | |
|Fine-Tune LED on up to 8K tokens | How to fine-tune LED on pubmed for long-range summarization | Patrick von Platen | |
|Evaluate LED on Arxiv | How to effectively evaluate LED on long-range summarization | Patrick von Platen | |
|Fine-tune LayoutLM on RVL-CDIP (a document image classification dataset) | How to fine-tune LayoutLMForSequenceClassification on the RVL-CDIP dataset for scanned document classification | Niels Rogge | |
|Wav2Vec2 CTC decoding with GPT2 adjustment | How to decode CTC sequence with language model adjustment | Eric Lam | |
|Fine-tune BART for summarization in two languages with Trainer class | How to fine-tune BART for summarization in two languages with Trainer class | Eliza Szczechla | |
|Evaluate Big Bird on Trivia QA | How to evaluate BigBird on long document question answering on Trivia QA | Patrick von Platen | |
| Create video captions using Wav2Vec2 | How to create YouTube captions from any video by transcribing the audio with Wav2Vec | Niklas Muennighoff | |
| Fine-tune the Vision Transformer on CIFAR-10 using PyTorch Lightning | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and PyTorch Lightning | Niels Rogge | |
| Fine-tune the Vision Transformer on CIFAR-10 using the 🤗 Trainer | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and the 🤗 Trainer | Niels Rogge | |
| Evaluate LUKE on Open Entity, an entity typing dataset | How to evaluate LukeForEntityClassification on the Open Entity dataset | Ikuya Yamada | |
| Evaluate LUKE on TACRED, a relation extraction dataset | How to evaluate LukeForEntityPairClassification on the TACRED dataset | Ikuya Yamada | |
| Evaluate LUKE on CoNLL-2003, an important NER benchmark | How to evaluate LukeForEntitySpanClassification on the CoNLL-2003 dataset | Ikuya Yamada | |
| Evaluate BigBird-Pegasus on PubMed dataset | How to evaluate BigBirdPegasusForConditionalGeneration on PubMed dataset | Vasudev Gupta |  |
| Speech Emotion Classification with Wav2Vec2 | How to leverage a pretrained Wav2Vec2 model for Emotion Classification on the MEGA dataset | Mehrdad Farahani |  |
| Detect objects in an image with DETR | How to use a trained DetrForObjectDetection model to detect objects in an image and visualize attention | Niels Rogge |  |
| Fine-tune DETR on a custom object detection dataset | How to fine-tune DetrForObjectDetection on a custom object detection dataset | Niels Rogge |  |
| Finetune T5 for Named Entity Recognition | How to fine-tune T5 on a Named Entity Recognition Task | Ogundepo Odunayo |  |"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate seqeval
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

from huggingface_hub import notebook_login
notebook_login()

Load WNUT 17 dataset
Start by loading the WNUT 17 dataset from the 🤗 Datasets library:

from datasets import load_dataset
wnut = load_dataset(""wnut_17"")

Then take a look at an example:

wnut[""train""][0]
{'id': '0',
 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
 'tokens': ['@paulwalk', 'It', ""'s"", 'the', 'view', 'from', 'where', 'I', ""'m"", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']
}

Each number in ner_tags represents an entity. Convert the numbers to their label names to find out what the entities are:

label_list = wnut[""train""].features[f""ner_tags""].feature.names
label_list
[
    ""O"",
    ""B-corporation"",
    ""I-corporation"",
    ""B-creative-work"",
    ""I-creative-work"",
    ""B-group"",
    ""I-group"",
    ""B-location"",
    ""I-location"",
    ""B-person"",
    ""I-person"",
    ""B-product"",
    ""I-product"",
]

The letter that prefixes each ner_tag indicates the token position of the entity:

B- indicates the beginning of an entity.
I- indicates a token is contained inside the same entity (for example, the State token is a part of an entity like
  Empire State Building).
0 indicates the token doesn't correspond to any entity.

Preprocess

The next step is to load a DistilBERT tokenizer to preprocess the tokens field:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""distilbert-base-uncased"")

As you saw in the example tokens field above, it looks like the input has already been tokenized. But the input actually hasn't been tokenized yet and you'll need to set is_split_into_words=True to tokenize the words into subwords. For example:

example = wnut[""train""][0]
tokenized_input = tokenizer(example[""tokens""], is_split_into_words=True)
tokens = tokenizer.convert_ids_to_tokens(tokenized_input[""input_ids""])
tokens
['[CLS]', '@', 'paul', '##walk', 'it', ""'"", 's', 'the', 'view', 'from', 'where', 'i', ""'"", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']

However, this adds some special tokens [CLS] and [SEP] and the subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may now be split into two subwords. You'll need to realign the tokens and labels by:

Mapping all tokens to their corresponding word with the word_ids method.
Assigning the label -100 to the special tokens [CLS] and [SEP] so they're ignored by the PyTorch loss function (see CrossEntropyLoss).
Only labeling the first token of a given word. Assign -100 to other subtokens from the same word.

Here is how you can create a function to realign the tokens and labels, and truncate sequences to be no longer than DistilBERT's maximum input length:

def tokenize_and_align_labels(examples):
     tokenized_inputs = tokenizer(examples[""tokens""], truncation=True, is_split_into_words=True)

     labels = []
     for i, label in enumerate(examples[f""ner_tags""]):
         word_ids = tokenized_inputs.word_ids(batch_index=i)  # Map tokens to their respective word.
         previous_word_idx = None
         label_ids = []
         for word_idx in word_ids:  # Set the special tokens to -100.
             if word_idx is None:
                 label_ids.append(-100)
             elif word_idx != previous_word_idx:  # Only label the first token of a given word.
                 label_ids.append(label[word_idx])
             else:
                 label_ids.append(-100)
             previous_word_idx = word_idx
         labels.append(label_ids)
     tokenized_inputs[""labels""] = labels
     return tokenized_inputs

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once:

tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)

Now create a batch of examples using [DataCollatorWithPadding]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.

from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
</pt>
<tf>py
from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors=""tf"")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the seqeval framework (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric). Seqeval actually produces several scores: precision, recall, F1, and accuracy.

import evaluate
seqeval = evaluate.load(""seqeval"")

Get the NER labels first, and then create a function that passes your true predictions and true labels to [~evaluate.EvaluationModule.compute] to calculate the scores:

import numpy as np
labels = [label_list[i] for i in example[f""ner_tags""]]
def compute_metrics(p):
     predictions, labels = p
     predictions = np.argmax(predictions, axis=2)

     true_predictions = [
         [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
         for prediction, label in zip(predictions, labels)
     ]
     true_labels = [
         [label_list[l] for (p, l) in zip(prediction, label) if l != -100]
         for prediction, label in zip(predictions, labels)
     ]
     results = seqeval.compute(predictions=true_predictions, references=true_labels)
     return {
         ""precision"": results[""overall_precision""],
         ""recall"": results[""overall_recall""],
         ""f1"": results[""overall_f1""],
         ""accuracy"": results[""overall_accuracy""],
     }

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train
Before you start training your model, create a map of the expected ids to their labels with id2label and label2id:

id2label = {
     0: ""O"",
     1: ""B-corporation"",
     2: ""I-corporation"",
     3: ""B-creative-work"",
     4: ""I-creative-work"",
     5: ""B-group"",
     6: ""I-group"",
     7: ""B-location"",
     8: ""I-location"",
     9: ""B-person"",
     10: ""I-person"",
     11: ""B-product"",
     12: ""I-product"",
 }
label2id = {
     ""O"": 0,
     ""B-corporation"": 1,
     ""I-corporation"": 2,
     ""B-creative-work"": 3,
     ""I-creative-work"": 4,
     ""B-group"": 5,
     ""I-group"": 6,
     ""B-location"": 7,
     ""I-location"": 8,
     ""B-person"": 9,
     ""I-person"": 10,
     ""B-product"": 11,
     ""I-product"": 12,
 }

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load DistilBERT with [AutoModelForTokenClassification] along with the number of expected labels, and the label mappings:

from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
model = AutoModelForTokenClassification.from_pretrained(
     ""distilbert-base-uncased"", num_labels=13, id2label=id2label, label2id=label2id
 )

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the seqeval scores and save the training checkpoint.
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_wnut_model"",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
     num_train_epochs=2,
     weight_decay=0.01,
     evaluation_strategy=""epoch"",
     save_strategy=""epoch"",
     load_best_model_at_end=True,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_wnut[""train""],
     eval_dataset=tokenized_wnut[""test""],
     tokenizer=tokenizer,
     data_collator=data_collator,
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer
batch_size = 16
num_train_epochs = 3
num_train_steps = (len(tokenized_wnut[""train""]) // batch_size) * num_train_epochs
optimizer, lr_schedule = create_optimizer(
     init_lr=2e-5,
     num_train_steps=num_train_steps,
     weight_decay_rate=0.01,
     num_warmup_steps=0,
 )

Then you can load DistilBERT with [TFAutoModelForTokenClassification] along with the number of expected labels, and the label mappings:

from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained(
     ""distilbert-base-uncased"", num_labels=13, id2label=id2label, label2id=label2id
 )

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     tokenized_wnut[""train""],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_validation_set = model.prepare_tf_dataset(
     tokenized_wnut[""validation""],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

import tensorflow as tf
model.compile(optimizer=optimizer)  # No loss argument!

The last two things to setup before you start training is to compute the seqeval scores from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks.
Pass your compute_metrics function to [~transformers.KerasMetricCallback]:

from transformers.keras_callbacks import KerasMetricCallback
metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)

Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
     output_dir=""my_awesome_wnut_model"",
     tokenizer=tokenizer,
 )

Then bundle your callbacks together:

callbacks = [metric_callback, push_to_hub_callback]

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for token classification, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Grab some text you'd like to run inference on:

text = ""The Golden State Warriors are an American professional basketball team based in San Francisco.""

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for NER with your model, and pass your text to it:

from transformers import pipeline
classifier = pipeline(""ner"", model=""stevhliu/my_awesome_wnut_model"")
classifier(text)
[{'entity': 'B-location',
  'score': 0.42658573,
  'index': 2,
  'word': 'golden',
  'start': 4,
  'end': 10},
 {'entity': 'I-location',
  'score': 0.35856336,
  'index': 3,
  'word': 'state',
  'start': 11,
  'end': 16},
 {'entity': 'B-group',
  'score': 0.3064001,
  'index': 4,
  'word': 'warriors',
  'start': 17,
  'end': 25},
 {'entity': 'B-location',
  'score': 0.65523505,
  'index': 13,
  'word': 'san',
  'start': 80,
  'end': 83},
 {'entity': 'B-location',
  'score': 0.4668663,
  'index': 14,
  'word': 'francisco',
  'start': 84,
  'end': 93}]

You can also manually replicate the results of the pipeline if you'd like:

Tokenize the text and return PyTorch tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""stevhliu/my_awesome_wnut_model"")
inputs = tokenizer(text, return_tensors=""pt"")

Pass your inputs to the model and return the logits:

from transformers import AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained(""stevhliu/my_awesome_wnut_model"")
with torch.no_grad():
     logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label:

predictions = torch.argmax(logits, dim=2)
predicted_token_class = [model.config.id2label[t.item()] for t in predictions[0]]
predicted_token_class
['O',
 'O',
 'B-location',
 'I-location',
 'B-group',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'B-location',
 'B-location',
 'O',
 'O']

Tokenize the text and return TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""stevhliu/my_awesome_wnut_model"")
inputs = tokenizer(text, return_tensors=""tf"")

Pass your inputs to the model and return the logits:

from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained(""stevhliu/my_awesome_wnut_model"")
logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label:

predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
predicted_token_class = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
predicted_token_class
['O',
 'O',
 'B-location',
 'I-location',
 'B-group',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'B-location',
 'B-location',
 'O',
 'O']

"
"
Efficient Inference on a Multiple GPUs
This document contains information on how to efficiently infer on a multiple GPUs. 

Note: A multi GPU setup can use the majority of the strategies described in the single GPU section. You must be aware of simple techniques, though, that can be used for a better usage.

BetterTransformer for faster inference
We have recently integrated BetterTransformer for faster inference on multi-GPU for text, image and audio models. Check the documentation about this integration here for more details."
"
BERTology
There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT
(that some call ""BERTology""). Some good examples of this field are:

BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
  https://arxiv.org/abs/1905.05950
Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
  Manning: https://arxiv.org/abs/1906.04341
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://arxiv.org/abs/2210.04633

In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to
help people access the inner representations, mainly adapted from the great work of Paul Michel
(https://arxiv.org/abs/1905.10650):

accessing all the hidden-states of BERT/GPT/GPT-2,
accessing all the attention weights for each head of BERT/GPT/GPT-2,
retrieving heads output values and gradients to be able to compute head importance score and prune head as explained
  in https://arxiv.org/abs/1905.10650.

To help you understand and use these features, we have added a specific example script: bertology.py while extract information and prune a model pre-trained on
GLUE."
"
Pipelines
The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of
the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
task summary for examples of use.
There are two categories of pipeline abstractions to be aware about:

The [pipeline] which is the most powerful object encapsulating all other pipelines.
Task-specific pipelines are available for audio, computer vision, natural language processing, and multimodal tasks.

The pipeline abstraction
The pipeline abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
pipeline but can provide additional quality of life.
Simple call on one item:
thon

pipe = pipeline(""text-classification"")
pipe(""This restaurant is awesome"")
[{'label': 'POSITIVE', 'score': 0.9998743534088135}]

If you want to use a specific model from the hub you can ignore the task if the model on
the hub already defines it:
thon

pipe = pipeline(model=""roberta-large-mnli"")
pipe(""This restaurant is awesome"")
[{'label': 'NEUTRAL', 'score': 0.7313136458396912}]

To call a pipeline on many items, you can call it with a list.
thon

pipe = pipeline(""text-classification"")
pipe([""This restaurant is awesome"", ""This restaurant is awful""])
[{'label': 'POSITIVE', 'score': 0.9998743534088135},
 {'label': 'NEGATIVE', 'score': 0.9996669292449951}]

To iterate over full datasets it is recommended to use a dataset directly. This means you don't need to allocate
the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
GPU. If it doesn't don't hesitate to create an issue.
thon
import datasets
from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
from tqdm.auto import tqdm
pipe = pipeline(""automatic-speech-recognition"", model=""facebook/wav2vec2-base-960h"", device=0)
dataset = datasets.load_dataset(""superb"", name=""asr"", split=""test"")
KeyDataset (only pt) will simply return the item in the dict returned by the dataset item
as we're not interested in the target part of the dataset. For sentence pair use KeyPairDataset
for out in tqdm(pipe(KeyDataset(dataset, ""file""))):
    print(out)
    # {""text"": ""NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND""}
    # {""text"": .}
    # .

For ease of use, a generator is also possible:
thon
from transformers import pipeline
pipe = pipeline(""text-classification"")
def data():
    while True:
        # This could come from a dataset, a database, a queue or HTTP request
        # in a server
        # Caveat: because this is iterative, you cannot use num_workers > 1 variable
        # to use multiple threads to preprocess data. You can still have 1 thread that
        # does the preprocessing while the main runs the big inference
        yield ""This is a test""
for out in pipe(data()):
    print(out)
    # {""text"": ""NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND""}
    # {""text"": .}
    # .

[[autodoc]] pipeline
Pipeline batching
All pipelines can use batching. This will work
whenever the pipeline uses its streaming ability (so when passing lists or Dataset or generator).
thon
from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
import datasets
dataset = datasets.load_dataset(""imdb"", name=""plain_text"", split=""unsupervised"")
pipe = pipeline(""text-classification"", device=0)
for out in pipe(KeyDataset(dataset, ""text""), batch_size=8, truncation=""only_first""):
    print(out)
    # [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
    # Exactly the same output as before, but the content are passed
    # as batches to the model

However, this is not automatically a win for performance. It can be either a 10x speedup or 5x slowdown depending
on hardware, data and the actual model being used.
Example where it's mostly a speedup:

thon
from transformers import pipeline
from torch.utils.data import Dataset
from tqdm.auto import tqdm
pipe = pipeline(""text-classification"", device=0)
class MyDataset(Dataset):
    def len(self):
        return 5000
def __getitem__(self, i):
    return ""This is a test""

dataset = MyDataset()
for batch_size in [1, 8, 64, 256]:
    print(""-"" * 30)
    print(f""Streaming batch_size={batch_size}"")
    for out in tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)):
        pass

On GTX 970

Streaming no batching
100%|██████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.52it/s]

Streaming batch_size=8
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:04<00:00, 1205.95it/s]

Streaming batch_size=64
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:02<00:00, 2478.24it/s]

Streaming batch_size=256
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:01<00:00, 2554.43it/s]
(diminishing returns, saturated the GPU)

Example where it's most a slowdown:
thon
class MyDataset(Dataset):
    def len(self):
        return 5000
def __getitem__(self, i):
    if i % 64 == 0:
        n = 100
    else:
        n = 1
    return ""This is a test"" * n

This is a occasional very long sentence compared to the other. In that case, the whole batch will need to be 400
tokens long, so the whole batch will be [64, 400] instead of [64, 4], leading to the high slowdown. Even worse, on
bigger batches, the program simply crashes.

Streaming no batching
100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 183.69it/s]

Streaming batch_size=8
100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 265.74it/s]

Streaming batch_size=64
100%|██████████████████████████████████████████████████████████████████████| 1000/1000 [00:26<00:00, 37.80it/s]

Streaming batch_size=256
  0%|                                                                                 | 0/1000 [00:00<?, ?it/s]
Traceback (most recent call last):
  File ""/home/nicolas/src/transformers/test.py"", line 42, in 
    for out in tqdm(pipe(dataset, batch_size=256), total=len(dataset)):
.
    q = q / math.sqrt(dim_per_head)  # (bs, n_heads, q_length, dim_per_head)
RuntimeError: CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 3.95 GiB total capacity; 1.72 GiB already allocated; 354.88 MiB free; 2.46 GiB reserved in total by PyTorch)

There are no good (general) solutions for this problem, and your mileage may vary depending on your use cases. Rule of
thumb:
For users, a rule of thumb is:

Measure performance on your load, with your hardware. Measure, measure, and keep measuring. Real numbers are the
  only way to go.
If you are latency constrained (live product doing inference), don't batch
If you are using CPU, don't batch.

If you are using throughput (you want to run your model on a bunch of static data), on GPU, then:

If you have no clue about the size of the sequence_length (""natural"" data), by default don't batch, measure and
    try tentatively to add it, add OOM checks to recover when it will fail (and it will at some point if you don't
    control the sequence_length.)

If your sequence_length is super regular, then batching is more likely to be VERY interesting, measure and push
    it until you get OOMs.
The larger the GPU the more likely batching is going to be more interesting
As soon as you enable batching, make sure you can handle OOMs nicely.

Pipeline chunk batching
zero-shot-classification and question-answering are slightly specific in the sense, that a single input might yield
multiple forward pass of a model. Under normal circumstances, this would yield issues with batch_size argument.
In order to circumvent this issue, both of these pipelines are a bit specific, they are ChunkPipeline instead of
regular Pipeline. In short:
python
preprocessed = pipe.preprocess(inputs)
model_outputs = pipe.forward(preprocessed)
outputs = pipe.postprocess(model_outputs)
Now becomes:
python
all_model_outputs = []
for preprocessed in pipe.preprocess(inputs):
    model_outputs = pipe.forward(preprocessed)
    all_model_outputs.append(model_outputs)
outputs = pipe.postprocess(all_model_outputs)
This should be very transparent to your code because the pipelines are used in
the same way.
This is a simplified view, since the pipeline can handle automatically the batch to ! Meaning you don't have to care
about how many forward passes you inputs are actually going to trigger, you can optimize the batch_size
independently of the inputs. The caveats from the previous section still apply.
Pipeline custom code
If you want to override a specific pipeline.
Don't hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most
cases, so transformers could maybe support your use case.
If you want to try simply you can:

Subclass your pipeline of choice

thon
class MyPipeline(TextClassificationPipeline):
    def postprocess():
        # Your code goes here
        scores = scores * 100
        # And here
my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, )
or if you use pipeline function, then:
my_pipeline = pipeline(model=""xxxx"", pipeline_class=MyPipeline)

That should enable you to do all the custom code you want.
Implementing a pipeline
Implementing a new pipeline
Audio
Pipelines available for audio tasks include the following.
AudioClassificationPipeline
[[autodoc]] AudioClassificationPipeline
    - call
    - all
AutomaticSpeechRecognitionPipeline
[[autodoc]] AutomaticSpeechRecognitionPipeline
    - call
    - all
ZeroShotAudioClassificationPipeline
[[autodoc]] ZeroShotAudioClassificationPipeline
    - call
    - all
Computer vision
Pipelines available for computer vision tasks include the following.
DepthEstimationPipeline
[[autodoc]] DepthEstimationPipeline
    - call
    - all
ImageClassificationPipeline
[[autodoc]] ImageClassificationPipeline
    - call
    - all
ImageSegmentationPipeline
[[autodoc]] ImageSegmentationPipeline
    - call
    - all
ObjectDetectionPipeline
[[autodoc]] ObjectDetectionPipeline
    - call
    - all
VideoClassificationPipeline
[[autodoc]] VideoClassificationPipeline
    - call
    - all
ZeroShotImageClassificationPipeline
[[autodoc]] ZeroShotImageClassificationPipeline
    - call
    - all
ZeroShotObjectDetectionPipeline
[[autodoc]] ZeroShotObjectDetectionPipeline
    - call
    - all
Natural Language Processing
Pipelines available for natural language processing tasks include the following.
ConversationalPipeline
[[autodoc]] Conversation
[[autodoc]] ConversationalPipeline
    - call
    - all
FillMaskPipeline
[[autodoc]] FillMaskPipeline
    - call
    - all
NerPipeline
[[autodoc]] NerPipeline
See [TokenClassificationPipeline] for all details.
QuestionAnsweringPipeline
[[autodoc]] QuestionAnsweringPipeline
    - call
    - all
SummarizationPipeline
[[autodoc]] SummarizationPipeline
    - call
    - all
TableQuestionAnsweringPipeline
[[autodoc]] TableQuestionAnsweringPipeline
    - call
TextClassificationPipeline
[[autodoc]] TextClassificationPipeline
    - call
    - all
TextGenerationPipeline
[[autodoc]] TextGenerationPipeline
    - call
    - all
Text2TextGenerationPipeline
[[autodoc]] Text2TextGenerationPipeline
    - call
    - all
TokenClassificationPipeline
[[autodoc]] TokenClassificationPipeline
    - call
    - all
TranslationPipeline
[[autodoc]] TranslationPipeline
    - call
    - all
ZeroShotClassificationPipeline
[[autodoc]] ZeroShotClassificationPipeline
    - call
    - all
Multimodal
Pipelines available for multimodal tasks include the following.
DocumentQuestionAnsweringPipeline
[[autodoc]] DocumentQuestionAnsweringPipeline
    - call
    - all
FeatureExtractionPipeline
[[autodoc]] FeatureExtractionPipeline
    - call
    - all
ImageToTextPipeline
[[autodoc]] ImageToTextPipeline
    - call
    - all
VisualQuestionAnsweringPipeline
[[autodoc]] VisualQuestionAnsweringPipeline
    - call
    - all
Parent class: Pipeline
[[autodoc]] Pipeline"
"
Testing
Let's take a look at how 🤗 Transformers models are tested and how you can write new tests and improve the existing ones.
There are 2 test suites in the repository:

tests -- tests for the general API
examples -- tests primarily for various applications that aren't part of the API

How transformers are tested

Once a PR is submitted it gets tested with 9 CircleCi jobs. Every new commit to that PR gets retested. These jobs
   are defined in this config file, so that if needed you can reproduce the same
   environment on your machine.

These CI jobs don't run @slow tests.

There are 3 jobs run by github actions:

torch hub integration: checks whether torch hub
     integration works.

self-hosted (push): runs fast tests on GPU only on commits on
     main. It only runs if a commit on main has updated the code in one of the following folders: src,
     tests, .github (to prevent running on added model cards, notebooks, etc.)

self-hosted runner: runs normal and slow tests on GPU in
     tests and examples:

RUN_SLOW=1 pytest tests/
RUN_SLOW=1 pytest examples/
The results can be observed here.
Running tests
Choosing which tests to run
This document goes into many details of how tests can be run. If after reading everything, you need even more details
you will find them here.
Here are some most useful ways of running tests.
Run all:
console
pytest
or:

make test
Note that the latter is defined as:

python -m pytest -n auto --dist=loadfile -s -v ./tests/
which tells pytest to:

run as many test processes as they are CPU cores (which could be too many if you don't have a ton of RAM!)
ensure that all tests from the same file will be run by the same test process
do not capture output
run in verbose mode

Getting the list of all tests
All tests of the test suite:

pytest --collect-only -q
All tests of a given test file:

pytest tests/test_optimization.py --collect-only -q
Run a specific test module
To run an individual test module:

pytest tests/test_logging.py
Run specific tests
Since unittest is used inside most of the tests, to run specific subtests you need to know the name of the unittest
class containing those tests. For example, it could be:

pytest tests/test_optimization.py::OptimizationTest::test_adam_w
Here:

tests/test_optimization.py - the file with tests
OptimizationTest - the name of the class
test_adam_w - the name of the specific test function

If the file contains multiple classes, you can choose to run only tests of a given class. For example:

pytest tests/test_optimization.py::OptimizationTest
will run all the tests inside that class.
As mentioned earlier you can see what tests are contained inside the OptimizationTest class by running:

pytest tests/test_optimization.py::OptimizationTest --collect-only -q
You can run tests by keyword expressions.
To run only tests whose name contains adam:

pytest -k adam tests/test_optimization.py
Logical and and or can be used to indicate whether all keywords should match or either. not can be used to
negate.
To run all tests except those whose name contains adam:

pytest -k ""not adam"" tests/test_optimization.py
And you can combine the two patterns in one:

pytest -k ""ada and not adam"" tests/test_optimization.py
For example to run both test_adafactor and test_adam_w you can use:

pytest -k ""test_adam_w or test_adam_w"" tests/test_optimization.py
Note that we use or here, since we want either of the keywords to match to include both.
If you want to include only tests that include both patterns, and is to be used:

pytest -k ""test and ada"" tests/test_optimization.py
Run accelerate tests
Sometimes you need to run accelerate tests on your models. For that you can just add -m accelerate_tests to your command, if let's say you want to run these tests on OPT run:

RUN_SLOW=1 pytest -m accelerate_tests tests/models/opt/test_modeling_opt.py
Run documentation tests
In order to test whether the documentation examples are correct, you should check that the doctests are passing. 
As an example, let's use WhisperModel.forward's docstring: 
thon 
r""""""
Returns:
Example:
    thon
    >>> import torch
    >>> from transformers import WhisperModel, WhisperFeatureExtractor
    >>> from datasets import load_dataset
>>> model = WhisperModel.from_pretrained(""openai/whisper-base"")
>>> feature_extractor = WhisperFeatureExtractor.from_pretrained(""openai/whisper-base"")
>>> ds = load_dataset(""hf-internal-testing/librispeech_asr_dummy"", ""clean"", split=""validation"")
>>> inputs = feature_extractor(ds[0][""audio""][""array""], return_tensors=""pt"")
>>> input_features = inputs.input_features
>>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
>>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 2, 512]
```""""""

Just run the following line to automatically test every docstring example in the desired file: 
 
pytest --doctest-modules <path_to_file_or_dir>
If the file has a markdown extention, you should add the --doctest-glob=""*.md"" argument.
Run only modified tests
You can run the tests related to the unstaged files or the current branch (according to Git) by using pytest-picked. This is a great way of quickly testing your changes didn't break
anything, since it won't run the tests related to files you didn't touch.

pip install pytest-picked

pytest --picked
All tests will be run from files and folders which are modified, but not yet committed.
Automatically rerun failed tests on source modification
pytest-xdist provides a very useful feature of detecting all failed
tests, and then waiting for you to modify files and continuously re-rerun those failing tests until they pass while you
fix them. So that you don't need to re start pytest after you made the fix. This is repeated until all tests pass after
which again a full run is performed.

pip install pytest-xdist
To enter the mode: pytest -f or pytest --looponfail
File changes are detected by looking at looponfailroots root directories and all of their contents (recursively).
If the default for this value does not work for you, you can change it in your project by setting a configuration
option in setup.cfg:
ini
[tool:pytest]
looponfailroots = transformers tests
or pytest.ini/tox.ini files:
ini
[pytest]
looponfailroots = transformers tests
This would lead to only looking for file changes in the respective directories, specified relatively to the ini-file’s
directory.
pytest-watch is an alternative implementation of this functionality.
Skip a test module
If you want to run all test modules, except a few you can exclude them by giving an explicit list of tests to run. For
example, to run all except test_modeling_*.py tests:

pytest *ls -1 tests/*py | grep -v test_modeling*
Clearing state
CI builds and when isolation is important (against speed), cache should be cleared:

pytest --cache-clear tests
Running tests in parallel
As mentioned earlier make test runs tests in parallel via pytest-xdist plugin (-n X argument, e.g. -n 2
to run 2 parallel jobs).
pytest-xdist's --dist= option allows one to control how the tests are grouped. --dist=loadfile puts the
tests located in one file onto the same process.
Since the order of executed tests is different and unpredictable, if running the test suite with pytest-xdist
produces failures (meaning we have some undetected coupled tests), use pytest-replay to replay the tests in the same order, which should help with then somehow
reducing that failing sequence to a minimum.
Test order and repetition
It's good to repeat the tests several times, in sequence, randomly, or in sets, to detect any potential
inter-dependency and state-related bugs (tear down). And the straightforward multiple repetition is just good to detect
some problems that get uncovered by randomness of DL.
Repeat tests

pytest-flakefinder:

pip install pytest-flakefinder
And then run every test multiple times (50 by default):

pytest --flake-finder --flake-runs=5 tests/test_failing_test.py

This plugin doesn't work with -n flag from pytest-xdist.

There is another plugin pytest-repeat, but it doesn't work with unittest.

Run tests in a random order

pip install pytest-random-order
Important: the presence of pytest-random-order will automatically randomize tests, no configuration change or
command line options is required.
As explained earlier this allows detection of coupled tests - where one test's state affects the state of another. When
pytest-random-order is installed it will print the random seed it used for that session, e.g:

pytest tests
[]
Using --random-order-bucket=module
Using --random-order-seed=573663
So that if the given particular sequence fails, you can reproduce it by adding that exact seed, e.g.:

pytest --random-order-seed=573663
[]
Using --random-order-bucket=module
Using --random-order-seed=573663
It will only reproduce the exact order if you use the exact same list of tests (or no list at all). Once you start to
manually narrowing down the list you can no longer rely on the seed, but have to list them manually in the exact order
they failed and tell pytest to not randomize them instead using --random-order-bucket=none, e.g.:

pytest --random-order-bucket=none tests/test_a.py tests/test_c.py tests/test_b.py
To disable the shuffling for all tests:

pytest --random-order-bucket=none
By default --random-order-bucket=module is implied, which will shuffle the files on the module levels. It can also
shuffle on class, package, global and none levels. For the complete details please see its
documentation.
Another randomization alternative is: pytest-randomly. This
module has a very similar functionality/interface, but it doesn't have the bucket modes available in
pytest-random-order. It has the same problem of imposing itself once installed.
Look and feel variations
pytest-sugar
pytest-sugar is a plugin that improves the look-n-feel, adds a
progressbar, and show tests that fail and the assert instantly. It gets activated automatically upon installation.

pip install pytest-sugar
To run tests without it, run:

pytest -p no:sugar
or uninstall it.
Report each sub-test name and its progress
For a single or a group of tests via pytest (after pip install pytest-pspec):

pytest --pspec tests/test_optimization.py
Instantly shows failed tests
pytest-instafail shows failures and errors instantly instead of
waiting until the end of test session.

pip install pytest-instafail

pytest --instafail
To GPU or not to GPU
On a GPU-enabled setup, to test in CPU-only mode add CUDA_VISIBLE_DEVICES="""":

CUDA_VISIBLE_DEVICES="""" pytest tests/test_logging.py
or if you have multiple gpus, you can specify which one is to be used by pytest. For example, to use only the
second gpu if you have gpus 0 and 1, you can run:

CUDA_VISIBLE_DEVICES=""1"" pytest tests/test_logging.py
This is handy when you want to run different tasks on different GPUs.
Some tests must be run on CPU-only, others on either CPU or GPU or TPU, yet others on multiple-GPUs. The following skip
decorators are used to set the requirements of tests CPU/GPU/TPU-wise:

require_torch - this test will run only under torch
require_torch_gpu - as require_torch plus requires at least 1 GPU
require_torch_multi_gpu - as require_torch plus requires at least 2 GPUs
require_torch_non_multi_gpu - as require_torch plus requires 0 or 1 GPUs
require_torch_up_to_2_gpus - as require_torch plus requires 0 or 1 or 2 GPUs
require_torch_tpu - as require_torch plus requires at least 1 TPU

Let's depict the GPU requirements in the following table:
| n gpus | decorator                      |
|--------+--------------------------------|
| >= 0 | @require_torch               |
| >= 1 | @require_torch_gpu           |
| >= 2 | @require_torch_multi_gpu     |
| < 2  | @require_torch_non_multi_gpu |
| < 3  | @require_torch_up_to_2_gpus  |
For example, here is a test that must be run only when there are 2 or more GPUs available and pytorch is installed:
python no-style
@require_torch_multi_gpu
def test_example_with_multi_gpu():
If a test requires tensorflow use the require_tf decorator. For example:
python no-style
@require_tf
def test_tf_thing_with_tensorflow():
These decorators can be stacked. For example, if a test is slow and requires at least one GPU under pytorch, here is
how to set it up:
python no-style
@require_torch_gpu
@slow
def test_example_slow_on_gpu():
Some decorators like @parametrized rewrite test names, therefore @require_* skip decorators have to be listed
last for them to work correctly. Here is an example of the correct usage:
python no-style
@parameterized.expand()
@require_torch_multi_gpu
def test_integration_foo():
This order problem doesn't exist with @pytest.mark.parametrize, you can put it first or last and it will still
work. But it only works with non-unittests.
Inside tests:

How many GPUs are available:

thon
from transformers.testing_utils import get_gpu_count
n_gpu = get_gpu_count()  # works with torch and tf

Distributed training
pytest can't deal with distributed training directly. If this is attempted - the sub-processes don't do the right
thing and end up thinking they are pytest and start running the test suite in loops. It works, however, if one
spawns a normal process that then spawns off multiple workers and manages the IO pipes.
Here are some tests that use it:

test_trainer_distributed.py
test_deepspeed.py

To jump right into the execution point, search for the execute_subprocess_async call in those tests.
You will need at least 2 GPUs to see these tests in action:

CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/test_trainer_distributed.py
Output capture
During test execution any output sent to stdout and stderr is captured. If a test or a setup method fails, its
according captured output will usually be shown along with the failure traceback.
To disable output capturing and to get the stdout and stderr normally, use -s or --capture=no:

pytest -s tests/test_logging.py
To send test results to JUnit format output:

py.test tests --junitxml=result.xml
Color control
To have no color (e.g., yellow on white background is not readable):

pytest --color=no tests/test_logging.py
Sending test report to online pastebin service
Creating a URL for each test failure:

pytest --pastebin=failed tests/test_logging.py
This will submit test run information to a remote Paste service and provide a URL for each failure. You may select
tests as usual or add for example -x if you only want to send one particular failure.
Creating a URL for a whole test session log:

pytest --pastebin=all tests/test_logging.py
Writing tests
🤗 transformers tests are based on unittest, but run by pytest, so most of the time features from both systems
can be used.
You can read here which features are supported, but the important
thing to remember is that most pytest fixtures don't work. Neither parametrization, but we use the module
parameterized that works in a similar way.
Parametrization
Often, there is a need to run the same test multiple times, but with different arguments. It could be done from within
the test, but then there is no way of running that test for just one set of arguments.
thon
test_this1.py
import unittest
from parameterized import parameterized
class TestMathUnitTest(unittest.TestCase):
    @parameterized.expand(
        [
            (""negative"", -1.5, -2.0),
            (""integer"", 1, 1.0),
            (""large fraction"", 1.6, 1),
        ]
    )
    def test_floor(self, name, input, expected):
        assert_equal(math.floor(input), expected)

Now, by default this test will be run 3 times, each time with the last 3 arguments of test_floor being assigned the
corresponding arguments in the parameter list.
and you could run just the negative and integer sets of params with:

pytest -k ""negative and integer"" tests/test_mytest.py
or all but negative sub-tests, with:

pytest -k ""not negative"" tests/test_mytest.py
Besides using the -k filter that was just mentioned, you can find out the exact name of each sub-test and run any
or all of them using their exact names.

pytest test_this1.py --collect-only -q
and it will list:

test_this1.py::TestMathUnitTest::test_floor_0_negative
test_this1.py::TestMathUnitTest::test_floor_1_integer
test_this1.py::TestMathUnitTest::test_floor_2_large_fraction
So now you can run just 2 specific sub-tests:

pytest test_this1.py::TestMathUnitTest::test_floor_0_negative  test_this1.py::TestMathUnitTest::test_floor_1_integer
The module parameterized which is already in the developer dependencies
of transformers works for both: unittests and pytest tests.
If, however, the test is not a unittest, you may use pytest.mark.parametrize (or you may see it being used in
some existing tests, mostly under examples).
Here is the same example, this time using pytest's parametrize marker:
thon
test_this2.py
import pytest
@pytest.mark.parametrize(
    ""name, input, expected"",
    [
        (""negative"", -1.5, -2.0),
        (""integer"", 1, 1.0),
        (""large fraction"", 1.6, 1),
    ],
)
def test_floor(name, input, expected):
    assert_equal(math.floor(input), expected)

Same as with parameterized, with pytest.mark.parametrize you can have a fine control over which sub-tests are
run, if the -k filter doesn't do the job. Except, this parametrization function creates a slightly different set of
names for the sub-tests. Here is what they look like:

pytest test_this2.py --collect-only -q
and it will list:

test_this2.py::test_floor[integer-1-1.0]
test_this2.py::test_floor[negative--1.5--2.0]
test_this2.py::test_floor[large fraction-1.6-1]
So now you can run just the specific test:

pytest test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[integer-1-1.0]
as in the previous example.
Files and directories
In tests often we need to know where things are relative to the current test file, and it's not trivial since the test
could be invoked from more than one directory or could reside in sub-directories with different depths. A helper class
transformers.test_utils.TestCasePlus solves this problem by sorting out all the basic paths and provides easy
accessors to them:

pathlib objects (all fully resolved):

test_file_path - the current test file path, i.e. __file__

test_file_dir - the directory containing the current test file
tests_dir - the directory of the tests test suite
examples_dir - the directory of the examples test suite
repo_root_dir - the directory of the repository

src_dir - the directory of src (i.e. where the transformers sub-dir resides)

stringified paths---same as above but these return paths as strings, rather than pathlib objects:

test_file_path_str

test_file_dir_str
tests_dir_str
examples_dir_str
repo_root_dir_str
src_dir_str

To start using those all you need is to make sure that the test resides in a subclass of
transformers.test_utils.TestCasePlus. For example:
thon
from transformers.testing_utils import TestCasePlus
class PathExampleTest(TestCasePlus):
    def test_something_involving_local_locations(self):
        data_dir = self.tests_dir / ""fixtures/tests_samples/wmt_en_ro""

If you don't need to manipulate paths via pathlib or you just need a path as a string, you can always invoked
str() on the pathlib object or use the accessors ending with _str. For example:
thon
from transformers.testing_utils import TestCasePlus
class PathExampleTest(TestCasePlus):
    def test_something_involving_stringified_locations(self):
        examples_dir = self.examples_dir_str

Temporary files and directories
Using unique temporary files and directories are essential for parallel test running, so that the tests won't overwrite
each other's data. Also we want to get the temporary files and directories removed at the end of each test that created
them. Therefore, using packages like tempfile, which address these needs is essential.
However, when debugging tests, you need to be able to see what goes into the temporary file or directory and you want
to know it's exact path and not having it randomized on every test re-run.
A helper class transformers.test_utils.TestCasePlus is best used for such purposes. It's a sub-class of
unittest.TestCase, so we can easily inherit from it in the test modules.
Here is an example of its usage:
thon
from transformers.testing_utils import TestCasePlus
class ExamplesTests(TestCasePlus):
    def test_whatever(self):
        tmp_dir = self.get_auto_remove_tmp_dir()

This code creates a unique temporary directory, and sets tmp_dir to its location.

Create a unique temporary dir:

python
def test_whatever(self):
    tmp_dir = self.get_auto_remove_tmp_dir()
tmp_dir will contain the path to the created temporary dir. It will be automatically removed at the end of the
test.

Create a temporary dir of my choice, ensure it's empty before the test starts and don't empty it after the test.

python
def test_whatever(self):
    tmp_dir = self.get_auto_remove_tmp_dir(""./xxx"")
This is useful for debug when you want to monitor a specific directory and want to make sure the previous tests didn't
leave any data in there.

You can override the default behavior by directly overriding the before and after args, leading to one of the
  following behaviors:

before=True: the temporary dir will always be cleared at the beginning of the test.

before=False: if the temporary dir already existed, any existing files will remain there.
after=True: the temporary dir will always be deleted at the end of the test.
after=False: the temporary dir will always be left intact at the end of the test.

In order to run the equivalent of rm -r safely, only subdirs of the project repository checkout are allowed if
an explicit tmp_dir is used, so that by mistake no /tmp or similar important part of the filesystem will
get nuked. i.e. please always pass paths that start with ./.

Each test can register multiple temporary directories and they all will get auto-removed, unless requested
otherwise.

Temporary sys.path override
If you need to temporary override sys.path to import from another test for example, you can use the
ExtendSysPath context manager. Example:
thon
import os
from transformers.testing_utils import ExtendSysPath
bindir = os.path.abspath(os.path.dirname(file))
with ExtendSysPath(f""{bindir}/..""):
    from test_trainer import TrainerIntegrationCommon  # noqa

Skipping tests
This is useful when a bug is found and a new test is written, yet the bug is not fixed yet. In order to be able to
commit it to the main repository we need make sure it's skipped during make test.
Methods:

A skip means that you expect your test to pass only if some conditions are met, otherwise pytest should skip
  running the test altogether. Common examples are skipping windows-only tests on non-windows platforms, or skipping
  tests that depend on an external resource which is not available at the moment (for example a database).

A xfail means that you expect a test to fail for some reason. A common example is a test for a feature not yet
  implemented, or a bug not yet fixed. When a test passes despite being expected to fail (marked with
  pytest.mark.xfail), it’s an xpass and will be reported in the test summary.

One of the important differences between the two is that skip doesn't run the test, and xfail does. So if the
code that's buggy causes some bad state that will affect other tests, do not use xfail.
Implementation

Here is how to skip whole test unconditionally:

python no-style
@unittest.skip(""this bug needs to be fixed"")
def test_feature_x():
or via pytest:
python no-style
@pytest.mark.skip(reason=""this bug needs to be fixed"")
or the xfail way:
python no-style
@pytest.mark.xfail
def test_feature_x():

Here is how to skip a test based on some internal check inside the test:

python
def test_feature_x():
    if not has_something():
        pytest.skip(""unsupported configuration"")
or the whole module:
thon
import pytest
if not pytest.config.getoption(""--custom-flag""):
    pytest.skip(""--custom-flag is missing, skipping tests"", allow_module_level=True)

or the xfail way:
python
def test_feature_x():
    pytest.xfail(""expected to fail until bug XYZ is fixed"")

Here is how to skip all tests in a module if some import is missing:

python
docutils = pytest.importorskip(""docutils"", minversion=""0.3"")

Skip a test based on a condition:

python no-style
@pytest.mark.skipif(sys.version_info < (3,6), reason=""requires python3.6 or higher"")
def test_feature_x():
or:
python no-style
@unittest.skipIf(torch_device == ""cpu"", ""Can't do half precision"")
def test_feature_x():
or skip the whole module:
python no-style
@pytest.mark.skipif(sys.platform == 'win32', reason=""does not run on windows"")
class TestClass():
    def test_feature_x(self):
More details, example and ways are here.
Slow tests
The library of tests is ever-growing, and some of the tests take minutes to run, therefore we can't afford waiting for
an hour for the test suite to complete on CI. Therefore, with some exceptions for essential tests, slow tests should be
marked as in the example below:
python no-style
from transformers.testing_utils import slow
@slow
def test_integration_foo():
Once a test is marked as @slow, to run such tests set RUN_SLOW=1 env var, e.g.:

RUN_SLOW=1 pytest tests
Some decorators like @parameterized rewrite test names, therefore @slow and the rest of the skip decorators
@require_* have to be listed last for them to work correctly. Here is an example of the correct usage:
python no-style
@parameteriz ed.expand()
@slow
def test_integration_foo():
As explained at the beginning of this document, slow tests get to run on a scheduled basis, rather than in PRs CI
checks. So it's possible that some problems will be missed during a PR submission and get merged. Such problems will
get caught during the next scheduled CI job. But it also means that it's important to run the slow tests on your
machine before submitting the PR.
Here is a rough decision making mechanism for choosing which tests should be marked as slow:
If the test is focused on one of the library's internal components (e.g., modeling files, tokenization files,
pipelines), then we should run that test in the non-slow test suite. If it's focused on an other aspect of the library,
such as the documentation or the examples, then we should run these tests in the slow test suite. And then, to refine
this approach we should have exceptions:

All tests that need to download a heavy set of weights or a dataset that is larger than ~50MB (e.g., model or
  tokenizer integration tests, pipeline integration tests) should be set to slow. If you're adding a new model, you
  should create and upload to the hub a tiny version of it (with random weights) for integration tests. This is
  discussed in the following paragraphs.
All tests that need to do a training not specifically optimized to be fast should be set to slow.
We can introduce exceptions if some of these should-be-non-slow tests are excruciatingly slow, and set them to
  @slow. Auto-modeling tests, which save and load large files to disk, are a good example of tests that are marked
  as @slow.
If a test completes under 1 second on CI (including downloads if any) then it should be a normal test regardless.

Collectively, all the non-slow tests need to cover entirely the different internals, while remaining fast. For example,
a significant coverage can be achieved by testing with specially created tiny models with random weights. Such models
have the very minimal number of layers (e.g., 2), vocab size (e.g., 1000), etc. Then the @slow tests can use large
slow models to do qualitative testing. To see the use of these simply look for tiny models with:

grep tiny tests examples
Here is a an example of a script that created the tiny model
stas/tiny-wmt19-en-de. You can easily adjust it to your specific
model's architecture.
It's easy to measure the run-time incorrectly if for example there is an overheard of downloading a huge model, but if
you test it locally the downloaded files would be cached and thus the download time not measured. Hence check the
execution speed report in CI logs instead (the output of pytest --durations=0 tests).
That report is also useful to find slow outliers that aren't marked as such, or which need to be re-written to be fast.
If you notice that the test suite starts getting slow on CI, the top listing of this report will show the slowest
tests.
Testing the stdout/stderr output
In order to test functions that write to stdout and/or stderr, the test can access those streams using the
pytest's capsys system. Here is how this is accomplished:
thon
import sys
def print_to_stdout(s):
    print(s)
def print_to_stderr(s):
    sys.stderr.write(s)
def test_result_and_stdout(capsys):
    msg = ""Hello""
    print_to_stdout(msg)
    print_to_stderr(msg)
    out, err = capsys.readouterr()  # consume the captured output streams
    # optional: if you want to replay the consumed streams:
    sys.stdout.write(out)
    sys.stderr.write(err)
    # test:
    assert msg in out
    assert msg in err

And, of course, most of the time, stderr will come as a part of an exception, so try/except has to be used in such
a case:
thon
def raise_exception(msg):
    raise ValueError(msg)
def test_something_exception():
    msg = ""Not a good value""
    error = """"
    try:
        raise_exception(msg)
    except Exception as e:
        error = str(e)
        assert msg in error, f""{msg} is in the exception:\n{error}""

Another approach to capturing stdout is via contextlib.redirect_stdout:
thon
from io import StringIO
from contextlib import redirect_stdout
def print_to_stdout(s):
    print(s)
def test_result_and_stdout():
    msg = ""Hello""
    buffer = StringIO()
    with redirect_stdout(buffer):
        print_to_stdout(msg)
    out = buffer.getvalue()
    # optional: if you want to replay the consumed streams:
    sys.stdout.write(out)
    # test:
    assert msg in out

An important potential issue with capturing stdout is that it may contain \r characters that in normal print
reset everything that has been printed so far. There is no problem with pytest, but with pytest -s these
characters get included in the buffer, so to be able to have the test run with and without -s, you have to make an
extra cleanup to the captured output, using re.sub(r'~.*\r', '', buf, 0, re.M).
But, then we have a helper context manager wrapper to automatically take care of it all, regardless of whether it has
some \r's in it or not, so it's a simple:
thon
from transformers.testing_utils import CaptureStdout
with CaptureStdout() as cs:
    function_that_writes_to_stdout()
print(cs.out)

Here is a full test example:
thon
from transformers.testing_utils import CaptureStdout
msg = ""Secret message\r""
final = ""Hello World""
with CaptureStdout() as cs:
    print(msg + final)
assert cs.out == final + ""\n"", f""captured: {cs.out}, expecting {final}""

If you'd like to capture stderr use the CaptureStderr class instead:
thon
from transformers.testing_utils import CaptureStderr
with CaptureStderr() as cs:
    function_that_writes_to_stderr()
print(cs.err)

If you need to capture both streams at once, use the parent CaptureStd class:
thon
from transformers.testing_utils import CaptureStd
with CaptureStd() as cs:
    function_that_writes_to_stdout_and_stderr()
print(cs.err, cs.out)

Also, to aid debugging test issues, by default these context managers automatically replay the captured streams on exit
from the context.
Capturing logger stream
If you need to validate the output of a logger, you can use CaptureLogger:
thon
from transformers import logging
from transformers.testing_utils import CaptureLogger
msg = ""Testing 1, 2, 3""
logging.set_verbosity_info()
logger = logging.get_logger(""transformers.models.bart.tokenization_bart"")
with CaptureLogger(logger) as cl:
    logger.info(msg)
assert cl.out, msg + ""\n""

Testing with environment variables
If you want to test the impact of environment variables for a specific test you can use a helper decorator
transformers.testing_utils.mockenv
thon
from transformers.testing_utils import mockenv
class HfArgumentParserTest(unittest.TestCase):
    @mockenv(TRANSFORMERS_VERBOSITY=""error"")
    def test_env_override(self):
        env_level_str = os.getenv(""TRANSFORMERS_VERBOSITY"", None)

At times an external program needs to be called, which requires setting PYTHONPATH in os.environ to include
multiple local paths. A helper class transformers.test_utils.TestCasePlus comes to help:
thon
from transformers.testing_utils import TestCasePlus
class EnvExampleTest(TestCasePlus):
    def test_external_prog(self):
        env = self.get_env()
        # now call the external program, passing env to it

Depending on whether the test file was under the tests test suite or examples it'll correctly set up
env[PYTHONPATH] to include one of these two directories, and also the src directory to ensure the testing is
done against the current repo, and finally with whatever env[PYTHONPATH] was already set to before the test was
called if anything.
This helper method creates a copy of the os.environ object, so the original remains intact.
Getting reproducible results
In some situations you may want to remove randomness for your tests. To get identical reproducible results set, you
will need to fix the seed:
thon
seed = 42
python RNG
import random
random.seed(seed)
pytorch RNGs
import torch
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(seed)
numpy RNG
import numpy as np
np.random.seed(seed)
tf RNG
tf.random.set_seed(seed)

Debugging tests
To start a debugger at the point of the warning, do this:

pytest tests/test_logging.py -W error::UserWarning --pdb
Working with github actions workflows
To trigger a self-push workflow CI job, you must:

Create a new branch on transformers origin (not a fork!).
The branch name has to start with either ci_ or ci- (main triggers it too, but we can't do PRs on
   main). It also gets triggered only for specific paths - you can find the up-to-date definition in case it
   changed since this document has been written here under push:
Create a PR from this branch.
Then you can see the job appear here. It may not run right away if there
   is a backlog.

Testing Experimental CI Features
Testing CI features can be potentially problematic as it can interfere with the normal CI functioning. Therefore if a
new CI feature is to be added, it should be done as following.

Create a new dedicated job that tests what needs to be tested
The new job must always succeed so that it gives us a green ✓ (details below).
Let it run for some days to see that a variety of different PR types get to run on it (user fork branches,
   non-forked branches, branches originating from github.com UI direct file edit, various forced pushes, etc. - there
   are so many) while monitoring the experimental job's logs (not the overall job green as it's purposefully always
   green)
When it's clear that everything is solid, then merge the new changes into existing jobs.

That way experiments on CI functionality itself won't interfere with the normal workflow.
Now how can we make the job always succeed while the new CI feature is being developed?
Some CIs, like TravisCI support ignore-step-failure and will report the overall job as successful, but CircleCI and
Github Actions as of this writing don't support that.
So the following workaround can be used:

set +euo pipefail at the beginning of the run command to suppress most potential failures in the bash script.
the last command must be a success: echo ""done"" or just true will do

Here is an example:
yaml
- run:
    name: run CI experiment
    command: |
        set +euo pipefail
        echo ""setting run-all-despite-any-errors-mode""
        this_command_will_fail
        echo ""but bash continues to run""
        # emulate another failure
        false
        # but the last command must be a success
        echo ""during experiment do not remove: reporting success to CI, even if there were failures""
For simple commands you could also do:

cmd_that_may_fail || true
Of course, once satisfied with the results, integrate the experimental step or job with the rest of the normal jobs,
while removing set +euo pipefail or any other things you may have added to ensure that the experimental job doesn't
interfere with the normal CI functioning.
This whole process would have been much easier if we only could set something like allow-failure for the
experimental step, and let it fail without impacting the overall status of PRs. But as mentioned earlier CircleCI and
Github Actions don't support it at the moment.
You can vote for this feature and see where it is at these CI-specific threads:

Github Actions:
CircleCI:
"
"
Configuration
The base class [PretrainedConfig] implements the common methods for loading/saving a configuration
either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
from HuggingFace's AWS S3 repository).
Each derived config class implements model specific attributes. Common attributes present in all config classes are:
hidden_size, num_attention_heads, and num_hidden_layers. Text models further implement:
vocab_size.
PretrainedConfig
[[autodoc]] PretrainedConfig
    - push_to_hub
    - all"
""
"
Utilities for Tokenizers
This page lists all the utility functions used by the tokenizers, mainly the class
[~tokenization_utils_base.PreTrainedTokenizerBase] that implements the common methods between
[PreTrainedTokenizer] and [PreTrainedTokenizerFast] and the mixin
[~tokenization_utils_base.SpecialTokensMixin].
Most of those are only useful if you are studying the code of the tokenizers in the library.
PreTrainedTokenizerBase
[[autodoc]] tokenization_utils_base.PreTrainedTokenizerBase
    - call
    - all
SpecialTokensMixin
[[autodoc]] tokenization_utils_base.SpecialTokensMixin
Enums and namedtuples
[[autodoc]] tokenization_utils_base.TruncationStrategy
[[autodoc]] tokenization_utils_base.CharSpan
[[autodoc]] tokenization_utils_base.TokenSpan"
"
Create a custom architecture
An AutoClass automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an AutoClass to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a custom 🤗 Transformers model from just a few base classes. This could be particularly useful for anyone who is interested in studying, training or experimenting with a 🤗 Transformers model. In this guide, dive deeper into creating a custom model without an AutoClass. Learn how to:

Load and customize a model configuration.
Create a model architecture.
Create a slow and fast tokenizer for text.
Create an image processor for vision tasks.
Create a feature extractor for audio tasks.
Create a processor for multimodal tasks.

Configuration
A configuration refers to a model's specific attributes. Each model configuration has different attributes; for instance, all NLP models have the hidden_size, num_attention_heads, num_hidden_layers and vocab_size attributes in common. These attributes specify the number of attention heads or hidden layers to construct a model with.
Get a closer look at DistilBERT by accessing [DistilBertConfig] to inspect it's attributes:

from transformers import DistilBertConfig
config = DistilBertConfig()
print(config)
DistilBertConfig {
  ""activation"": ""gelu"",
  ""attention_dropout"": 0.1,
  ""dim"": 768,
  ""dropout"": 0.1,
  ""hidden_dim"": 3072,
  ""initializer_range"": 0.02,
  ""max_position_embeddings"": 512,
  ""model_type"": ""distilbert"",
  ""n_heads"": 12,
  ""n_layers"": 6,
  ""pad_token_id"": 0,
  ""qa_dropout"": 0.1,
  ""seq_classif_dropout"": 0.2,
  ""sinusoidal_pos_embds"": false,
  ""transformers_version"": ""4.16.2"",
  ""vocab_size"": 30522
}

[DistilBertConfig] displays all the default attributes used to build a base [DistilBertModel]. All attributes are customizable, creating space for experimentation. For example, you can customize a default model to:

Try a different activation function with the activation parameter.
Use a higher dropout ratio for the attention probabilities with the attention_dropout parameter.

my_config = DistilBertConfig(activation=""relu"", attention_dropout=0.4)
print(my_config)
DistilBertConfig {
  ""activation"": ""relu"",
  ""attention_dropout"": 0.4,
  ""dim"": 768,
  ""dropout"": 0.1,
  ""hidden_dim"": 3072,
  ""initializer_range"": 0.02,
  ""max_position_embeddings"": 512,
  ""model_type"": ""distilbert"",
  ""n_heads"": 12,
  ""n_layers"": 6,
  ""pad_token_id"": 0,
  ""qa_dropout"": 0.1,
  ""seq_classif_dropout"": 0.2,
  ""sinusoidal_pos_embds"": false,
  ""transformers_version"": ""4.16.2"",
  ""vocab_size"": 30522
}

Pretrained model attributes can be modified in the [~PretrainedConfig.from_pretrained] function:

my_config = DistilBertConfig.from_pretrained(""distilbert-base-uncased"", activation=""relu"", attention_dropout=0.4)

Once you are satisfied with your model configuration, you can save it with [~PretrainedConfig.save_pretrained]. Your configuration file is stored as a JSON file in the specified save directory:

my_config.save_pretrained(save_directory=""./your_model_save_path"")

To reuse the configuration file, load it with [~PretrainedConfig.from_pretrained]:

my_config = DistilBertConfig.from_pretrained(""./your_model_save_path/config.json"")

You can also save your configuration file as a dictionary or even just the difference between your custom configuration attributes and the default configuration attributes! See the configuration documentation for more details.

Model
The next step is to create a model. The model - also loosely referred to as the architecture - defines what each layer is doing and what operations are happening. Attributes like num_hidden_layers from the configuration are used to define the architecture. Every model shares the base class [PreTrainedModel] and a few common methods like resizing input embeddings and pruning self-attention heads. In addition, all models are also either a torch.nn.Module, tf.keras.Model or flax.linen.Module subclass. This means models are compatible with each of their respective framework's usage.

Load your custom configuration attributes into the model:

from transformers import DistilBertModel
my_config = DistilBertConfig.from_pretrained(""./your_model_save_path/config.json"")
model = DistilBertModel(my_config)

This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.
Create a pretrained model with [~PreTrainedModel.from_pretrained]:

model = DistilBertModel.from_pretrained(""distilbert-base-uncased"")

When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:

model = DistilBertModel.from_pretrained(""distilbert-base-uncased"", config=my_config)

Load your custom configuration attributes into the model:

from transformers import TFDistilBertModel
my_config = DistilBertConfig.from_pretrained(""./your_model_save_path/my_config.json"")
tf_model = TFDistilBertModel(my_config)

This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.
Create a pretrained model with [~TFPreTrainedModel.from_pretrained]:

tf_model = TFDistilBertModel.from_pretrained(""distilbert-base-uncased"")

When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:

tf_model = TFDistilBertModel.from_pretrained(""distilbert-base-uncased"", config=my_config)

Model heads
At this point, you have a base DistilBERT model which outputs the hidden states. The hidden states are passed as inputs to a model head to produce the final output. 🤗 Transformers provides a different model head for each task as long as a model supports the task (i.e., you can't use DistilBERT for a sequence-to-sequence task like translation).

For example, [DistilBertForSequenceClassification] is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs.

from transformers import DistilBertForSequenceClassification
model = DistilBertForSequenceClassification.from_pretrained(""distilbert-base-uncased"")

Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [DistilBertForQuestionAnswering] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.

from transformers import DistilBertForQuestionAnswering
model = DistilBertForQuestionAnswering.from_pretrained(""distilbert-base-uncased"")
``
</pt>
<tf>
For example, [TFDistilBertForSequenceClassification`] is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs.

from transformers import TFDistilBertForSequenceClassification
tf_model = TFDistilBertForSequenceClassification.from_pretrained(""distilbert-base-uncased"")

Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [TFDistilBertForQuestionAnswering] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.

from transformers import TFDistilBertForQuestionAnswering
tf_model = TFDistilBertForQuestionAnswering.from_pretrained(""distilbert-base-uncased"")

Tokenizer
The last base class you need before using a model for textual data is a tokenizer to convert raw text to tensors. There are two types of tokenizers you can use with 🤗 Transformers:

[PreTrainedTokenizer]: a Python implementation of a tokenizer.
[PreTrainedTokenizerFast]: a tokenizer from our Rust-based 🤗 Tokenizer library. This tokenizer type is significantly faster - especially during batch tokenization - due to it's Rust implementation. The fast tokenizer also offers additional methods like offset mapping which maps tokens to their original words or characters.

Both tokenizers support common methods such as encoding and decoding, adding new tokens, and managing special tokens.

Not every model supports a fast tokenizer. Take a look at this table to check if a model has fast tokenizer support.

If you trained your own tokenizer, you can create one from your vocabulary file:

from transformers import DistilBertTokenizer
my_tokenizer = DistilBertTokenizer(vocab_file=""my_vocab_file.txt"", do_lower_case=False, padding_side=""left"")

It is important to remember the vocabulary from a custom tokenizer will be different from the vocabulary generated by a pretrained model's tokenizer. You need to use a pretrained model's vocabulary if you are using a pretrained model, otherwise the inputs won't make sense. Create a tokenizer with a pretrained model's vocabulary with the [DistilBertTokenizer] class:

from transformers import DistilBertTokenizer
slow_tokenizer = DistilBertTokenizer.from_pretrained(""distilbert-base-uncased"")

Create a fast tokenizer with the [DistilBertTokenizerFast] class:

from transformers import DistilBertTokenizerFast
fast_tokenizer = DistilBertTokenizerFast.from_pretrained(""distilbert-base-uncased"")

By default, [AutoTokenizer] will try to load a fast tokenizer. You can disable this behavior by setting use_fast=False in from_pretrained.

Image Processor
An image processor processes vision inputs. It inherits from the base [~image_processing_utils.ImageProcessingMixin] class.
To use, create an image processor associated with the model you're using. For example, create a default [ViTImageProcessor] if you are using ViT for image classification:

from transformers import ViTImageProcessor
vit_extractor = ViTImageProcessor()
print(vit_extractor)
ViTImageProcessor {
  ""do_normalize"": true,
  ""do_resize"": true,
  ""image_processor_type"": ""ViTImageProcessor"",
  ""image_mean"": [
    0.5,
    0.5,
    0.5
  ],
  ""image_std"": [
    0.5,
    0.5,
    0.5
  ],
  ""resample"": 2,
  ""size"": 224
}

If you aren't looking for any customization, just use the from_pretrained method to load a model's default image processor parameters.

Modify any of the [ViTImageProcessor] parameters to create your custom image processor:

from transformers import ViTImageProcessor
my_vit_extractor = ViTImageProcessor(resample=""PIL.Image.BOX"", do_normalize=False, image_mean=[0.3, 0.3, 0.3])
print(my_vit_extractor)
ViTImageProcessor {
  ""do_normalize"": false,
  ""do_resize"": true,
  ""image_processor_type"": ""ViTImageProcessor"",
  ""image_mean"": [
    0.3,
    0.3,
    0.3
  ],
  ""image_std"": [
    0.5,
    0.5,
    0.5
  ],
  ""resample"": ""PIL.Image.BOX"",
  ""size"": 224
}

Feature Extractor
A feature extractor processes audio inputs. It inherits from the base [~feature_extraction_utils.FeatureExtractionMixin] class, and may also inherit from the [SequenceFeatureExtractor] class for processing audio inputs.
To use, create a feature extractor associated with the model you're using. For example, create a default [Wav2Vec2FeatureExtractor] if you are using Wav2Vec2 for audio classification:

from transformers import Wav2Vec2FeatureExtractor
w2v2_extractor = Wav2Vec2FeatureExtractor()
print(w2v2_extractor)
Wav2Vec2FeatureExtractor {
  ""do_normalize"": true,
  ""feature_extractor_type"": ""Wav2Vec2FeatureExtractor"",
  ""feature_size"": 1,
  ""padding_side"": ""right"",
  ""padding_value"": 0.0,
  ""return_attention_mask"": false,
  ""sampling_rate"": 16000
}

If you aren't looking for any customization, just use the from_pretrained method to load a model's default feature extractor parameters.

Modify any of the [Wav2Vec2FeatureExtractor] parameters to create your custom feature extractor:

from transformers import Wav2Vec2FeatureExtractor
w2v2_extractor = Wav2Vec2FeatureExtractor(sampling_rate=8000, do_normalize=False)
print(w2v2_extractor)
Wav2Vec2FeatureExtractor {
  ""do_normalize"": false,
  ""feature_extractor_type"": ""Wav2Vec2FeatureExtractor"",
  ""feature_size"": 1,
  ""padding_side"": ""right"",
  ""padding_value"": 0.0,
  ""return_attention_mask"": false,
  ""sampling_rate"": 8000
}

Processor
For models that support multimodal tasks, 🤗 Transformers offers a processor class that conveniently wraps processing classes such as a feature extractor and a tokenizer into a single object. For example, let's use the [Wav2Vec2Processor] for an automatic speech recognition task (ASR). ASR transcribes audio to text, so you will need a feature extractor and a tokenizer.
Create a feature extractor to handle the audio inputs:

from transformers import Wav2Vec2FeatureExtractor
feature_extractor = Wav2Vec2FeatureExtractor(padding_value=1.0, do_normalize=True)

Create a tokenizer to handle the text inputs:

from transformers import Wav2Vec2CTCTokenizer
tokenizer = Wav2Vec2CTCTokenizer(vocab_file=""my_vocab_file.txt"")

Combine the feature extractor and tokenizer in [Wav2Vec2Processor]:

from transformers import Wav2Vec2Processor
processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)

With two basic classes - configuration and model - and an additional preprocessing class (tokenizer, image processor, feature extractor, or processor), you can create any of the models supported by 🤗 Transformers. Each of these base classes are configurable, allowing you to use the specific attributes you want. You can easily setup a model for training or modify an existing pretrained model to fine-tune."
"
Debugging
Multi-GPU Network Issues Debug
When training or inferencing with DistributedDataParallel and multiple GPU, if you run into issue of inter-communication between processes and/or nodes, you can use the following script to diagnose network issues.

wget https://raw.githubusercontent.com/huggingface/transformers/main/scripts/distributed/torch-distributed-gpu-test.py
For example to test how 2 GPUs interact do:

python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
If both processes can talk to each and allocate GPU memory each will print an OK status.
For more GPUs or nodes adjust the arguments in the script.
You will find a lot more details inside the diagnostics script and even a recipe to how you could run it in a SLURM environment.
An additional level of debug is to add NCCL_DEBUG=INFO environment variable as follows:

NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
This will dump a lot of NCCL-related debug information, which you can then search online if you find that some problems are reported. Or if you're not sure how to interpret the output you can share the log file in an Issue.
Underflow and Overflow Detection

This feature is currently available for PyTorch-only.

For multi-GPU training it requires DDP (torch.distributed.launch).

This feature can be used with any nn.Module-based model.

If you start getting loss=NaN or the model inhibits some other abnormal behavior due to inf or nan in
activations or weights one needs to discover where the first underflow or overflow happens and what led to it. Luckily
you can accomplish that easily by activating a special module that will do the detection automatically.
If you're using [Trainer], you just need to add:

--debug underflow_overflow
to the normal command line arguments, or pass debug=""underflow_overflow"" when creating the
[TrainingArguments] object.
If you're using your own training loop or another Trainer you can accomplish the same with:
thon
from transformers.debug_utils import DebugUnderflowOverflow
debug_overflow = DebugUnderflowOverflow(model)

[~debug_utils.DebugUnderflowOverflow] inserts hooks into the model that immediately after each
forward call will test input and output variables and also the corresponding module's weights. As soon as inf or
nan is detected in at least one element of the activations or weights, the program will assert and print a report
like this (this was caught with google/mt5-small under fp16 mixed precision):
Detected inf/nan during batch_number=0
Last 21 forward frames:
abs min  abs max  metadata
                  encoder.block.1.layer.1.DenseReluDense.dropout Dropout
0.00e+00 2.57e+02 input[0]
0.00e+00 2.85e+02 output
[]
                  encoder.block.2.layer.0 T5LayerSelfAttention
6.78e-04 3.15e+03 input[0]
2.65e-04 3.42e+03 output[0]
             None output[1]
2.25e-01 1.00e+04 output[2]
                  encoder.block.2.layer.1.layer_norm T5LayerNorm
8.69e-02 4.18e-01 weight
2.65e-04 3.42e+03 input[0]
1.79e-06 4.65e+00 output
                  encoder.block.2.layer.1.DenseReluDense.wi_0 Linear
2.17e-07 4.50e+00 weight
1.79e-06 4.65e+00 input[0]
2.68e-06 3.70e+01 output
                  encoder.block.2.layer.1.DenseReluDense.wi_1 Linear
8.08e-07 2.66e+01 weight
1.79e-06 4.65e+00 input[0]
1.27e-04 2.37e+02 output
                  encoder.block.2.layer.1.DenseReluDense.dropout Dropout
0.00e+00 8.76e+03 input[0]
0.00e+00 9.74e+03 output
                  encoder.block.2.layer.1.DenseReluDense.wo Linear
1.01e-06 6.44e+00 weight
0.00e+00 9.74e+03 input[0]
3.18e-04 6.27e+04 output
                  encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense
1.79e-06 4.65e+00 input[0]
3.18e-04 6.27e+04 output
                  encoder.block.2.layer.1.dropout Dropout
3.18e-04 6.27e+04 input[0]
0.00e+00      inf output
The example output has been trimmed in the middle for brevity.
The second column shows the value of the absolute largest element, so if you have a closer look at the last few frames,
the inputs and outputs were in the range of 1e4. So when this training was done under fp16 mixed precision the very
last step overflowed (since under fp16 the largest number before inf is 64e3). To avoid overflows under
fp16 the activations must remain way below 1e4, because 1e4 * 1e4 = 1e8 so any matrix multiplication with
large activations is going to lead to a numerical overflow condition.
At the very start of the trace you can discover at which batch number the problem occurred (here Detected inf/nan during batch_number=0 means the problem occurred on the first batch).
Each reported frame starts by declaring the fully qualified entry for the corresponding module this frame is reporting
for. If we look just at this frame:
encoder.block.2.layer.1.layer_norm T5LayerNorm
8.69e-02 4.18e-01 weight
2.65e-04 3.42e+03 input[0]
1.79e-06 4.65e+00 output
Here, encoder.block.2.layer.1.layer_norm indicates that it was a layer norm for the first layer, of the second
block of the encoder. And the specific calls of the forward is T5LayerNorm.
Let's look at the last few frames of that report:
Detected inf/nan during batch_number=0
Last 21 forward frames:
abs min  abs max  metadata
[]
                  encoder.block.2.layer.1.DenseReluDense.wi_0 Linear
2.17e-07 4.50e+00 weight
1.79e-06 4.65e+00 input[0]
2.68e-06 3.70e+01 output
                  encoder.block.2.layer.1.DenseReluDense.wi_1 Linear
8.08e-07 2.66e+01 weight
1.79e-06 4.65e+00 input[0]
1.27e-04 2.37e+02 output
                  encoder.block.2.layer.1.DenseReluDense.wo Linear
1.01e-06 6.44e+00 weight
0.00e+00 9.74e+03 input[0]
3.18e-04 6.27e+04 output
                  encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense
1.79e-06 4.65e+00 input[0]
3.18e-04 6.27e+04 output
                  encoder.block.2.layer.1.dropout Dropout
3.18e-04 6.27e+04 input[0]
0.00e+00      inf output
The last frame reports for Dropout.forward function with the first entry for the only input and the second for the
only output. You can see that it was called from an attribute dropout inside DenseReluDense class. We can see
that it happened during the first layer, of the 2nd block, during the very first batch. Finally, the absolute largest
input elements was 6.27e+04 and same for the output was inf.
You can see here, that T5DenseGatedGeluDense.forward resulted in output activations, whose absolute max value was
around 62.7K, which is very close to fp16's top limit of 64K. In the next frame we have Dropout which renormalizes
the weights, after it zeroed some of the elements, which pushes the absolute max value to more than 64K, and we get an
overflow (inf).
As you can see it's the previous frames that we need to look into when the numbers start going into very large for fp16
numbers.
Let's match the report to the code from models/t5/modeling_t5.py:
thon
class T5DenseGatedGeluDense(nn.Module):
    def init(self, config):
        super().init()
        self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.gelu_act = ACT2FN[""gelu_new""]
def forward(self, hidden_states):
    hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
    hidden_linear = self.wi_1(hidden_states)
    hidden_states = hidden_gelu * hidden_linear
    hidden_states = self.dropout(hidden_states)
    hidden_states = self.wo(hidden_states)
    return hidden_states

Now it's easy to see the dropout call, and all the previous calls as well.
Since the detection is happening in a forward hook, these reports are printed immediately after each forward
returns.
Going back to the full report, to act on it and to fix the problem, we need to go a few frames up where the numbers
started to go up and most likely switch to the fp32 mode here, so that the numbers don't overflow when multiplied
or summed up. Of course, there might be other solutions. For example, we could turn off amp temporarily if it's
enabled, after moving the original forward into a helper wrapper, like so:
thon
def _forward(self, hidden_states):
    hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
    hidden_linear = self.wi_1(hidden_states)
    hidden_states = hidden_gelu * hidden_linear
    hidden_states = self.dropout(hidden_states)
    hidden_states = self.wo(hidden_states)
    return hidden_states
import torch
def forward(self, hidden_states):
    if torch.is_autocast_enabled():
        with torch.cuda.amp.autocast(enabled=False):
            return self._forward(hidden_states)
    else:
        return self._forward(hidden_states)

Since the automatic detector only reports on inputs and outputs of full frames, once you know where to look, you may
want to analyse the intermediary stages of any specific forward function as well. In such a case you can use the
detect_overflow helper function to inject the detector where you want it, for example:
thon
from debug_utils import detect_overflow
class T5LayerFF(nn.Module):
    []
def forward(self, hidden_states):
    forwarded_states = self.layer_norm(hidden_states)
    detect_overflow(forwarded_states, ""after layer_norm"")
    forwarded_states = self.DenseReluDense(forwarded_states)
    detect_overflow(forwarded_states, ""after DenseReluDense"")
    return hidden_states + self.dropout(forwarded_states)

You can see that we added 2 of these and now we track if inf or nan for forwarded_states was detected
somewhere in between.
Actually, the detector already reports these because each of the calls in the example above is a nn.Module, but
let's say if you had some local direct calculations this is how you'd do that.
Additionally, if you're instantiating the debugger in your own code, you can adjust the number of frames printed from
its default, e.g.:
thon
from transformers.debug_utils import DebugUnderflowOverflow
debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100)

Specific batch absolute min and max value tracing
The same debugging class can be used for per-batch tracing with the underflow/overflow detection feature turned off.
Let's say you want to watch the absolute min and max values for all the ingredients of each forward call of a given
batch, and only do that for batches 1 and 3. Then you instantiate this class as:
python
debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3])
And now full batches 1 and 3 will be traced using the same format as the underflow/overflow detector does.
Batches are 0-indexed.
This is helpful if you know that the program starts misbehaving after a certain batch number, so you can fast-forward
right to that area. Here is a sample truncated output for such configuration:

                  *** Starting batch number=1 ***
abs min  abs max  metadata
                  shared Embedding
1.01e-06 7.92e+02 weight
0.00e+00 2.47e+04 input[0]
5.36e-05 7.92e+02 output
[]
                  decoder.dropout Dropout
1.60e-07 2.27e+01 input[0]
0.00e+00 2.52e+01 output
                  decoder T5Stack
     not a tensor output
                  lm_head Linear
1.01e-06 7.92e+02 weight
0.00e+00 1.11e+00 input[0]
6.06e-02 8.39e+01 output
                   T5ForConditionalGeneration
     not a tensor output
              *** Starting batch number=3 ***

abs min  abs max  metadata
                  shared Embedding
1.01e-06 7.92e+02 weight
0.00e+00 2.78e+04 input[0]
5.36e-05 7.92e+02 output
[]

Here you will get a huge number of frames dumped - as many as there were forward calls in your model, so it may or may
not what you want, but sometimes it can be easier to use for debugging purposes than a normal debugger. For example, if
a problem starts happening at batch number 150. So you can dump traces for batches 149 and 150 and compare where
numbers started to diverge.
You can also specify the batch number after which to stop the training, with:
python
debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3)"
"
Custom Layers and Utilities
This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
Most of those are only useful if you are studying the code of the models in the library.
Pytorch custom modules
[[autodoc]] pytorch_utils.Conv1D
[[autodoc]] modeling_utils.PoolerStartLogits
    - forward
[[autodoc]] modeling_utils.PoolerEndLogits
    - forward
[[autodoc]] modeling_utils.PoolerAnswerClass
    - forward
[[autodoc]] modeling_utils.SquadHeadOutput
[[autodoc]] modeling_utils.SQuADHead
    - forward
[[autodoc]] modeling_utils.SequenceSummary
    - forward
PyTorch Helper Functions
[[autodoc]] pytorch_utils.apply_chunking_to_forward
[[autodoc]] pytorch_utils.find_pruneable_heads_and_indices
[[autodoc]] pytorch_utils.prune_layer
[[autodoc]] pytorch_utils.prune_conv1d_layer
[[autodoc]] pytorch_utils.prune_linear_layer
TensorFlow custom layers
[[autodoc]] modeling_tf_utils.TFConv1D
[[autodoc]] modeling_tf_utils.TFSequenceSummary
TensorFlow loss functions
[[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss
[[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss
[[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss
[[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss
[[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss
[[autodoc]] modeling_tf_utils.TFTokenClassificationLoss
TensorFlow Helper Functions
[[autodoc]] modeling_tf_utils.get_initializer
[[autodoc]] modeling_tf_utils.keras_serializable
[[autodoc]] modeling_tf_utils.shape_list"
"
Training on TPUs

Note: Most of the strategies introduced in the single GPU section (such as mixed precision training or gradient accumulation) and multi-GPU section are generic and apply to training models in general so make sure to have a look at it before diving into this section.

This document will be completed soon with information on how to train on TPUs."
"
Notes:

if you need to run on a specific GPU, which is different from GPU 0, you can't use CUDA_VISIBLE_DEVICES to limit
  the visible scope of available GPUs. Instead, you have to use the following syntax:

  deepspeed --include localhost:1 examples/pytorch/translation/run_translation.py 
In this example, we tell DeepSpeed to use GPU 1 (second gpu).

Deployment with multiple Nodes
The information in this section isn't not specific to the DeepSpeed integration and is applicable to any multi-node program. But DeepSpeed provides a deepspeed launcher that is easier to use than other launchers unless you are in a SLURM environment.
For the duration of this section let's assume that you have 2 nodes with 8 gpus each. And you can reach the first node with ssh hostname1 and second node with ssh hostname2, and both must be able to reach each other via ssh locally without a password. Of course, you will need to rename these host (node) names to the actual host names you are working with.
The torch.distributed.run launcher
For example, to use torch.distributed.run, you could do:

python -m torch.distributed.run --nproc_per_node=8 --nnode=2 --node_rank=0 --master_addr=hostname1 \
--master_port=9901 your_program.py <normal cl args> --deepspeed ds_config.json
You have to ssh to each node and run this same command on each one of them! There is no rush, the launcher will wait until both nodes will synchronize.
For more information please see torchrun. Incidentally, this is also the launcher that replaced torch.distributed.launch a few pytorch versions back.
The deepspeed launcher
To use the deepspeed launcher instead, you have to first create a hostfile file:
hostname1 slots=8
hostname2 slots=8
and then you can launch it as:

deepspeed --num_gpus 8 --num_nodes 2 --hostfile hostfile --master_addr hostname1 --master_port=9901 \
your_program.py <normal cl args> --deepspeed ds_config.json
Unlike the torch.distributed.run launcher, deepspeed will automatically launch this command on both nodes!
For more information please see Resource Configuration (multi-node).
Launching in a SLURM environment
In the SLURM environment the following approach can be used. The following is a slurm script launch.slurm which you will need to adapt it to your specific SLURM environment.
```bash
SBATCH --job-name=test-nodes        # name
SBATCH --nodes=2                    # nodes
SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
SBATCH --cpus-per-task=10           # number of cores per tasks
SBATCH --gres=gpu:8                 # number of gpus
SBATCH --time 20:00:00              # maximum execution time (HH:MM:SS)
SBATCH --output=%x-%j.out           # output file name
export GPUS_PER_NODE=8
export MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
export MASTER_PORT=9901
srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
 --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
 --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
your_program.py  --deepspeed ds_config.json'

All is left is to schedule it to run:

sbatch launch.slurm
srun will take care of launching the program simultaneously on all nodes.
Use of Non-shared filesystem
By default DeepSpeed expects that a multi-node environment uses a shared storage. If this is not the case and each node can only see the local filesystem, you need to adjust the config file to include a  checkpoint_section with the following setting:
json
{
  ""checkpoint"": {
    ""use_node_local_storage"": true
  }
}
Alternatively, you can also use the [Trainer]'s --save_on_each_node argument, and the above config will be added automatically for you.

Deployment in Notebooks
The problem with running notebook cells as a script is that there is no normal deepspeed launcher to rely on, so
under certain setups we have to emulate it.
If you're using only 1 GPU, here is how you'd have to adjust your training code in the notebook to use DeepSpeed.
thon
DeepSpeed requires a distributed environment even when only one process is used.
This emulates a launcher in the notebook
import os
os.environ[""MASTER_ADDR""] = ""localhost""
os.environ[""MASTER_PORT""] = ""9994""  # modify if RuntimeError: Address already in use
os.environ[""RANK""] = ""0""
os.environ[""LOCAL_RANK""] = ""0""
os.environ[""WORLD_SIZE""] = ""1""
Now proceed as normal, plus pass the deepspeed config file
training_args = TrainingArguments(, deepspeed=""ds_config_zero3.json"")
trainer = Trainer()
trainer.train()

Note:  stands for the normal arguments that you'd pass to the functions.
If you want to use more than 1 GPU, you must use a multi-process environment for DeepSpeed to work. That is, you have
to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented
at the beginning of this section.
If you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated
cell with:
thon no-style
%%bash
cat <<'EOT' > ds_config_zero3.json
{
    ""fp16"": {
        ""enabled"": ""auto"",
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    },
""optimizer"": {
    ""type"": ""AdamW"",
    ""params"": {
        ""lr"": ""auto"",
        ""betas"": ""auto"",
        ""eps"": ""auto"",
        ""weight_decay"": ""auto""
    }
},

""scheduler"": {
    ""type"": ""WarmupLR"",
    ""params"": {
        ""warmup_min_lr"": ""auto"",
        ""warmup_max_lr"": ""auto"",
        ""warmup_num_steps"": ""auto""
    }
},

""zero_optimization"": {
    ""stage"": 3,
    ""offload_optimizer"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""offload_param"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""overlap_comm"": true,
    ""contiguous_gradients"": true,
    ""sub_group_size"": 1e9,
    ""reduce_bucket_size"": ""auto"",
    ""stage3_prefetch_bucket_size"": ""auto"",
    ""stage3_param_persistence_threshold"": ""auto"",
    ""stage3_max_live_parameters"": 1e9,
    ""stage3_max_reuse_distance"": 1e9,
    ""stage3_gather_16bit_weights_on_model_save"": true
},

""gradient_accumulation_steps"": ""auto"",
""gradient_clipping"": ""auto"",
""steps_per_print"": 2000,
""train_batch_size"": ""auto"",
""train_micro_batch_size_per_gpu"": ""auto"",
""wall_clock_breakdown"": false

}
EOT

If the training script is in a normal file and not in the notebook cells, you can launch deepspeed normally via
shell from a cell. For example, to use run_translation.py you would launch it with:
python no-style
!git clone https://github.com/huggingface/transformers
!cd transformers; deepspeed examples/pytorch/translation/run_translation.py 
or with %%bash magic, where you can write a multi-line code for the shell program to run:
thon no-style
%%bash
git clone https://github.com/huggingface/transformers
cd transformers
deepspeed examples/pytorch/translation/run_translation.py 

In such case you don't need any of the code presented at the beginning of this section.
Note: While %%bash magic is neat, but currently it buffers the output so you won't see the logs until the process
completes.

Configuration
For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer
to the following documentation.
You can find dozens of DeepSpeed configuration examples that address various practical needs in the DeepSpeedExamples
repo:

git clone https://github.com/microsoft/DeepSpeedExamples
cd DeepSpeedExamples
find . -name '*json'
Continuing the code from above, let's say you're looking to configure the Lamb optimizer. So you can search through the
example .json files with:

grep -i Lamb $(find . -name '*json')
Some more examples are to be found in the main repo as well.
When using DeepSpeed you always need to supply a DeepSpeed configuration file, yet some configuration parameters have
to be configured via the command line. You will find the nuances in the rest of this guide.
To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features,
including optimizer states cpu offload, uses AdamW optimizer and WarmupLR scheduler and will enable mixed
precision training if --fp16 is passed:
```json
{
    ""fp16"": {
        ""enabled"": ""auto"",
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    },
""optimizer"": {
    ""type"": ""AdamW"",
    ""params"": {
        ""lr"": ""auto"",
        ""betas"": ""auto"",
        ""eps"": ""auto"",
        ""weight_decay"": ""auto""
    }
},

""scheduler"": {
    ""type"": ""WarmupLR"",
    ""params"": {
        ""warmup_min_lr"": ""auto"",
        ""warmup_max_lr"": ""auto"",
        ""warmup_num_steps"": ""auto""
    }
},

""zero_optimization"": {
    ""stage"": 2,
    ""offload_optimizer"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""allgather_partitions"": true,
    ""allgather_bucket_size"": 2e8,
    ""overlap_comm"": true,
    ""reduce_scatter"": true,
    ""reduce_bucket_size"": 2e8,
    ""contiguous_gradients"": true
},

""gradient_accumulation_steps"": ""auto"",
""gradient_clipping"": ""auto"",
""train_batch_size"": ""auto"",
""train_micro_batch_size_per_gpu"": ""auto"",

}

When you execute the program, DeepSpeed will log the configuration it received from the [Trainer]
to the console, so you can see exactly what was the final configuration passed to it.

Passing Configuration
As discussed in this document normally the DeepSpeed configuration is passed as a path to a json file, but if you're
not using the command line interface to configure the training, and instead instantiate the
[Trainer] via [TrainingArguments] then for the deepspeed argument you can
pass a nested dict. This allows you to create the configuration on the fly and doesn't require you to write it to
the file system before passing it to [TrainingArguments].
To summarize you can do:
python
TrainingArguments(, deepspeed=""/path/to/ds_config.json"")
or:
python
ds_config_dict = dict(scheduler=scheduler_params, optimizer=optimizer_params)
TrainingArguments(, deepspeed=ds_config_dict)

Shared Configuration

This section is a must-read

Some configuration values are required by both the [Trainer] and DeepSpeed to function correctly,
therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those
via the [Trainer] command line arguments.
Additionally, some configuration values are derived automatically based on the model's configuration, so instead of
remembering to manually adjust multiple values, it's the best to let the [Trainer] do the majority
of configuration for you.
Therefore, in the rest of this guide you will find a special configuration value: auto, which when set will be
automatically replaced with the correct or most efficient value. Please feel free to choose to ignore this
recommendation and set the values explicitly, in which case be very careful that your the
[Trainer] arguments and DeepSpeed configurations agree. For example, are you using the same
learning rate, or batch size, or gradient accumulation settings? if these mismatch the training may fail in very
difficult to detect ways. You have been warned.
There are multiple other values that are specific to DeepSpeed-only and those you will have to set manually to suit
your needs.
In your own programs, you can also use the following approach if you'd like to modify the DeepSpeed config as a master
and configure [TrainingArguments] based on that. The steps are:

Create or load the DeepSpeed configuration to be used as a master configuration
Create the [TrainingArguments] object based on these values

Do note that some values, such as scheduler.params.total_num_steps are calculated by
[Trainer] during train, but you can of course do the math yourself.

ZeRO
Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. It
supports 3 different levels (stages) of optimization. The first one is not quite interesting for scalability purposes,
therefore this document focuses on stages 2 and 3. Stage 3 is further improved by the latest addition of ZeRO-Infinity.
You will find more indepth information in the DeepSpeed documentation.
The zero_optimization section of the configuration file is the most important part (docs), since that is where you define
which ZeRO stages you want to enable and how to configure them. You will find the explanation for each parameter in the
DeepSpeed docs.
This section has to be configured exclusively via DeepSpeed configuration - the [Trainer] provides
no equivalent command line arguments.
Note: currently DeepSpeed doesn't validate parameter names, so if you misspell any, it'll use the default setting for
the parameter that got misspelled. You can watch the DeepSpeed engine start up log messages to see what values it is
going to use.

ZeRO-2 Config
The following is an example of configuration for ZeRO stage 2:
json
{
    ""zero_optimization"": {
        ""stage"": 2,
        ""offload_optimizer"": {
            ""device"": ""cpu"",
            ""pin_memory"": true
        },
        ""allgather_partitions"": true,
        ""allgather_bucket_size"": 5e8,
        ""overlap_comm"": true,
        ""reduce_scatter"": true,
        ""reduce_bucket_size"": 5e8,
        ""contiguous_gradients"": true
    }
}
Performance tuning:

enabling offload_optimizer should reduce GPU RAM usage (it requires ""stage"": 2)
""overlap_comm"": true trades off increased GPU RAM usage to lower all-reduce latency. overlap_comm uses 4.5x
  the allgather_bucket_size and reduce_bucket_size values. So if they are set to 5e8, this requires a 9GB
  footprint (5e8 x 2Bytes x 2 x 4.5). Therefore, if you have a GPU with 8GB or less RAM, to avoid getting
  OOM-errors you will need to reduce those parameters to about 2e8, which would require 3.6GB. You will want to do
  the same on larger capacity GPU as well, if you're starting to hit OOM.
when reducing these buffers you're trading communication speed to avail more GPU RAM. The smaller the buffer size is,
  the slower the communication gets, and the more GPU RAM will be available to other tasks. So if a bigger batch size is
  important, getting a slightly slower training time could be a good trade.

Additionally, deepspeed==0.4.4 added a new option round_robin_gradients which you can enable with:
json
{
    ""zero_optimization"": {
        ""round_robin_gradients"": true
    }
}
This is a stage 2 optimization for CPU offloading that parallelizes gradient copying to CPU memory among ranks by fine-grained gradient partitioning. Performance benefit grows with gradient accumulation steps (more copying between optimizer steps) or GPU count (increased parallelism).

ZeRO-3 Config
The following is an example of configuration for ZeRO stage 3:
json
{
    ""zero_optimization"": {
        ""stage"": 3,
        ""offload_optimizer"": {
            ""device"": ""cpu"",
            ""pin_memory"": true
        },
        ""offload_param"": {
            ""device"": ""cpu"",
            ""pin_memory"": true
        },
        ""overlap_comm"": true,
        ""contiguous_gradients"": true,
        ""sub_group_size"": 1e9,
        ""reduce_bucket_size"": ""auto"",
        ""stage3_prefetch_bucket_size"": ""auto"",
        ""stage3_param_persistence_threshold"": ""auto"",
        ""stage3_max_live_parameters"": 1e9,
        ""stage3_max_reuse_distance"": 1e9,
        ""stage3_gather_16bit_weights_on_model_save"": true
    }
}
If you are getting OOMs, because your model or activations don't fit into the GPU memory and you have unutilized CPU
memory offloading the optimizer states and parameters to CPU memory with ""device"": ""cpu"" may solve this limitation.
If you don't want to offload to CPU memory, use none instead of cpu for the device entry. Offloading to
NVMe is discussed further down.
Pinned memory is enabled with pin_memory set to true. This feature can improve the throughput at the cost of
making less memory available to other processes. Pinned memory is set aside to the specific process that requested it
and its typically accessed much faster than normal CPU memory.
Performance tuning:

stage3_max_live_parameters: 1e9
stage3_max_reuse_distance: 1e9

If hitting OOM reduce stage3_max_live_parameters and stage3_max_reuse_distance. They should have minimal impact
on performance unless you are doing activation checkpointing. 1e9 would consume ~2GB. The memory is shared by
stage3_max_live_parameters and stage3_max_reuse_distance, so it's not additive, it's just 2GB total.
stage3_max_live_parameters is the upper limit on how many full parameters you want to keep on the GPU at any given
time. ""reuse distance"" is a metric we are using to figure out when will a parameter be used again in the future, and we
use the stage3_max_reuse_distance to decide whether to throw away the parameter or to keep it. If a parameter is
going to be used again in near future (less than stage3_max_reuse_distance) then we keep it to reduce communication
overhead. This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and
backward passes a single layer granularity and want to keep the parameter in the forward recompute till the backward
The following configuration values depend on the model's hidden size:

reduce_bucket_size: hidden_size*hidden_size
stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size
stage3_param_persistence_threshold: 10 * hidden_size

therefore set these values to auto and the [Trainer] will automatically assign the recommended
values. But, of course, feel free to set these explicitly as well.
stage3_gather_16bit_weights_on_model_save enables model fp16 weights consolidation when model gets saved. With large
models and multiple GPUs this is an expensive operation both in terms of memory and speed. It's currently required if
you plan to resume the training. Watch out for future updates that will remove this limitation and make things more
flexible.
If you're migrating from ZeRO-2 configuration note that allgather_partitions, allgather_bucket_size and
reduce_scatter configuration parameters are not used in ZeRO-3. If you keep these in the config file they will just
be ignored.

sub_group_size: 1e9

sub_group_size controls the granularity in which parameters are updated during optimizer steps. Parameters are
grouped into buckets of sub_group_size and each buckets is updated one at a time. When used with NVMe offload in
ZeRO-Infinity, sub_group_size therefore controls the granularity in which model states are moved in and out of CPU
memory from NVMe during the optimizer step. This prevents running out of CPU memory for extremely large models.
You can leave sub_group_size to its default value of 1e9 when not using NVMe offload. You may want to change its
default value in the following cases:

Running into OOM during optimizer step: Reduce sub_group_size to reduce memory utilization of temporary buffers
Optimizer Step is taking a long time: Increase sub_group_size to improve bandwidth utilization as a result of
   the increased data buffers.

ZeRO-0 Config
Note that we're listing Stage 0 and 1 last since they are rarely used.
Stage 0 is disabling all types of sharding and just using DeepSpeed as DDP. You can turn it on with:
json
{
    ""zero_optimization"": {
        ""stage"": 0
    }
}
This will essentially disable ZeRO without you needing to change anything else.
ZeRO-1 Config
Stage 1 is Stage 2 minus gradient sharding. You can always try it to speed things a tiny bit to only shard the optimizer states with:
json
{
    ""zero_optimization"": {
        ""stage"": 1
    }
}

NVMe Support
ZeRO-Infinity allows for training incredibly large models by extending GPU and CPU memory with NVMe memory. Thanks to
smart partitioning and tiling algorithms each GPU needs to send and receive very small amounts of data during
offloading so modern NVMe proved to be fit to allow for an even larger total memory pool available to your training
process. ZeRO-Infinity requires ZeRO-3 enabled.
The following configuration example enables NVMe to offload both optimizer states and the params:
json
{
    ""zero_optimization"": {
        ""stage"": 3,
        ""offload_optimizer"": {
            ""device"": ""nvme"",
            ""nvme_path"": ""/local_nvme"",
            ""pin_memory"": true,
            ""buffer_count"": 4,
            ""fast_init"": false
        },
        ""offload_param"": {
            ""device"": ""nvme"",
            ""nvme_path"": ""/local_nvme"",
            ""pin_memory"": true,
            ""buffer_count"": 5,
            ""buffer_size"": 1e8,
            ""max_in_cpu"": 1e9
        },
        ""aio"": {
            ""block_size"": 262144,
            ""queue_depth"": 32,
            ""thread_count"": 1,
            ""single_submit"": false,
            ""overlap_events"": true
        },
        ""overlap_comm"": true,
        ""contiguous_gradients"": true,
        ""sub_group_size"": 1e9,
        ""reduce_bucket_size"": ""auto"",
        ""stage3_prefetch_bucket_size"": ""auto"",
        ""stage3_param_persistence_threshold"": ""auto"",
        ""stage3_max_live_parameters"": 1e9,
        ""stage3_max_reuse_distance"": 1e9,
        ""stage3_gather_16bit_weights_on_model_save"": true
    },
}
You can choose to offload both optimizer states and params to NVMe, or just one of them or none. For example, if you
have copious amounts of CPU memory available, by all means offload to CPU memory only as it'd be faster (hint:
""device"": ""cpu"").
Here is the full documentation for offloading optimizer states and parameters.
Make sure that your nvme_path is actually an NVMe, since it will work with the normal hard drive or SSD, but it'll
be much much slower. The fast scalable training was designed with modern NVMe transfer speeds in mind (as of this
writing one can have ~3.5GB/s read, ~3GB/s write peak speeds).
In order to figure out the optimal aio configuration block you must run a benchmark on your target setup, as
explained here.

ZeRO-2 vs ZeRO-3 Performance
ZeRO-3 is likely to be slower than ZeRO-2 if everything else is configured the same because the former has to gather
model weights in addition to what ZeRO-2 does. If ZeRO-2 meets your needs and you don't need to scale beyond a few GPUs
then you may choose to stick to it. It's important to understand that ZeRO-3 enables a much higher scalability capacity
at a cost of speed.
It's possible to adjust ZeRO-3 configuration to make it perform closer to ZeRO-2:

set stage3_param_persistence_threshold to a very large number - larger than the largest parameter, e.g., 6 * hidden_size * hidden_size. This will keep the parameters on the GPUs.
turn off offload_params since ZeRO-2 doesn't have that option.

The performance will likely improve significantly with just offload_params turned off, even if you don't change
stage3_param_persistence_threshold. Of course, these changes will impact the size of the model you can train. So
these help you to trade scalability for speed depending on your needs.

ZeRO-2 Example
Here is a full ZeRO-2 auto-configuration file ds_config_zero2.json:
```json
{
    ""fp16"": {
        ""enabled"": ""auto"",
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    },
""optimizer"": {
    ""type"": ""AdamW"",
    ""params"": {
        ""lr"": ""auto"",
        ""betas"": ""auto"",
        ""eps"": ""auto"",
        ""weight_decay"": ""auto""
    }
},

""scheduler"": {
    ""type"": ""WarmupLR"",
    ""params"": {
        ""warmup_min_lr"": ""auto"",
        ""warmup_max_lr"": ""auto"",
        ""warmup_num_steps"": ""auto""
    }
},

""zero_optimization"": {
    ""stage"": 2,
    ""offload_optimizer"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""allgather_partitions"": true,
    ""allgather_bucket_size"": 2e8,
    ""overlap_comm"": true,
    ""reduce_scatter"": true,
    ""reduce_bucket_size"": 2e8,
    ""contiguous_gradients"": true
},

""gradient_accumulation_steps"": ""auto"",
""gradient_clipping"": ""auto"",
""steps_per_print"": 2000,
""train_batch_size"": ""auto"",
""train_micro_batch_size_per_gpu"": ""auto"",
""wall_clock_breakdown"": false

}

Here is a full ZeRO-2 all-enabled manually set configuration file. It is here mainly for you to see what the typical
values look like, but we highly recommend using the one with multiple auto settings in it.
```json
{
    ""fp16"": {
        ""enabled"": true,
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    },
""optimizer"": {
    ""type"": ""AdamW"",
    ""params"": {
        ""lr"": 3e-5,
        ""betas"": [0.8, 0.999],
        ""eps"": 1e-8,
        ""weight_decay"": 3e-7
    }
},

""scheduler"": {
    ""type"": ""WarmupLR"",
    ""params"": {
        ""warmup_min_lr"": 0,
        ""warmup_max_lr"": 3e-5,
        ""warmup_num_steps"": 500
    }
},

""zero_optimization"": {
    ""stage"": 2,
    ""offload_optimizer"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""allgather_partitions"": true,
    ""allgather_bucket_size"": 2e8,
    ""overlap_comm"": true,
    ""reduce_scatter"": true,
    ""reduce_bucket_size"": 2e8,
    ""contiguous_gradients"": true
},

""steps_per_print"": 2000,
""wall_clock_breakdown"": false

}

ZeRO-3 Example
Here is a full ZeRO-3 auto-configuration file ds_config_zero3.json:
```json
{
    ""fp16"": {
        ""enabled"": ""auto"",
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    },
""optimizer"": {
    ""type"": ""AdamW"",
    ""params"": {
        ""lr"": ""auto"",
        ""betas"": ""auto"",
        ""eps"": ""auto"",
        ""weight_decay"": ""auto""
    }
},

""scheduler"": {
    ""type"": ""WarmupLR"",
    ""params"": {
        ""warmup_min_lr"": ""auto"",
        ""warmup_max_lr"": ""auto"",
        ""warmup_num_steps"": ""auto""
    }
},

""zero_optimization"": {
    ""stage"": 3,
    ""offload_optimizer"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""offload_param"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""overlap_comm"": true,
    ""contiguous_gradients"": true,
    ""sub_group_size"": 1e9,
    ""reduce_bucket_size"": ""auto"",
    ""stage3_prefetch_bucket_size"": ""auto"",
    ""stage3_param_persistence_threshold"": ""auto"",
    ""stage3_max_live_parameters"": 1e9,
    ""stage3_max_reuse_distance"": 1e9,
    ""stage3_gather_16bit_weights_on_model_save"": true
},

""gradient_accumulation_steps"": ""auto"",
""gradient_clipping"": ""auto"",
""steps_per_print"": 2000,
""train_batch_size"": ""auto"",
""train_micro_batch_size_per_gpu"": ""auto"",
""wall_clock_breakdown"": false

}

Here is a full ZeRO-3 all-enabled manually set configuration file. It is here mainly for you to see what the typical
values look like, but we highly recommend using the one with multiple auto settings in it.
```json
{
    ""fp16"": {
        ""enabled"": true,
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    },
""optimizer"": {
    ""type"": ""AdamW"",
    ""params"": {
        ""lr"": 3e-5,
        ""betas"": [0.8, 0.999],
        ""eps"": 1e-8,
        ""weight_decay"": 3e-7
    }
},

""scheduler"": {
    ""type"": ""WarmupLR"",
    ""params"": {
        ""warmup_min_lr"": 0,
        ""warmup_max_lr"": 3e-5,
        ""warmup_num_steps"": 500
    }
},

""zero_optimization"": {
    ""stage"": 3,
    ""offload_optimizer"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""offload_param"": {
        ""device"": ""cpu"",
        ""pin_memory"": true
    },
    ""overlap_comm"": true,
    ""contiguous_gradients"": true,
    ""sub_group_size"": 1e9,
    ""reduce_bucket_size"": 1e6,
    ""stage3_prefetch_bucket_size"": 0.94e6,
    ""stage3_param_persistence_threshold"": 1e4,
    ""stage3_max_live_parameters"": 1e9,
    ""stage3_max_reuse_distance"": 1e9,
    ""stage3_gather_16bit_weights_on_model_save"": true
},

""steps_per_print"": 2000,
""wall_clock_breakdown"": false

}

How to Choose Which ZeRO Stage and Offloads To Use For Best Performance
So now you know there are all these different stages. How to decide which of them to use? This section will attempt to address this question.
In general the following applies:

Speed-wise (left is faster than right)

Stage 0 (DDP) > Stage 1 > Stage 2 > Stage 2 + offload > Stage 3 > Stage 3 + offloads

GPU Memory usage-wise (right is more GPU memory efficient than left)

Stage 0 (DDP) < Stage 1 < Stage 2 < Stage 2 + offload < Stage 3 < Stage 3 + offloads
So when you want to get the fastest execution while fitting into minimal number of GPUs, here is the process you could follow. We start with the fastest approach and if running into GPU OOM we then go to the next slower approach, but which will use less GPU memory. And so on and so forth.
First of all set batch size to 1 (you can always use gradient accumulation for any desired effective batch size).

Enable --gradient_checkpointing 1 (HF Trainer) or directly model.gradient_checkpointing_enable() - if OOM then
Try ZeRO stage 2 first. if OOM then
Try ZeRO stage 2 + offload_optimizer - if OOM then
Switch to ZeRO stage 3 - if OOM then
Enable offload_param to cpu - if OOM then

Enable offload_optimizer to cpu - if OOM then

If you still can't fit a batch size of 1 first check various default values and lower them if you can. For example, if you use generate and you don't use a wide search beam make it narrower as it'd take a lot of memory.

Definitely use mixed half-precision over fp32 - so bf16 on Ampere and higher GPUs and fp16 on older gpu architectures.

If you still OOM you could add more hardware or enable ZeRO-Infinity - that is switch offloads offload_param and  offload_optimizer to nvme. You need to make sure it's a very fast nvme. As an anecdote I was able to infer BLOOM-176B on a tiny GPU using ZeRO-Infinity except it was extremely slow. But it worked!

You can, of course, work through these steps in reverse by starting with the most GPU memory efficient config and then going backwards. Or try bi-secting it.
Once you have your batch size 1 not leading to OOM, measure your effective throughput.
Next try to increase the batch size to as large as you can, since the higher the batch size the more efficient the GPUs are as they perform the best when matrices they multiply are huge.
Now the performance optimization game starts. You can turn off some offload features or step down in ZeRO stages and increase/decrease batch size and again measure your effective throughput. Rinse and repeat until satisfied.
Don't spend forever on it, but if you're about to start a 3 months training - do spend a few days on it to find the most effective throughput-wise setup. So that your training cost will be the lowest and you will finish training faster. In the current crazy-paced ML world, if it takes you an extra month to train something you are likely to miss a golden opportunity. Of course, this is only me sharing an observation and in no way I'm trying to rush you. Before beginning to train BLOOM-176B I spent 2 days on this process and was able to increase throughput from 90 to 150 TFLOPs! This effort saved us more than one month of training time.
These notes were written primarily for the training mode, but they should mostly apply for inference as well. For example, during inference Gradient Checkpointing is a no-op since it is only useful during training. Additionally, we found out that if you are doing a multi-GPU inference and not using DeepSpeed-Inference, Accelerate should provide a superior performance.
Other quick related performance notes:
- if you are training something from scratch always try to have tensors with shapes that are divisible by 16 (e.g. hidden size). For batch size try divisible by 2 at least. There are wave and tile quanitization divisibility that is hardware-specific if you want to squeeze even higher performance from your GPUs.
Activation Checkpointing or Gradient Checkpointing
Activation checkpointing and gradient checkpointing are two distinct terms that refer to the same methodology. It's very confusing but this is how it is.
Gradient checkpointing allows one to trade speed for GPU memory, which either allows one to overcome a GPU OOM, or increase their batch size, which often leads to a better performance.
HF Transformers models don't know anything about DeepSpeed's activation checkpointing, so if you try to enable that feature in the DeepSpeed config file, nothing will happen.
Therefore you have two ways to take advantage of this very beneficial feature:

If you want to use a HF Transformers models you can do model.gradient_checkpointing_enable() or use --gradient_checkpointing in the HF Trainer, which will automatically enable this for you. torch.utils.checkpoint is used there.
If you write your own model and you want to use DeepSpeed's activation checkpointing you can use the API prescribed there. You can also take the HF Transformers modeling code and replace torch.utils.checkpoint with the DeepSpeed's API. The latter is more flexible since it allows you to offload the forward activations to the CPU memory instead of recalculating them.

Optimizer and Scheduler
As long as you don't enable offload_optimizer you can mix and match DeepSpeed and HuggingFace schedulers and
optimizers, with the exception of using the combination of HuggingFace scheduler and DeepSpeed optimizer:
| Combos       | HF Scheduler | DS Scheduler |
| HF Optimizer | Yes          | Yes          |
| DS Optimizer | No           | Yes          |
It is possible to use a non-DeepSpeed optimizer when offload_optimizer is enabled, as long as it has both CPU and
GPU implementation (except LAMB).

Optimizer
DeepSpeed's main optimizers are Adam, AdamW, OneBitAdam, and Lamb. These have been thoroughly tested with ZeRO and are
thus recommended to be used. It, however, can import other optimizers from torch. The full documentation is here.
If you don't configure the optimizer entry in the configuration file, the [Trainer] will
automatically set it to AdamW and will use the supplied values or the defaults for the following command line
arguments: --learning_rate, --adam_beta1, --adam_beta2, --adam_epsilon and --weight_decay.
Here is an example of the auto-configured optimizer entry for AdamW:
json
{
   ""optimizer"": {
       ""type"": ""AdamW"",
       ""params"": {
         ""lr"": ""auto"",
         ""betas"": ""auto"",
         ""eps"": ""auto"",
         ""weight_decay"": ""auto""
       }
   }
}
Note that the command line arguments will set the values in the configuration file. This is so that there is one
definitive source of the values and to avoid hard to find errors when for example, the learning rate is set to
different values in different places. Command line rules. The values that get overridden are:

lr with the value of --learning_rate
betas with the value of --adam_beta1 --adam_beta2
eps with the value of --adam_epsilon
weight_decay with the value of --weight_decay

Therefore please remember to tune the shared hyperparameters on the command line.
You can also set the values explicitly:
json
{
   ""optimizer"": {
       ""type"": ""AdamW"",
       ""params"": {
         ""lr"": 0.001,
         ""betas"": [0.8, 0.999],
         ""eps"": 1e-8,
         ""weight_decay"": 3e-7
       }
   }
}
But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed
configuration.
If you want to use another optimizer which is not listed above, you will have to add to the top level configuration.
json
{
   ""zero_allow_untested_optimizer"": true
}
Similarly to AdamW, you can configure other officially supported optimizers. Just remember that those may have different config values. e.g. for Adam you will want weight_decay around 0.01.
Additionally, offload works the best when it's used with Deepspeed's CPU Adam optimizer. If you want to use a different optimizer with offload, since deepspeed==0.8.3 you need to also add:
json
{
   ""zero_force_ds_cpu_optimizer"": false
}
to the top level configuration.

Scheduler
DeepSpeed supports LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR learning rate schedulers. The full
documentation is here.
Here is where the schedulers overlap between 🤗 Transformers and DeepSpeed:

WarmupLR via --lr_scheduler_type constant_with_warmup
WarmupDecayLR via --lr_scheduler_type linear. This is also the default value for --lr_scheduler_type,
  therefore, if you don't configure the scheduler this is scheduler that will get configured by default.

If you don't configure the scheduler entry in the configuration file, the [Trainer] will use
the values of --lr_scheduler_type, --learning_rate and --warmup_steps or --warmup_ratio to configure a
🤗 Transformers version of it.
Here is an example of the auto-configured scheduler entry for WarmupLR:
json
{
   ""scheduler"": {
         ""type"": ""WarmupLR"",
         ""params"": {
             ""warmup_min_lr"": ""auto"",
             ""warmup_max_lr"": ""auto"",
             ""warmup_num_steps"": ""auto""
         }
     }
}
Since ""auto"" is used the [Trainer] arguments will set the correct values in the configuration
file. This is so that there is one definitive source of the values and to avoid hard to find errors when, for example,
the learning rate is set to different values in different places. Command line rules. The values that get set are:

warmup_min_lr with the value of 0.
warmup_max_lr with the value of --learning_rate.
warmup_num_steps with the value of --warmup_steps if provided. Otherwise will use --warmup_ratio
  multiplied by the number of training steps and rounded up.
total_num_steps with either the value of --max_steps or if it is not provided, derived automatically at run
  time based on the environment and the size of the dataset and other command line arguments (needed for
  WarmupDecayLR).

You can, of course, take over any or all of the configuration values and set those yourself:
json
{
   ""scheduler"": {
         ""type"": ""WarmupLR"",
         ""params"": {
             ""warmup_min_lr"": 0,
             ""warmup_max_lr"": 0.001,
             ""warmup_num_steps"": 1000
         }
     }
}
But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed
configuration.
For example, for WarmupDecayLR, you can use the following entry:
json
{
   ""scheduler"": {
         ""type"": ""WarmupDecayLR"",
         ""params"": {
             ""last_batch_iteration"": -1,
             ""total_num_steps"": ""auto"",
             ""warmup_min_lr"": ""auto"",
             ""warmup_max_lr"": ""auto"",
             ""warmup_num_steps"": ""auto""
         }
     }
}
and total_num_steps, warmup_max_lr, warmup_num_steps and total_num_steps will be set at loading time.

fp32 Precision
Deepspeed supports the full fp32 and the fp16 mixed precision.
Because of the much reduced memory needs and faster speed one gets with the fp16 mixed precision, the only time you
will want to not use it is when the model you're using doesn't behave well under this training mode. Typically this
happens when the model wasn't pretrained in the fp16 mixed precision (e.g. often this happens with bf16-pretrained
models). Such models may overflow or underflow leading to NaN loss. If this is your case then you will want to use
the full fp32 mode, by explicitly disabling the otherwise default fp16 mixed precision mode with:
json
{
    ""fp16"": {
        ""enabled"": ""false"",
    }
}
If you're using the Ampere-architecture based GPU, pytorch version 1.7 and higher will automatically switch to using
the much more efficient tf32 format for some operations, but the results will still be in fp32. For details and
benchmarks, please, see TensorFloat-32(TF32) on Ampere devices. The document includes
instructions on how to disable this automatic conversion if for some reason you prefer not to use it.
With the 🤗 Trainer you can use --tf32 to enable it, or disable it with --tf32 0 or --no_tf32. By default the PyTorch default is used.

Automatic Mixed Precision
You can use automatic mixed precision with either a pytorch-like AMP way or the apex-like way:
fp16
To configure pytorch AMP-like mode with fp16 (float16) set:
json
{
    ""fp16"": {
        ""enabled"": ""auto"",
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    }
}
and the [Trainer] will automatically enable or disable it based on the value of
args.fp16_backend. The rest of config values are up to you.
This mode gets enabled when --fp16 --fp16_backend amp or --fp16_full_eval command line args are passed.
You can also enable/disable this mode explicitly:
json
{
    ""fp16"": {
        ""enabled"": true,
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    }
}
But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed
configuration.
Here is the documentation.
bf16
If bf16 (bfloat16) is desired instead of fp16 then the following configuration section is to be used:
json
{
    ""bf16"": {
        ""enabled"": ""auto""
    }
}
bf16 has the same dynamic range as fp32 and thus doesn't require loss scaling.
This mode gets enabled when --bf16 or --bf16_full_eval command line args are passed.
You can also enable/disable this mode explicitly:
json
{
    ""bf16"": {
        ""enabled"": true
    }
}

As of deepspeed==0.6.0 the bf16 support is new and experimental.
If you use gradient accumulation with bf16-enabled, you need to be aware that it'll accumulate gradients in bf16, which may not be what you want due to this format's low precision, as it may lead to a lossy accumulation.
A work is being done to fix that and provide an option to use a higher precision dtype (fp16 or fp32).

NCCL Collectives
There is the dtype of the training regime and there is a separate dtype that is used for communication collectives like various reduction and gathering/scattering operations.
All gather/scatter ops are performed in the same dtype the data is in, so if you're using bf16 training regime it gets gathered in bf16 - gathering is a non-lossy operation.
Various reduce operations can be quite lossy, for example when gradients are averaged across multiple-gpus, if the communications are done in fp16 or bf16 the outcome is likely be lossy - since when one ads multiple numbers in low precision the result isn't exact. More so with bf16 as it has a lower precision than fp16. Often fp16 is good enough as the loss is minimal when averaging grads which are typically very small. Therefore, by default for half precision training fp16 is used as the default for reduction operations. But you have full control over this functionality and if you choose you can add a small overhead and ensure that reductions will be using fp32 as the accumulation dtype and only when the result is ready it'll get downcast to the half precision dtype you're training in.
In order to override the default you simply add a new configuration entry:
json
{
    ""communication_data_type"": ""fp32""
}
The valid values as of this writing are ""fp16"", ""bfp16"", ""fp32"".
note: stage zero 3 had a bug with regards to bf16 comm dtype that was fixed in deepspeed==0.8.1
apex
To configure apex AMP-like mode set:
json
""amp"": {
    ""enabled"": ""auto"",
    ""opt_level"": ""auto""
}
and the [Trainer] will automatically configure it based on the values of args.fp16_backend and
args.fp16_opt_level.
This mode gets enabled when --fp16 --fp16_backend apex --fp16_opt_level 01 command line args are passed.
You can also configure this mode explicitly:
json
{
    ""amp"": {
        ""enabled"": true,
        ""opt_level"": ""O1""
    }
}
But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed
configuration.
Here is the documentation.

Batch Size
To configure batch size, use:
json
{
    ""train_batch_size"": ""auto"",
    ""train_micro_batch_size_per_gpu"": ""auto""
}
and the [Trainer] will automatically set train_micro_batch_size_per_gpu to the value of
args.per_device_train_batch_size and train_batch_size to args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps.
You can also set the values explicitly:
json
{
    ""train_batch_size"": 12,
    ""train_micro_batch_size_per_gpu"": 4
}
But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed
configuration.

Gradient Accumulation
To configure gradient accumulation set:
json
{
    ""gradient_accumulation_steps"": ""auto""
}
and the [Trainer] will automatically set it to the value of args.gradient_accumulation_steps.
You can also set the value explicitly:
json
{
    ""gradient_accumulation_steps"": 3
}
But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed
configuration.

Gradient Clipping
To configure gradient gradient clipping set:
json
{
    ""gradient_clipping"": ""auto""
}
and the [Trainer] will automatically set it to the value of args.max_grad_norm.
You can also set the value explicitly:
json
{
    ""gradient_clipping"": 1.0
}
But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed
configuration.

Getting The Model Weights Out
As long as you continue training and resuming using DeepSpeed you don't need to worry about anything. DeepSpeed stores
fp32 master weights in its custom checkpoint optimizer files, which are global_step*/*optim_states.pt (this is glob
pattern), and are saved under the normal checkpoint.
FP16 Weights:
When a model is saved under ZeRO-2, you end up having the normal pytorch_model.bin file with the model weights, but
they are only the fp16 version of the weights.
Under ZeRO-3, things are much more complicated, since the model weights are partitioned out over multiple GPUs,
therefore ""stage3_gather_16bit_weights_on_model_save"": true is required to get the Trainer to save the fp16
version of the weights. If this setting is False pytorch_model.bin won't be created. This is because by default DeepSpeed's state_dict contains a placeholder and not the real weights. If we were to save this state_dict it won't be possible to load it back.
json
{
    ""zero_optimization"": {
        ""stage3_gather_16bit_weights_on_model_save"": true
    }
}
FP32 Weights:
While the fp16 weights are fine for resuming training, if you finished finetuning your model and want to upload it to
the models hub or pass it to someone else you most likely will want to get the fp32
weights. This ideally shouldn't be done during training since this is a process that requires a lot of memory, and
therefore best to be performed offline after the training is complete. But if desired and you have plenty of free CPU
memory it can be done in the same training script. The following sections will discuss both approaches.
Live FP32 Weights Recovery:
This approach may not work if you model is large and you have little free CPU memory left, at the end of the training.
If you have saved at least one checkpoint, and you want to use the latest one, you can do the following:
thon
from transformers.trainer_utils import get_last_checkpoint
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
checkpoint_dir = get_last_checkpoint(trainer.args.output_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)

If you're using the --load_best_model_at_end class:~transformers.TrainingArguments argument (to track the best
checkpoint), then you can finish the training by first saving the final model explicitly and then do the same as above:
thon
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
checkpoint_dir = os.path.join(trainer.args.output_dir, ""checkpoint-final"")
trainer.deepspeed.save_checkpoint(checkpoint_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)

Note, that once load_state_dict_from_zero_checkpoint was run, the model will no longer be usable in the
DeepSpeed context of the same application. i.e. you will need to re-initialize the deepspeed engine, since
model.load_state_dict(state_dict) will remove all the DeepSpeed magic from it. So do this only at the very end
of the training.

Of course, you don't have to use class:~transformers.Trainer and you can adjust the examples above to your own
trainer.
If for some reason you want more refinement, you can also extract the fp32 state_dict of the weights and apply
these yourself as is shown in the following example:
thon
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir)  # already on cpu
model = model.cpu()
model.load_state_dict(state_dict)

Offline FP32 Weights Recovery:
DeepSpeed creates a special conversion script zero_to_fp32.py which it places in the top-level of the checkpoint
folder. Using this script you can extract the weights at any point. The script is standalone and you no longer need to
have the configuration file or a Trainer to do the extraction.
Let's say your checkpoint folder looks like this:

$ ls -l output_dir/checkpoint-1/
-rw-rw-r-- 1 stas stas 1.4K Mar 27 20:42 config.json
drwxrwxr-x 2 stas stas 4.0K Mar 25 19:52 global_step1/
-rw-rw-r-- 1 stas stas   12 Mar 27 13:16 latest
-rw-rw-r-- 1 stas stas 827K Mar 27 20:42 optimizer.pt
-rw-rw-r-- 1 stas stas 231M Mar 27 20:42 pytorch_model.bin
-rw-rw-r-- 1 stas stas  623 Mar 27 20:42 scheduler.pt
-rw-rw-r-- 1 stas stas 1.8K Mar 27 20:42 special_tokens_map.json
-rw-rw-r-- 1 stas stas 774K Mar 27 20:42 spiece.model
-rw-rw-r-- 1 stas stas 1.9K Mar 27 20:42 tokenizer_config.json
-rw-rw-r-- 1 stas stas  339 Mar 27 20:42 trainer_state.json
-rw-rw-r-- 1 stas stas 2.3K Mar 27 20:42 training_args.bin
-rwxrw-r-- 1 stas stas 5.5K Mar 27 13:16 zero_to_fp32.py*
In this example there is just one DeepSpeed checkpoint sub-folder global_step1. Therefore to reconstruct the fp32
weights just run:

python zero_to_fp32.py . pytorch_model.bin
This is it. pytorch_model.bin will now contain the full fp32 model weights consolidated from multiple GPUs.
The script will automatically be able to handle either a ZeRO-2 or ZeRO-3 checkpoint.
python zero_to_fp32.py -h will give you usage details.
The script will auto-discover the deepspeed sub-folder using the contents of the file latest, which in the current
example will contain global_step1.
Note: currently the script requires 2x general RAM of the final fp32 model weights.
ZeRO-3 and Infinity Nuances
ZeRO-3 is quite different from ZeRO-2 because of its param sharding feature.
ZeRO-Infinity further extends ZeRO-3 to support NVMe memory and multiple other speed and scalability improvements.
While all the efforts were made for things to just work without needing any special changes to your models, in certain
circumstances you may find the following information to be needed.
Constructing Massive Models
DeepSpeed/ZeRO-3 can handle models with Trillions of parameters which may not fit onto the existing RAM. In such cases,
but also if you want the initialization to happen much faster, initialize the model using deepspeed.zero.Init()
context manager (which is also a function decorator), like so:
thon
from transformers import T5ForConditionalGeneration, T5Config
import deepspeed
with deepspeed.zero.Init():
    config = T5Config.from_pretrained(""t5-small"")
    model = T5ForConditionalGeneration(config)

As you can see this gives you a randomly initialized model.
If you want to use a pretrained model, model_class.from_pretrained will activate this feature as long as
is_deepspeed_zero3_enabled() returns True, which currently is setup by the
[TrainingArguments] object if the passed DeepSpeed configuration file contains ZeRO-3 config
section. Thus you must create the [TrainingArguments] object before calling
from_pretrained. Here is an example of a possible sequence:
thon
from transformers import AutoModel, Trainer, TrainingArguments
training_args = TrainingArguments(, deepspeed=ds_config)
model = AutoModel.from_pretrained(""t5-small"")
trainer = Trainer(model=model, args=training_args, )

If you're using the official example scripts and your command line arguments include --deepspeed ds_config.json
with ZeRO-3 config enabled, then everything is already done for you, since this is how example scripts are written.
Note: If the fp16 weights of the model can't fit onto the memory of a single GPU this feature must be used.
For full details on this method and other related features please refer to Constructing Massive Models.
Also when loading fp16-pretrained models, you will want to tell from_pretrained to use
torch_dtype=torch.float16. For details, please, see from_pretrained-torch-dtype.
Gathering Parameters
Under ZeRO-3 on multiple GPUs no single GPU has all the parameters unless it's the parameters for the currently
executing layer. So if you need to access all parameters from all layers at once there is a specific method to do it.
Most likely you won't need it, but if you do please refer to Gathering Parameters
We do however use it internally in several places, one such example is when loading pretrained model weights in
from_pretrained. We load one layer at a time and immediately partition it to all participating GPUs, as for very
large models it won't be possible to load it on one GPU and then spread it out to multiple GPUs, due to memory
limitations.
Also under ZeRO-3, if you write your own code and run into a model parameter weight that looks like:
python
tensor([1.0], device=""cuda:0"", dtype=torch.float16, requires_grad=True)
stress on tensor([1.]), or if you get an error where it says the parameter is of size 1, instead of some much
larger multi-dimensional shape, this means that the parameter is partitioned and what you see is a ZeRO-3 placeholder.

ZeRO Inference
ZeRO Inference uses the same config as ZeRO-3 Training. You just don't need the optimizer and scheduler sections. In
fact you can leave these in the config file if you want to share the same one with the training. They will just be
ignored.
Otherwise you just need to pass the usual [TrainingArguments] arguments. For example:

deepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json
The only important thing is that you need to use a ZeRO-3 configuration, since ZeRO-2 provides no benefit whatsoever
for the inference as only ZeRO-3 performs sharding of parameters, whereas ZeRO-1 shards gradients and optimizer states.
Here is an example of running run_translation.py under DeepSpeed deploying all available GPUs:

deepspeed examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero3.json \
--model_name_or_path t5-small --output_dir output_dir \
--do_eval --max_eval_samples 50 --warmup_steps 50  \
--max_source_length 128 --val_max_target_length 128 \
--overwrite_output_dir --per_device_eval_batch_size 4 \
--predict_with_generate --dataset_config ""ro-en"" --fp16 \
--source_lang en --target_lang ro --dataset_name wmt16 \
--source_prefix ""translate English to Romanian: ""
Since for inference there is no need for additional large memory used by the optimizer states and the gradients you
should be able to fit much larger batches and/or sequence length onto the same hardware.
Additionally DeepSpeed is currently developing a related product called Deepspeed-Inference which has no relationship
to the ZeRO technology, but instead uses tensor parallelism to scale models that can't fit onto a single GPU. This is a
work in progress and we will provide the integration once that product is complete.
Memory Requirements
Since Deepspeed ZeRO can offload memory to CPU (and NVMe) the framework provides utils that allow one to tell how much CPU and GPU memory will be needed depending on the number of GPUs being used.
Let's estimate how much memory is needed to finetune ""bigscience/T0_3B"" on a single GPU:

$ python -c 'from transformers import AutoModel; \
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \
model = AutoModel.from_pretrained(""bigscience/T0_3B""); \
estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=1, num_nodes=1)'
[]
Estimated memory needed for params, optim states and gradients for a:
HW: Setup with 1 node, 1 GPU per node.
SW: Model with 2783M total params, 65M largest layer params.
  per CPU  |  per GPU |   Options
   70.00GB |   0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1
   70.00GB |   0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0
   62.23GB |   5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=1
   62.23GB |   5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=0
    0.37GB |  46.91GB | offload_param=none, offload_optimizer=none, zero_init=1
   15.56GB |  46.91GB | offload_param=none, offload_optimizer=none, zero_init=0
So you can fit it on a single 80GB GPU and no CPU offload, or a tiny 8GB GPU but then need ~60GB of CPU memory. (Remember this is just the memory for params, optimizer states and gradients - you will need a bit more memory for cuda kernels, activations and temps.)
Then it's a tradeoff of cost vs speed. It'll be cheaper to buy/rent a smaller GPU (or less GPUs since you can use multiple GPUs with Deepspeed ZeRO. But then it'll be slower, so even if you don't care about how fast something will be done, the slowdown has a direct impact on the duration of using the GPU and thus bigger cost. So experiment and compare which works the best.
If you have enough GPU memory make sure to disable the CPU/NVMe offload as it'll make everything faster.
For example, let's repeat the same for 2 GPUs:
```bash
$ python -c 'from transformers import AutoModel; \
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \
model = AutoModel.from_pretrained(""bigscience/T0_3B""); \
estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=2, num_nodes=1)'
[]
Estimated memory needed for params, optim states and gradients for a:
HW: Setup with 1 node, 2 GPUs per node.
SW: Model with 2783M total params, 65M largest layer params.
  per CPU  |  per GPU |   Options
   70.00GB |   0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1
   70.00GB |   0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0
   62.23GB |   2.84GB | offload_param=none, offload_optimizer=cpu , zero_init=1
   62.23GB |   2.84GB | offload_param=none, offload_optimizer=cpu , zero_init=0
    0.74GB |  23.58GB | offload_param=none, offload_optimizer=none, zero_init=1
   31.11GB |  23.58GB | offload_param=none, offload_optimizer=none, zero_init=0

So here you'd want 2x 32GB GPUs or higher without offloading to CPU.
For full information please see memory estimators.
Filing Issues
Here is how to file an issue so that we could quickly get to the bottom of the issue and help you to unblock your work.
In your report please always include:

the full Deepspeed config file in the report

either the command line arguments if you were using the [Trainer] or
   [TrainingArguments] arguments if you were scripting the Trainer setup yourself. Please do not
   dump the [TrainingArguments] as it has dozens of entries that are irrelevant.

Output of:

python -c 'import torch; print(f""torch: {torch.__version__}"")'
python -c 'import transformers; print(f""transformers: {transformers.__version__}"")'
python -c 'import deepspeed; print(f""deepspeed: {deepspeed.__version__}"")'

If possible include a link to a Google Colab notebook that we can reproduce the problem with. You can use this
   notebook as
   a starting point.

Unless it's impossible please always use a standard dataset that we can use and not something custom.

If possible try to use one of the existing examples to reproduce the problem with.

Things to consider:

Deepspeed is often not the cause of the problem.

Some of the filed issues proved to be Deepspeed-unrelated. That is once Deepspeed was removed from the setup, the
  problem was still there.
Therefore, if it's not absolutely obvious it's a DeepSpeed-related problem, as in you can see that there is an
  exception and you can see that DeepSpeed modules are involved, first re-test your setup without DeepSpeed in it.
  And only if the problem persists then do mentioned Deepspeed and supply all the required details.

If it's clear to you that the issue is in the DeepSpeed core and not the integration part, please file the Issue
  directly with Deepspeed. If you aren't sure, please do not worry,
  either Issue tracker will do, we will figure it out once you posted it and redirect you to another Issue tracker if
  need be.

Troubleshooting
the deepspeed process gets killed at startup without a traceback
If the deepspeed process gets killed at launch time without a traceback, that usually means that the program tried
to allocate more CPU memory than your system has or your process is allowed to allocate and the OS kernel killed that
process. This is because your configuration file most likely has either offload_optimizer or offload_param or
both configured to offload to cpu. If you have NVMe, experiment with offloading to NVMe if you're running under
ZeRO-3. Here is how you can estimate how much memory is needed for a specific model.
training and/or eval/predict loss is NaN
This often happens when one takes a model pre-trained in bf16 mixed precision mode and tries to use it under fp16 (with or without mixed precision). Most models trained on TPU and often the ones released by Google are in this category (e.g. almost all t5-based models). Here the solution is to either use fp32 or bf16 if your hardware supports it (TPU, Ampere GPUs or newer).
The other problem may have to do with using fp16. When you configure this section:
json
{
    ""fp16"": {
        ""enabled"": ""auto"",
        ""loss_scale"": 0,
        ""loss_scale_window"": 1000,
        ""initial_scale_power"": 16,
        ""hysteresis"": 2,
        ""min_loss_scale"": 1
    }
}
and you see in your log that Deepspeed reports OVERFLOW! as follows:
0%|                                                                                                                             | 0/189 [00:00<?, ?it/s]
 [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 262144
  1%|▌                                                                                                                    | 1/189 [00:00<01:26,  2.17it/s]
 [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 131072.0
  1%|█▏
 []
 [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
 14%|████████████████▌                                                                                                   | 27/189 [00:14<01:13,  2.21it/s]
 [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
 15%|█████████████████▏                                                                                                  | 28/189 [00:14<01:13,  2.18it/s]
 [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
 15%|█████████████████▊                                                                                                  | 29/189 [00:15<01:13,  2.18it/s]
 [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
[]
that means that the Deepspeed loss scaler can't figure out a scaling co-efficient that overcomes loss overflow.
(the log was massaged to be more readable here.)
In this case you usually need to raise the value of initial_scale_power. Setting it to ""initial_scale_power"": 32 will typically resolve the problem.
Notes

DeepSpeed works with the PyTorch [Trainer] but not TF [TFTrainer].
While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from source to best match your hardware and also if you need to enable
  certain features, like 1-bit Adam, which aren't available in the pypi distribution.
You don't have to use the [Trainer] to use DeepSpeed with 🤗 Transformers - you can use any model
  with your own trainer, and you will have to adapt the latter according to the DeepSpeed integration instructions.

Non-Trainer Deepspeed Integration
The [~deepspeed.HfDeepSpeedConfig] is used to integrate Deepspeed into the 🤗 Transformers core
functionality, when [Trainer] is not used. The only thing that it does is handling Deepspeed ZeRO-3 param gathering and automatically splitting the model onto multiple gpus during from_pretrained call. Everything else you have to do by yourself.
When using [Trainer] everything is automatically taken care of.
When not using [Trainer], to efficiently deploy DeepSpeed ZeRO-3, you must instantiate the
[~deepspeed.HfDeepSpeedConfig] object before instantiating the model and keep that object alive.
If you're using Deepspeed ZeRO-1 or ZeRO-2 you don't need to use HfDeepSpeedConfig at all.
For example for a pretrained model:
thon
from transformers.deepspeed import HfDeepSpeedConfig
from transformers import AutoModel
import deepspeed
ds_config = {}  # deepspeed config object or path to the file
must run before instantiating the model to detect zero 3
dschf = HfDeepSpeedConfig(ds_config)  # keep this object alive
model = AutoModel.from_pretrained(""gpt2"")
engine = deepspeed.initialize(model=model, config_params=ds_config, )

or for non-pretrained model:
thon
from transformers.deepspeed import HfDeepSpeedConfig
from transformers import AutoModel, AutoConfig
import deepspeed
ds_config = {}  # deepspeed config object or path to the file
must run before instantiating the model to detect zero 3
dschf = HfDeepSpeedConfig(ds_config)  # keep this object alive
config = AutoConfig.from_pretrained(""gpt2"")
model = AutoModel.from_config(config)
engine = deepspeed.initialize(model=model, config_params=ds_config, )

Please note that if you're not using the [Trainer] integration, you're completely on your own. Basically follow the documentation on the Deepspeed website. Also you have to configure explicitly the config file - you can't use ""auto"" values and you will have to put real values instead.
HfDeepSpeedConfig
[[autodoc]] deepspeed.HfDeepSpeedConfig
    - all
Custom DeepSpeed ZeRO Inference
Here is an example of how one could do DeepSpeed ZeRO Inference without using [Trainer] when one can't fit a model onto a single GPU. The solution includes using additional GPUs or/and offloading GPU memory to CPU memory.
The important nuance to understand here is that the way ZeRO is designed you can process different inputs on different GPUs in parallel.
The example has copious notes and is self-documenting.
Make sure to:

disable CPU offload if you have enough GPU memory (since it slows things down)
enable bf16 if you own an Ampere or a newer GPU to make things faster. If you don't have that hardware you may enable fp16 as long as you don't use any model that was pre-trained in bf16 mixed precision (such as most t5 models). These usually overflow in fp16 and you will see garbage as output.

thon
!/usr/bin/env python
This script demonstrates how to use Deepspeed ZeRO in an inference mode when one can't fit a model
into a single GPU

1. Use 1 GPU with CPU offload
2. Or use multiple GPUs instead

First you need to install deepspeed: pip install deepspeed

Here we use a 3B ""bigscience/T0_3B"" model which needs about 15GB GPU RAM - so 1 largish or 2
small GPUs can handle it. or 1 small GPU and a lot of CPU memory.

To use a larger model like ""bigscience/T0"" which needs about 50GB, unless you have an 80GB GPU -
you will need 2-4 gpus. And then you can adapt the script to handle more gpus if you want to
process multiple inputs at once.

The provided deepspeed config also activates CPU memory offloading, so chances are that if you
have a lot of available CPU memory and you don't mind a slowdown you should be able to load a
model that doesn't normally fit into a single GPU. If you have enough GPU memory the program will
run faster if you don't want offload to CPU - so disable that section then.

To deploy on 1 gpu:

deepspeed --num_gpus 1 t0.py
or:
python -m torch.distributed.run --nproc_per_node=1 t0.py

To deploy on 2 gpus:

deepspeed --num_gpus 2 t0.py
or:
python -m torch.distributed.run --nproc_per_node=2 t0.py
from transformers import AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM
from transformers.deepspeed import HfDeepSpeedConfig
import deepspeed
import os
import torch
os.environ[""TOKENIZERS_PARALLELISM""] = ""false""  # To avoid warnings about parallelism in tokenizers
distributed setup
local_rank = int(os.getenv(""LOCAL_RANK"", ""0""))
world_size = int(os.getenv(""WORLD_SIZE"", ""1""))
torch.cuda.set_device(local_rank)
deepspeed.init_distributed()
model_name = ""bigscience/T0_3B""
config = AutoConfig.from_pretrained(model_name)
model_hidden_size = config.d_model
batch size has to be divisible by world_size, but can be bigger than world_size
train_batch_size = 1 * world_size
ds_config notes

- enable bf16 if you use Ampere or higher GPU - this will run in mixed precision and will be
faster.

- for older GPUs you can enable fp16, but it'll only work for non-bf16 pretrained models - e.g.
all official t5 models are bf16-pretrained

- set offload_param.device to ""none"" or completely remove the offload_param section if you don't
- want CPU offload

- if using offload_param you can manually finetune stage3_param_persistence_threshold to control
- which params should remain on gpus - the larger the value the smaller the offload size

For indepth info on Deepspeed config see
https://huggingface.co/docs/transformers/main/main_classes/deepspeed
keeping the same format as json for consistency, except it uses lower case for true/false
fmt: off
ds_config = {
    ""fp16"": {
        ""enabled"": False
    },
    ""bf16"": {
        ""enabled"": False
    },
    ""zero_optimization"": {
        ""stage"": 3,
        ""offload_param"": {
            ""device"": ""cpu"",
            ""pin_memory"": True
        },
        ""overlap_comm"": True,
        ""contiguous_gradients"": True,
        ""reduce_bucket_size"": model_hidden_size * model_hidden_size,
        ""stage3_prefetch_bucket_size"": 0.9 * model_hidden_size * model_hidden_size,
        ""stage3_param_persistence_threshold"": 10 * model_hidden_size
    },
    ""steps_per_print"": 2000,
    ""train_batch_size"": train_batch_size,
    ""train_micro_batch_size_per_gpu"": 1,
    ""wall_clock_breakdown"": False
}
fmt: on
next line instructs transformers to partition the model directly over multiple gpus using
deepspeed.zero.Init when model's from_pretrained method is called.

it has to be run before loading the model AutoModelForSeq2SeqLM.from_pretrained(model_name)

otherwise the model will first be loaded normally and only partitioned at forward time which is
less efficient and when there is little CPU RAM may fail
dschf = HfDeepSpeedConfig(ds_config)  # keep this object alive
now a model can be loaded.
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
initialise Deepspeed ZeRO and store only the engine object
ds_engine = deepspeed.initialize(model=model, config_params=ds_config)[0]
ds_engine.module.eval()  # inference
Deepspeed ZeRO can process unrelated inputs on each GPU. So for 2 gpus you process 2 inputs at once.
If you use more GPUs adjust for more.
And of course if you have just one input to process you then need to pass the same string to both gpus
If you use only one GPU, then you will have only rank 0.
rank = torch.distributed.get_rank()
if rank == 0:
    text_in = ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy""
elif rank == 1:
    text_in = ""Is this review positive or negative? Review: this is the worst restaurant ever""
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer.encode(text_in, return_tensors=""pt"").to(device=local_rank)
with torch.no_grad():
    outputs = ds_engine.module.generate(inputs, synced_gpus=True)
text_out = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f""rank{rank}:\n   in={text_in}\n  out={text_out}"")

Let's save it as t0.py and run it:
$ deepspeed --num_gpus 2 t0.py
rank0:
   in=Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy
  out=Positive
rank1:
   in=Is this review positive or negative? Review: this is the worst restaurant ever
  out=negative
This was a very basic example and you will want to adapt it to your needs.
generate nuances
When using multiple GPUs with ZeRO Stage-3, one has to synchronize the GPUs by calling generate(, synced_gpus=True). If this is not done if one GPU finished generating before other GPUs the whole system will hang as the rest of the GPUs will not be able to received the shard of weights from the GPU that stopped generating.
Starting from transformers>=4.28, if synced_gpus isn't explicitly specified, it'll be set to True automatically if these conditions are detected. But you can still override the value of synced_gpus if need to.
Testing Deepspeed Integration
If you submit a PR that involves DeepSpeed integration please note our CircleCI PR CI setup has no GPUs, so we only run tests requiring gpus on a different CI nightly. Therefore if you get a green CI report in your PR it doesn't mean DeepSpeed tests pass.
To run DeepSpeed tests, please run at least:
RUN_SLOW=1 pytest tests/deepspeed/test_deepspeed.py
If you changed any of the modeling or pytorch examples code, then run the model zoo tests as well. The following will run all DeepSpeed tests:
RUN_SLOW=1 pytest tests/deepspeed
Main DeepSpeed Resources

Project's github
Usage docs
API docs
Blog posts

Papers:

ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
ZeRO-Offload: Democratizing Billion-Scale Model Training
ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning

Finally, please, remember that, HuggingFace [Trainer] only integrates DeepSpeed, therefore if you
have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub."
"
Image Processor
An image processor is in charge of preparing input features for vision models and post processing their outputs. This includes transformations such as resizing, normalization, and conversion to PyTorch, TensorFlow, Flax and Numpy tensors. It may also include model specific post-processing such as converting logits to segmentation masks.
ImageProcessingMixin
[[autodoc]] image_processing_utils.ImageProcessingMixin
    - from_pretrained
    - save_pretrained
BatchFeature
[[autodoc]] BatchFeature
BaseImageProcessor
[[autodoc]] image_processing_utils.BaseImageProcessor"
"
Sharing custom models
The 🤗 Transformers library is designed to be easily extensible. Every model is fully coded in a given subfolder
of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs.
If you are writing a brand new model, it might be easier to start from scratch. In this tutorial, we will show you
how to write a custom model and its configuration so it can be used inside Transformers, and how you can share it
with the community (with the code it relies on) so that anyone can use it, even if it's not present in the 🤗
Transformers library.
We will illustrate all of this on a ResNet model, by wrapping the ResNet class of the
timm library into a [PreTrainedModel].
Writing a custom configuration
Before we dive into the model, let's first write its configuration. The configuration of a model is an object that
will contain all the necessary information to build the model. As we will see in the next section, the model can only
take a config to be initialized, so we really need that object to be as complete as possible.
In our example, we will take a couple of arguments of the ResNet class that we might want to tweak. Different
configurations will then give us the different types of ResNets that are possible. We then just store those arguments,
after checking the validity of a few of them.
thon
from transformers import PretrainedConfig
from typing import List
class ResnetConfig(PretrainedConfig):
    model_type = ""resnet""
def __init__(
    self,
    block_type=""bottleneck"",
    layers: List[int] = [3, 4, 6, 3],
    num_classes: int = 1000,
    input_channels: int = 3,
    cardinality: int = 1,
    base_width: int = 64,
    stem_width: int = 64,
    stem_type: str = """",
    avg_down: bool = False,
    **kwargs,
):
    if block_type not in [""basic"", ""bottleneck""]:
        raise ValueError(f""`block_type` must be 'basic' or bottleneck', got {block_type}."")
    if stem_type not in ["""", ""deep"", ""deep-tiered""]:
        raise ValueError(f""`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}."")

    self.block_type = block_type
    self.layers = layers
    self.num_classes = num_classes
    self.input_channels = input_channels
    self.cardinality = cardinality
    self.base_width = base_width
    self.stem_width = stem_width
    self.stem_type = stem_type
    self.avg_down = avg_down
    super().__init__(**kwargs)

The three important things to remember when writing you own configuration are the following:
- you have to inherit from PretrainedConfig,
- the __init__ of your PretrainedConfig must accept any kwargs,
- those kwargs need to be passed to the superclass __init__.
The inheritance is to make sure you get all the functionality from the 🤗 Transformers library, while the two other
constraints come from the fact a PretrainedConfig has more fields than the ones you are setting. When reloading a
config with the from_pretrained method, those fields need to be accepted by your config and then sent to the
superclass.
Defining a model_type for your configuration (here model_type=""resnet"") is not mandatory, unless you want to
register your model with the auto classes (see last section).
With this done, you can easily create and save your configuration like you would do with any other model config of the
library. Here is how we can create a resnet50d config and save it:
py
resnet50d_config = ResnetConfig(block_type=""bottleneck"", stem_width=32, stem_type=""deep"", avg_down=True)
resnet50d_config.save_pretrained(""custom-resnet"")
This will save a file named config.json inside the folder custom-resnet. You can then reload your config with the
from_pretrained method:
py
resnet50d_config = ResnetConfig.from_pretrained(""custom-resnet"")
You can also use any other method of the [PretrainedConfig] class, like [~PretrainedConfig.push_to_hub] to
directly upload your config to the Hub.
Writing a custom model
Now that we have our ResNet configuration, we can go on writing the model. We will actually write two: one that
extracts the hidden features from a batch of images (like [BertModel]) and one that is suitable for image
classification (like [BertForSequenceClassification]).
As we mentioned before, we'll only write a loose wrapper of the model to keep it simple for this example. The only
thing we need to do before writing this class is a map between the block types and actual block classes. Then the
model is defined from the configuration by passing everything to the ResNet class:

from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet
from .configuration_resnet import ResnetConfig
BLOCK_MAPPING = {""basic"": BasicBlock, ""bottleneck"": Bottleneck}
class ResnetModel(PreTrainedModel):
    config_class = ResnetConfig
def __init__(self, config):
    super().__init__(config)
    block_layer = BLOCK_MAPPING[config.block_type]
    self.model = ResNet(
        block_layer,
        config.layers,
        num_classes=config.num_classes,
        in_chans=config.input_channels,
        cardinality=config.cardinality,
        base_width=config.base_width,
        stem_width=config.stem_width,
        stem_type=config.stem_type,
        avg_down=config.avg_down,
    )

def forward(self, tensor):
    return self.model.forward_features(tensor)

For the model that will classify images, we just change the forward method:

import torch
class ResnetModelForImageClassification(PreTrainedModel):
    config_class = ResnetConfig
def __init__(self, config):
    super().__init__(config)
    block_layer = BLOCK_MAPPING[config.block_type]
    self.model = ResNet(
        block_layer,
        config.layers,
        num_classes=config.num_classes,
        in_chans=config.input_channels,
        cardinality=config.cardinality,
        base_width=config.base_width,
        stem_width=config.stem_width,
        stem_type=config.stem_type,
        avg_down=config.avg_down,
    )

def forward(self, tensor, labels=None):
    logits = self.model(tensor)
    if labels is not None:
        loss = torch.nn.cross_entropy(logits, labels)
        return {""loss"": loss, ""logits"": logits}
    return {""logits"": logits}

In both cases, notice how we inherit from PreTrainedModel and call the superclass initialization with the config
(a bit like when you write a regular torch.nn.Module). The line that sets the config_class is not mandatory, unless
you want to register your model with the auto classes (see last section).

If your model is very similar to a model inside the library, you can re-use the same configuration as this model.

You can have your model return anything you want, but returning a dictionary like we did for
ResnetModelForImageClassification, with the loss included when labels are passed, will make your model directly
usable inside the [Trainer] class. Using another output format is fine as long as you are planning on using your own
training loop or another library for training.
Now that we have our model class, let's create one:
py
resnet50d = ResnetModelForImageClassification(resnet50d_config)
Again, you can use any of the methods of [PreTrainedModel], like [~PreTrainedModel.save_pretrained] or
[~PreTrainedModel.push_to_hub]. We will use the second in the next section, and see how to push the model weights
with the code of our model. But first, let's load some pretrained weights inside our model.
In your own use case, you will probably be training your custom model on your own data. To go fast for this tutorial,
we will use the pretrained version of the resnet50d. Since our model is just a wrapper around it, it's going to be
easy to transfer those weights:

import timm
pretrained_model = timm.create_model(""resnet50d"", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())

Now let's see how to make sure that when we do [~PreTrainedModel.save_pretrained] or [~PreTrainedModel.push_to_hub], the
code of the model is saved.
Sending the code to the Hub

This API is experimental and may have some slight breaking changes in the next releases.

First, make sure your model is fully defined in a .py file. It can rely on relative imports to some other files as
long as all the files are in the same directory (we don't support submodules for this feature yet). For our example,
we'll define a modeling_resnet.py file and a configuration_resnet.py file in a folder of the current working
directory named resnet_model. The configuration file contains the code for ResnetConfig and the modeling file
contains the code of ResnetModel and ResnetModelForImageClassification.
.
└── resnet_model
    ├── __init__.py
    ├── configuration_resnet.py
    └── modeling_resnet.py
The __init__.py can be empty, it's just there so that Python detects resnet_model can be use as a module.

If copying a modeling files from the library, you will need to replace all the relative imports at the top of the file
to import from the transformers package.

Note that you can re-use (or subclass) an existing configuration/model.
To share your model with the community, follow those steps: first import the ResNet model and config from the newly
created files:
py
from resnet_model.configuration_resnet import ResnetConfig
from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification
Then you have to tell the library you want to copy the code files of those objects when using the save_pretrained
method and properly register them with a given Auto class (especially for models), just run:
py
ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class(""AutoModel"")
ResnetModelForImageClassification.register_for_auto_class(""AutoModelForImageClassification"")
Note that there is no need to specify an auto class for the configuration (there is only one auto class for them,
[AutoConfig]) but it's different for models. Your custom model could be suitable for many different tasks, so you
have to specify which one of the auto classes is the correct one for your model.
Next, let's create the config and models as we did before:

resnet50d_config = ResnetConfig(block_type=""bottleneck"", stem_width=32, stem_type=""deep"", avg_down=True)
resnet50d = ResnetModelForImageClassification(resnet50d_config)
pretrained_model = timm.create_model(""resnet50d"", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())

Now to send the model to the Hub, make sure you are logged in. Either run in your terminal:

huggingface-cli login
or from a notebook:

from huggingface_hub import notebook_login
notebook_login()

You can then push to your own namespace (or an organization you are a member of) like this:
py
resnet50d.push_to_hub(""custom-resnet50d"")
On top of the modeling weights and the configuration in json format, this also copied the modeling and
configuration .py files in the folder custom-resnet50d and uploaded the result to the Hub. You can check the result
in this model repo.
See the sharing tutorial for more information on the push to Hub method.
Using a model with custom code
You can use any configuration, model or tokenizer with custom code files in its repository with the auto-classes and
the from_pretrained method. All files and code uploaded to the Hub are scanned for malware (refer to the Hub security documentation for more information), but you should still 
review the model code and author to avoid executing malicious code on your machine. Set trust_remote_code=True to use
a model with custom code:

from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained(""sgugger/custom-resnet50d"", trust_remote_code=True)

It is also strongly encouraged to pass a commit hash as a revision to make sure the author of the models did not
update the code with some malicious new lines (unless you fully trust the authors of the models).
py
commit_hash = ""ed94a7c6247d8aedce4647f00f20de6875b5b292""
model = AutoModelForImageClassification.from_pretrained(
    ""sgugger/custom-resnet50d"", trust_remote_code=True, revision=commit_hash
)
Note that when browsing the commit history of the model repo on the Hub, there is a button to easily copy the commit
hash of any commit.
Registering a model with custom code to the auto classes
If you are writing a library that extends 🤗 Transformers, you may want to extend the auto classes to include your own
model. This is different from pushing the code to the Hub in the sense that users will need to import your library to
get the custom models (contrarily to automatically downloading the model code from the Hub).
As long as your config has a model_type attribute that is different from existing model types, and that your model
classes have the right config_class attributes, you can just add them to the auto classes likes this:

from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
AutoConfig.register(""resnet"", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)

Note that the first argument used when registering your custom config to [AutoConfig] needs to match the model_type
of your custom config, and the first argument used when registering your custom models to any auto model class needs
to match the config_class of those models."
"
Training on TPU with TensorFlow

If you don't need long explanations and just want TPU code samples to get started with, check out our TPU example notebook!

What is a TPU?
A TPU is a Tensor Processing Unit. They are hardware designed by Google, which are used to greatly speed up the tensor computations within neural networks, much like GPUs. They can be used for both network training and inference. They are generally accessed through Google’s cloud services, but small TPUs can also be accessed directly for free through Google Colab and Kaggle Kernels.
Because all TensorFlow models in 🤗 Transformers are Keras models, most of the methods in this document are generally applicable to TPU training for any Keras model! However, there are a few points that are specific to the HuggingFace ecosystem (hug-o-system?) of Transformers and Datasets, and we’ll make sure to flag them up when we get to them.
What kinds of TPU are available?
New users are often very confused by the range of TPUs, and the different ways to access them. The first key distinction to understand is the difference between TPU Nodes and TPU VMs.
When you use a TPU Node, you are effectively indirectly accessing a remote TPU. You will need a separate VM, which will initialize your network and data pipeline and then forward them to the remote node. When you use a TPU on Google Colab, you are accessing it in the TPU Node style.
Using TPU Nodes can have some quite unexpected behaviour for people who aren’t used to them! In particular, because the TPU is located on a physically different system to the machine you’re running your Python code on, your data cannot be local to your machine - any data pipeline that loads from your machine’s internal storage will totally fail! Instead, data must be stored in Google Cloud Storage where your data pipeline can still access it, even when the pipeline is running on the remote TPU node.

If you can fit all your data in memory as np.ndarray or tf.Tensor, then you can fit() on that data even when using Colab or a TPU Node, without needing to upload it to Google Cloud Storage.

🤗Specific Hugging Face Tip🤗: The methods Dataset.to_tf_dataset() and its higher-level wrapper model.prepare_tf_dataset() , which you will see throughout our TF code examples, will both fail on a TPU Node. The reason for this is that even though they create a tf.data.Dataset it is not a “pure” tf.data pipeline and uses tf.numpy_function or Dataset.from_generator() to stream data from the underlying HuggingFace Dataset. This HuggingFace Dataset is backed by data that is on a local disc and which the remote TPU Node will not be able to read.

The second way to access a TPU is via a TPU VM. When using a TPU VM, you connect directly to the machine that the TPU is attached to, much like training on a GPU VM. TPU VMs are generally easier to work with, particularly when it comes to your data pipeline. All of the above warnings do not apply to TPU VMs!
This is an opinionated document, so here’s our opinion: Avoid using TPU Node if possible. It is more confusing and more difficult to debug than TPU VMs. It is also likely to be unsupported in future - Google’s latest TPU, TPUv4, can only be accessed as a TPU VM, which suggests that TPU Nodes are increasingly going to become a “legacy” access method. However, we understand that the only free TPU access is on Colab and Kaggle Kernels, which uses TPU Node - so we’ll try to explain how to handle it if you have to! Check the TPU example notebook for code samples that explain this in more detail.
What sizes of TPU are available?
A single TPU (a v2-8/v3-8/v4-8) runs 8 replicas. TPUs exist in pods that can run hundreds or thousands of replicas simultaneously. When you use more than a single TPU but less than a whole pod (for example, a v3-32), your TPU fleet is referred to as a pod slice.
When you access a free TPU via Colab, you generally get a single v2-8 TPU.
I keep hearing about this XLA thing. What’s XLA, and how does it relate to TPUs?
XLA is an optimizing compiler, used by both TensorFlow and JAX. In JAX it is the only compiler, whereas in TensorFlow it is optional (but mandatory on TPU!). The easiest way to enable it when training a Keras model is to pass the argument jit_compile=True to model.compile(). If you don’t get any errors and performance is good, that’s a great sign that you’re ready to move to TPU!
Debugging on TPU is generally a bit harder than on CPU/GPU, so we recommend getting your code running on CPU/GPU with XLA first before trying it on TPU. You don’t have to train for long, of course - just for a few steps to make sure that your model and data pipeline are working like you expect them to.

XLA compiled code is usually faster - so even if you’re not planning to run on TPU, adding jit_compile=True can improve your performance. Be sure to note the caveats below about XLA compatibility, though!

Tip born of painful experience: Although using jit_compile=True is a good way to get a speed boost and test if your CPU/GPU code is XLA-compatible, it can actually cause a lot of problems if you leave it in when actually training on TPU. XLA compilation will happen implicitly on TPU, so remember to remove that line before actually running your code on a TPU!

How do I make my model XLA compatible?
In many cases, your code is probably XLA-compatible already! However, there are a few things that work in normal TensorFlow that don’t work in XLA. We’ve distilled them into three core rules below:

🤗Specific HuggingFace Tip🤗: We’ve put a lot of effort into rewriting our TensorFlow models and loss functions to be XLA-compatible. Our models and loss functions generally obey rule #1 and #2 by default, so you can skip over them if you’re using transformers models. Don’t forget about these rules when writing your own models and loss functions, though!

XLA Rule #1: Your code cannot have “data-dependent conditionals”
What that means is that any if statement cannot depend on values inside a tf.Tensor. For example, this code block cannot be compiled with XLA!
python
if tf.reduce_sum(tensor) > 10:
    tensor = tensor / 2.0
This might seem very restrictive at first, but most neural net code doesn’t need to do this. You can often get around this restriction by using tf.cond (see the documentation here) or by removing the conditional and finding a clever math trick with indicator variables instead, like so:
python
sum_over_10 = tf.cast(tf.reduce_sum(tensor) > 10, tf.float32)
tensor = tensor / (1.0 + sum_over_10)
This code has exactly the same effect as the code above, but by avoiding a conditional, we ensure it will compile with XLA without problems!
XLA Rule #2: Your code cannot have “data-dependent shapes”
What this means is that the shape of all of the tf.Tensor objects in your code cannot depend on their values. For example, the function tf.unique cannot be compiled with XLA, because it returns a tensor containing one instance of each unique value in the input. The shape of this output will obviously be different depending on how repetitive the input Tensor was, and so XLA refuses to handle it!
In general, most neural network code obeys rule #2 by default. However, there are a few common cases where it becomes a problem. One very common one is when you use label masking, setting your labels to a negative value to indicate that those positions should be ignored when computing the loss. If you look at NumPy or PyTorch loss functions that support label masking, you will often see code like this that uses boolean indexing:
python
label_mask = labels >= 0
masked_outputs = outputs[label_mask]
masked_labels = labels[label_mask]
loss = compute_loss(masked_outputs, masked_labels)
mean_loss = torch.mean(loss)
This code is totally fine in NumPy or PyTorch, but it breaks in XLA! Why? Because the shape of masked_outputs and masked_labels depends on how many positions are masked - that makes it a data-dependent shape. However, just like for rule #1, we can often rewrite this code to yield exactly the same output without any data-dependent shapes.
python
label_mask = tf.cast(labels >= 0, tf.float32)
loss = compute_loss(outputs, labels)
loss = loss * label_mask  # Set negative label positions to 0
mean_loss = tf.reduce_sum(loss) / tf.reduce_sum(label_mask)
Here, we avoid data-dependent shapes by computing the loss for every position, but zeroing out the masked positions in both the numerator and denominator when we calculate the mean, which yields exactly the same result as the first block while maintaining XLA compatibility. Note that we use the same trick as in rule #1 - converting a tf.bool to tf.float32 and using it as an indicator variable. This is a really useful trick, so remember it if you need to convert your own code to XLA!
XLA Rule #3: XLA will need to recompile your model for every different input shape it sees
This is the big one. What this means is that if your input shapes are very variable, XLA will have to recompile your model over and over, which will create huge performance problems. This commonly arises in NLP models, where input texts have variable lengths after tokenization. In other modalities, static shapes are more common and this rule is much less of a problem.
How can you get around rule #3? The key is padding - if you pad all your inputs to the same length, and then use an attention_mask, you can get the same results as you’d get from variable shapes, but without any XLA issues. However, excessive padding can cause severe slowdown too - if you pad all your samples to the maximum length in the whole dataset, you might end up with batches consisting endless padding tokens, which will waste a lot of compute and memory!
There isn’t a perfect solution to this problem. However, you can try some tricks. One very useful trick is to pad batches of samples up to a multiple of a number like 32 or 64 tokens. This often only increases the number of tokens by a small amount, but it hugely reduces the number of unique input shapes, because every input shape now has to be a multiple of 32 or 64. Fewer unique input shapes means fewer XLA compilations!

🤗Specific HuggingFace Tip🤗: Our tokenizers and data collators have methods that can help you here. You can use padding=""max_length"" or padding=""longest"" when calling tokenizers to get them to output padded data. Our tokenizers and data collators also have a pad_to_multiple_of argument that you can use to reduce the number of unique input shapes you see!

How do I actually train my model on TPU?
Once your training is XLA-compatible and (if you’re using TPU Node / Colab) your dataset has been prepared appropriately, running on TPU is surprisingly easy! All you really need to change in your code is to add a few lines to initialize your TPU, and to ensure that your model and dataset are created inside a TPUStrategy scope. Take a look at our TPU example notebook to see this in action!
Summary
There was a lot in here, so let’s summarize with a quick checklist you can follow when you want to get your model ready for TPU training:

Make sure your code follows the three rules of XLA
Compile your model with jit_compile=True on CPU/GPU and confirm that you can train it with XLA
Either load your dataset into memory or use a TPU-compatible dataset loading approach (see notebook)
Migrate your code either to Colab (with accelerator set to “TPU”) or a TPU VM on Google Cloud
Add TPU initializer code (see notebook)
Create your TPUStrategy and make sure dataset loading and model creation are inside the strategy.scope() (see notebook)
Don’t forget to take jit_compile=True out again when you move to TPU!
🙏🙏🙏🥺🥺🥺
Call model.fit()
You did it!
"
"
Quick tour
[[open-in-colab]]
Get up and running with 🤗 Transformers! Whether you're a developer or an everyday user, this quick tour will help you get started and show you how to use the [pipeline] for inference, load a pretrained model and preprocessor with an AutoClass, and quickly train a model with PyTorch or TensorFlow. If you're a beginner, we recommend checking out our tutorials or course next for more in-depth explanations of the concepts introduced here.
Before you begin, make sure you have all the necessary libraries installed:

!pip install transformers datasets
You'll also need to install your preferred machine learning framework:

pip install torch

pip install tensorflow

Pipeline

The [pipeline] is the easiest and fastest way to use a pretrained model for inference. You can use the [pipeline] out-of-the-box for many tasks across different modalities, some of which are shown in the table below:

For a complete list of available tasks, check out the pipeline API reference.

| Task                     | Description                                                                                              | Modality    | Pipeline identifier                       |
|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|-----------------------------------------------|
| Text classification          | assign a label to a given sequence of text                                                                   | NLP             | pipeline(task=“sentiment-analysis”)           |
| Text generation              | generate text given a prompt                                                                                 | NLP             | pipeline(task=“text-generation”)              |
| Summarization                | generate a summary of a sequence of text or document                                                         | NLP             | pipeline(task=“summarization”)                |
| Image classification         | assign a label to an image                                                                                   | Computer vision | pipeline(task=“image-classification”)         |
| Image segmentation           | assign a label to each individual pixel of an image (supports semantic, panoptic, and instance segmentation) | Computer vision | pipeline(task=“image-segmentation”)           |
| Object detection             | predict the bounding boxes and classes of objects in an image                                                | Computer vision | pipeline(task=“object-detection”)             |
| Audio classification         | assign a label to some audio data                                                                            | Audio           | pipeline(task=“audio-classification”)         |
| Automatic speech recognition | transcribe speech into text                                                                                  | Audio           | pipeline(task=“automatic-speech-recognition”) |
| Visual question answering    | answer a question about the image, given an image and a question                                             | Multimodal      | pipeline(task=“vqa”)                          |
| Document question answering  | answer a question about the document, given a document and a question                                        | Multimodal      | pipeline(task=""document-question-answering"")  |
| Image captioning             | generate a caption for a given image                                                                         | Multimodal      | pipeline(task=""image-to-text"")                |
Start by creating an instance of [pipeline] and specifying a task you want to use it for. In this guide, you'll use the [pipeline] for sentiment analysis as an example:

from transformers import pipeline
classifier = pipeline(""sentiment-analysis"")

The [pipeline] downloads and caches a default pretrained model and tokenizer for sentiment analysis. Now you can use the classifier on your target text:

classifier(""We are very happy to show you the 🤗 Transformers library."")
[{'label': 'POSITIVE', 'score': 0.9998}]

If you have more than one input, pass your inputs as a list to the [pipeline] to return a list of dictionaries:

results = classifier([""We are very happy to show you the 🤗 Transformers library."", ""We hope you don't hate it.""])
for result in results:
     print(f""label: {result['label']}, with score: {round(result['score'], 4)}"")
label: POSITIVE, with score: 0.9998
label: NEGATIVE, with score: 0.5309

The [pipeline] can also iterate over an entire dataset for any task you like. For this example, let's choose automatic speech recognition as our task:

import torch
from transformers import pipeline
speech_recognizer = pipeline(""automatic-speech-recognition"", model=""facebook/wav2vec2-base-960h"")

Load an audio dataset (see the 🤗 Datasets Quick Start for more details) you'd like to iterate over. For example, load the MInDS-14 dataset:

from datasets import load_dataset, Audio
dataset = load_dataset(""PolyAI/minds14"", name=""en-US"", split=""train"")  # doctest: +IGNORE_RESULT

You need to make sure the sampling rate of the dataset matches the sampling 
rate facebook/wav2vec2-base-960h was trained on:

dataset = dataset.cast_column(""audio"", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))

The audio files are automatically loaded and resampled when calling the ""audio"" column.
Extract the raw waveform arrays from the first 4 samples and pass it as a list to the pipeline:

result = speech_recognizer(dataset[:4][""audio""])
print([d[""text""] for d in result])
['I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT', ""FONDERING HOW I'D SET UP A JOIN TO HELL T WITH MY WIFE AND WHERE THE AP MIGHT BE"", ""I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE APSO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AN I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS"", 'HOW DO I FURN A JOINA COUT']

For larger datasets where the inputs are big (like in speech or vision), you'll want to pass a generator instead of a list to load all the inputs in memory. Take a look at the pipeline API reference for more information.
Use another model and tokenizer in the pipeline
The [pipeline] can accommodate any model from the Hub, making it easy to adapt the [pipeline] for other use-cases. For example, if you'd like a model capable of handling French text, use the tags on the Hub to filter for an appropriate model. The top filtered result returns a multilingual BERT model finetuned for sentiment analysis you can use for French text:

model_name = ""nlptown/bert-base-multilingual-uncased-sentiment""

Use [AutoModelForSequenceClassification] and [AutoTokenizer] to load the pretrained model and it's associated tokenizer (more on an AutoClass in the next section):

from transformers import AutoTokenizer, AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
``
</pt>
<tf>
Use [TFAutoModelForSequenceClassification] and [AutoTokenizer] to load the pretrained model and it's associated tokenizer (more on anTFAutoClass` in the next section):

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Specify the model and tokenizer in the [pipeline], and now you can apply the classifier on French text:

classifier = pipeline(""sentiment-analysis"", model=model, tokenizer=tokenizer)
classifier(""Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers."")
[{'label': '5 stars', 'score': 0.7273}]

If you can't find a model for your use-case, you'll need to finetune a pretrained model on your data. Take a look at our finetuning tutorial to learn how. Finally, after you've finetuned your pretrained model, please consider sharing the model with the community on the Hub to democratize machine learning for everyone! 🤗
AutoClass

Under the hood, the [AutoModelForSequenceClassification] and [AutoTokenizer] classes work together to power the [pipeline] you used above. An AutoClass is a shortcut that automatically retrieves the architecture of a pretrained model from its name or path. You only need to select the appropriate AutoClass for your task and it's associated preprocessing class. 
Let's return to the example from the previous section and see how you can use the AutoClass to replicate the results of the [pipeline].
AutoTokenizer
A tokenizer is responsible for preprocessing text into an array of numbers as inputs to a model. There are multiple rules that govern the tokenization process, including how to split a word and at what level words should be split (learn more about tokenization in the tokenizer summary). The most important thing to remember is you need to instantiate a tokenizer with the same model name to ensure you're using the same tokenization rules a model was pretrained with.
Load a tokenizer with [AutoTokenizer]:

from transformers import AutoTokenizer
model_name = ""nlptown/bert-base-multilingual-uncased-sentiment""
tokenizer = AutoTokenizer.from_pretrained(model_name)

Pass your text to the tokenizer:

encoding = tokenizer(""We are very happy to show you the 🤗 Transformers library."")
print(encoding)
{'input_ids': [101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103, 100, 58263, 13299, 119, 102],
 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

The tokenizer returns a dictionary containing:

input_ids: numerical representations of your tokens.
attention_mask: indicates which tokens should be attended to.

A tokenizer can also accept a list of inputs, and pad and truncate the text to return a batch with uniform length:

pt_batch = tokenizer(
     [""We are very happy to show you the 🤗 Transformers library."", ""We hope you don't hate it.""],
     padding=True,
     truncation=True,
     max_length=512,
     return_tensors=""pt"",
 )
</pt>
<tf>py
tf_batch = tokenizer(
     [""We are very happy to show you the 🤗 Transformers library."", ""We hope you don't hate it.""],
     padding=True,
     truncation=True,
     max_length=512,
     return_tensors=""tf"",
 )

Check out the preprocess tutorial for more details about tokenization, and how to use an [AutoImageProcessor], [AutoFeatureExtractor] and [AutoProcessor] to preprocess image, audio, and multimodal inputs.

AutoModel

🤗 Transformers provides a simple and unified way to load pretrained instances. This means you can load an [AutoModel] like you would load an [AutoTokenizer]. The only difference is selecting the correct [AutoModel] for the task. For text (or sequence) classification, you should load [AutoModelForSequenceClassification]:

from transformers import AutoModelForSequenceClassification
model_name = ""nlptown/bert-base-multilingual-uncased-sentiment""
pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)

See the task summary for tasks supported by an [AutoModel] class.

Now pass your preprocessed batch of inputs directly to the model. You just have to unpack the dictionary by adding **:

pt_outputs = pt_model(**pt_batch)

The model outputs the final activations in the logits attribute. Apply the softmax function to the logits to retrieve the probabilities:

from torch import nn
pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
print(pt_predictions)
tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
        [0.2084, 0.1826, 0.1969, 0.1755, 0.2365]], grad_fn=)
``
</pt>
<tf>
🤗 Transformers provides a simple and unified way to load pretrained instances. This means you can load an [TFAutoModel] like you would load an [AutoTokenizer]. The only difference is selecting the correct [TFAutoModel] for the task. For text (or sequence) classification, you should load [TFAutoModelForSequenceClassification`]:

from transformers import TFAutoModelForSequenceClassification
model_name = ""nlptown/bert-base-multilingual-uncased-sentiment""
tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)

See the task summary for tasks supported by an [AutoModel] class.

Now pass your preprocessed batch of inputs directly to the model. You can pass the tensors as-is:

tf_outputs = tf_model(tf_batch)

The model outputs the final activations in the logits attribute. Apply the softmax function to the logits to retrieve the probabilities:

import tensorflow as tf
tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
tf_predictions  # doctest: +IGNORE_RESULT

All 🤗 Transformers models (PyTorch or TensorFlow) output the tensors before the final activation
function (like softmax) because the final activation function is often fused with the loss. Model outputs are special dataclasses so their attributes are autocompleted in an IDE. The model outputs behave like a tuple or a dictionary (you can index with an integer, a slice or a string) in which case, attributes that are None are ignored.

Save a model

Once your model is fine-tuned, you can save it with its tokenizer using [PreTrainedModel.save_pretrained]:

pt_save_directory = ""./pt_save_pretrained""
tokenizer.save_pretrained(pt_save_directory)  # doctest: +IGNORE_RESULT
pt_model.save_pretrained(pt_save_directory)

When you are ready to use the model again, reload it with [PreTrainedModel.from_pretrained]:

pt_model = AutoModelForSequenceClassification.from_pretrained(""./pt_save_pretrained"")
``
</pt>
<tf>
Once your model is fine-tuned, you can save it with its tokenizer using [TFPreTrainedModel.save_pretrained`]:

tf_save_directory = ""./tf_save_pretrained""
tokenizer.save_pretrained(tf_save_directory)  # doctest: +IGNORE_RESULT
tf_model.save_pretrained(tf_save_directory)

When you are ready to use the model again, reload it with [TFPreTrainedModel.from_pretrained]:

tf_model = TFAutoModelForSequenceClassification.from_pretrained(""./tf_save_pretrained"")

One particularly cool 🤗 Transformers feature is the ability to save a model and reload it as either a PyTorch or TensorFlow model. The from_pt or from_tf parameter can convert the model from one framework to the other:

from transformers import AutoModel
tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
</pt>
<tf>py
from transformers import TFAutoModel
tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)

Custom model builds
You can modify the model's configuration class to change how a model is built. The configuration specifies a model's attributes, such as the number of hidden layers or attention heads. You start from scratch when you initialize a model from a custom configuration class. The model attributes are randomly initialized, and you'll need to train the model before you can use it to get meaningful results.
Start by importing [AutoConfig], and then load the pretrained model you want to modify. Within [AutoConfig.from_pretrained], you can specify the attribute you want to change, such as the number of attention heads:

from transformers import AutoConfig
my_config = AutoConfig.from_pretrained(""distilbert-base-uncased"", n_heads=12)

Create a model from your custom configuration with [AutoModel.from_config]:

from transformers import AutoModel
my_model = AutoModel.from_config(my_config)
``
</pt>
<tf>
Create a model from your custom configuration with [TFAutoModel.from_config`]:

from transformers import TFAutoModel
my_model = TFAutoModel.from_config(my_config)

Take a look at the Create a custom architecture guide for more information about building custom configurations.
Trainer - a PyTorch optimized training loop
All models are a standard torch.nn.Module so you can use them in any typical training loop. While you can write your own training loop, 🤗 Transformers provides a [Trainer] class for PyTorch, which contains the basic training loop and adds additional functionality for features like distributed training, mixed precision, and more.
Depending on your task, you'll typically pass the following parameters to [Trainer]:

You'll start with a [PreTrainedModel] or a torch.nn.Module:

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(""distilbert-base-uncased"")
   

[TrainingArguments] contains the model hyperparameters you can change like learning rate, batch size, and the number of epochs to train for. The default values are used if you don't specify any training arguments:

from transformers import TrainingArguments
training_args = TrainingArguments(
        output_dir=""path/to/save/folder/"",
        learning_rate=2e-5,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        num_train_epochs=2,
    )
   

Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""distilbert-base-uncased"")
   

Load a dataset:

from datasets import load_dataset
dataset = load_dataset(""rotten_tomatoes"")  # doctest: +IGNORE_RESULT
   

Create a function to tokenize the dataset:

def tokenize_dataset(dataset):
        return tokenizer(dataset[""text""])
   

Then apply it over the entire dataset with [~datasets.Dataset.map]:

dataset = dataset.map(tokenize_dataset, batched=True)
   

A [DataCollatorWithPadding] to create a batch of examples from your dataset:

from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
   

Now gather all these classes in [Trainer]:

from transformers import Trainer
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=dataset[""train""],
     eval_dataset=dataset[""test""],
     tokenizer=tokenizer,
     data_collator=data_collator,
 )  # doctest: +SKIP

When you're ready, call [~Trainer.train] to start training:

trainer.train()  # doctest: +SKIP

For tasks - like translation or summarization - that use a sequence-to-sequence model, use the [Seq2SeqTrainer] and [Seq2SeqTrainingArguments] classes instead.

You can customize the training loop behavior by subclassing the methods inside [Trainer]. This allows you to customize features such as the loss function, optimizer, and scheduler. Take a look at the [Trainer] reference for which methods can be subclassed. 
The other way to customize the training loop is by using Callbacks. You can use callbacks to integrate with other libraries and inspect the training loop to report on progress or stop the training early. Callbacks do not modify anything in the training loop itself. To customize something like the loss function, you need to subclass the [Trainer] instead.
Train with TensorFlow
All models are a standard tf.keras.Model so they can be trained in TensorFlow with the Keras API. 🤗 Transformers provides the [~TFPreTrainedModel.prepare_tf_dataset] method to easily load your dataset as a tf.data.Dataset so you can start training right away with Keras' compile and fit methods.

You'll start with a [TFPreTrainedModel] or a tf.keras.Model:

from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained(""distilbert-base-uncased"")
   

Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""distilbert-base-uncased"")
   

Create a function to tokenize the dataset:

def tokenize_dataset(dataset):
        return tokenizer(dataset[""text""])  # doctest: +SKIP
   

Apply the tokenizer over the entire dataset with [~datasets.Dataset.map] and then pass the dataset and tokenizer to [~TFPreTrainedModel.prepare_tf_dataset]. You can also change the batch size and shuffle the dataset here if you'd like:

dataset = dataset.map(tokenize_dataset)  # doctest: +SKIP
tf_dataset = model.prepare_tf_dataset(
        dataset[""train""], batch_size=16, shuffle=True, tokenizer=tokenizer
    )  # doctest: +SKIP
   

When you're ready, you can call compile and fit to start training. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

from tensorflow.keras.optimizers import Adam
model.compile(optimizer=Adam(3e-5))  # No loss argument!
model.fit(tf_dataset)  # doctest: +SKIP
   

What's next?
Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate rouge_score
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

from huggingface_hub import notebook_login
notebook_login()

Load BillSum dataset
Start by loading the smaller California state bill subset of the BillSum dataset from the 🤗 Datasets library:

from datasets import load_dataset
billsum = load_dataset(""billsum"", split=""ca_test"")

Split the dataset into a train and test set with the [~datasets.Dataset.train_test_split] method:

billsum = billsum.train_test_split(test_size=0.2)

Then take a look at an example:

billsum[""train""][0]
{'summary': 'Existing law authorizes state agencies to enter into contracts for the acquisition of goods or services upon approval by the Department of General Services. Existing law sets forth various requirements and prohibitions for those contracts, including, but not limited to, a prohibition on entering into contracts for the acquisition of goods or services of $100,000 or more with a contractor that discriminates between spouses and domestic partners or same-sex and different-sex couples in the provision of benefits. Existing law provides that a contract entered into in violation of those requirements and prohibitions is void and authorizes the state or any person acting on behalf of the state to bring a civil action seeking a determination that a contract is in violation and therefore void. Under existing law, a willful violation of those requirements and prohibitions is a misdemeanor.\nThis bill would also prohibit a state agency from entering into contracts for the acquisition of goods or services of $100,000 or more with a contractor that discriminates between employees on the basis of gender identity in the provision of benefits, as specified. By expanding the scope of a crime, this bill would impose a state-mandated local program.\nThe California Constitution requires the state to reimburse local agencies and school districts for certain costs mandated by the state. Statutory provisions establish procedures for making that reimbursement.\nThis bill would provide that no reimbursement is required by this act for a specified reason.',
 'text': 'The people of the State of California do enact as follows:\n\n\nSECTION 1.\nSection 10295.35 is added to the Public Contract Code, to read:\n10295.35.\n(a) (1) Notwithstanding any other law, a state agency shall not enter into any contract for the acquisition of goods or services in the amount of one hundred thousand dollars ($100,000) or more with a contractor that, in the provision of benefits, discriminates between employees on the basis of an employee’s or dependent’s actual or perceived gender identity, including, but not limited to, the employee’s or dependent’s identification as transgender.\n(2) For purposes of this section, “contract” includes contracts with a cumulative amount of one hundred thousand dollars ($100,000) or more per contractor in each fiscal year.\n(3) For purposes of this section, an employee health plan is discriminatory if the plan is not consistent with Section 1365.5 of the Health and Safety Code and Section 10140 of the Insurance Code.\n(4) The requirements of this section shall apply only to those portions of a contractor’s operations that occur under any of the following conditions:\n(A) Within the state.\n(B) On real property outside the state if the property is owned by the state or if the state has a right to occupy the property, and if the contractor’s presence at that location is connected to a contract with the state.\n(C) Elsewhere in the United States where work related to a state contract is being performed.\n(b) Contractors shall treat as confidential, to the maximum extent allowed by law or by the requirement of the contractor’s insurance provider, any request by an employee or applicant for employment benefits or any documentation of eligibility for benefits submitted by an employee or applicant for employment.\n(c) After taking all reasonable measures to find a contractor that complies with this section, as determined by the state agency, the requirements of this section may be waived under any of the following circumstances:\n(1) There is only one prospective contractor willing to enter into a specific contract with the state agency.\n(2) The contract is necessary to respond to an emergency, as determined by the state agency, that endangers the public health, welfare, or safety, or the contract is necessary for the provision of essential services, and no entity that complies with the requirements of this section capable of responding to the emergency is immediately available.\n(3) The requirements of this section violate, or are inconsistent with, the terms or conditions of a grant, subvention, or agreement, if the agency has made a good faith attempt to change the terms or conditions of any grant, subvention, or agreement to authorize application of this section.\n(4) The contractor is providing wholesale or bulk water, power, or natural gas, the conveyance or transmission of the same, or ancillary services, as required for ensuring reliable services in accordance with good utility practice, if the purchase of the same cannot practically be accomplished through the standard competitive bidding procedures and the contractor is not providing direct retail services to end users.\n(d) (1) A contractor shall not be deemed to discriminate in the provision of benefits if the contractor, in providing the benefits, pays the actual costs incurred in obtaining the benefit.\n(2) If a contractor is unable to provide a certain benefit, despite taking reasonable measures to do so, the contractor shall not be deemed to discriminate in the provision of benefits.\n(e) (1) Every contract subject to this chapter shall contain a statement by which the contractor certifies that the contractor is in compliance with this section.\n(2) The department or other contracting agency shall enforce this section pursuant to its existing enforcement powers.\n(3) (A) If a contractor falsely certifies that it is in compliance with this section, the contract with that contractor shall be subject to Article 9 (commencing with Section 10420), unless, within a time period specified by the department or other contracting agency, the contractor provides to the department or agency proof that it has complied, or is in the process of complying, with this section.\n(B) The application of the remedies or penalties contained in Article 9 (commencing with Section 10420) to a contract subject to this chapter shall not preclude the application of any existing remedies otherwise available to the department or other contracting agency under its existing enforcement powers.\n(f) Nothing in this section is intended to regulate the contracting practices of any local jurisdiction.\n(g) This section shall be construed so as not to conflict with applicable federal laws, rules, or regulations. In the event that a court or agency of competent jurisdiction holds that federal law, rule, or regulation invalidates any clause, sentence, paragraph, or section of this code or the application thereof to any person or circumstances, it is the intent of the state that the court or agency sever that clause, sentence, paragraph, or section so that the remainder of this section shall remain in effect.\nSEC. 2.\nSection 10295.35 of the Public Contract Code shall not be construed to create any new enforcement authority or responsibility in the Department of General Services or any other contracting agency.\nSEC. 3.\nNo reimbursement is required by this act pursuant to Section 6 of Article XIII\u2009B of the California Constitution because the only costs that may be incurred by a local agency or school district will be incurred because this act creates a new crime or infraction, eliminates a crime or infraction, or changes the penalty for a crime or infraction, within the meaning of Section 17556 of the Government Code, or changes the definition of a crime within the meaning of Section 6 of Article XIII\u2009B of the California Constitution.',
 'title': 'An act to add Section 10295.35 to the Public Contract Code, relating to public contracts.'}

There are two fields that you'll want to use:

text: the text of the bill which'll be the input to the model.
summary: a condensed version of text which'll be the model target.

Preprocess
The next step is to load a T5 tokenizer to process text and summary:

from transformers import AutoTokenizer
checkpoint = ""t5-small""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

The preprocessing function you want to create needs to:

Prefix the input with a prompt so T5 knows this is a summarization task. Some models capable of multiple NLP tasks require prompting for specific tasks.
Use the keyword text_target argument when tokenizing labels.
Truncate sequences to be no longer than the maximum length set by the max_length parameter.

prefix = ""summarize: ""
def preprocess_function(examples):
     inputs = [prefix + doc for doc in examples[""text""]]
     model_inputs = tokenizer(inputs, max_length=1024, truncation=True)

     labels = tokenizer(text_target=examples[""summary""], max_length=128, truncation=True)
     model_inputs[""labels""] = labels[""input_ids""]
     return model_inputs

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once:

tokenized_billsum = billsum.map(preprocess_function, batched=True)

Now create a batch of examples using [DataCollatorForSeq2Seq]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.

from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)
</pt>
<tf>py
from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors=""tf"")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the ROUGE metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
rouge = evaluate.load(""rouge"")

Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the ROUGE metric:

import numpy as np
def compute_metrics(eval_pred):
     predictions, labels = eval_pred
     decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
     labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
     decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

     result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
     prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]
     result[""gen_len""] = np.mean(prediction_lens)
     return {k: round(v, 4) for k, v in result.items()}

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load T5 with [AutoModelForSeq2SeqLM]:

from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)

At this point, only three steps remain:

Define your training hyperparameters in [Seq2SeqTrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the ROUGE metric and save the training checkpoint.
Pass the training arguments to [Seq2SeqTrainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = Seq2SeqTrainingArguments(
     output_dir=""my_awesome_billsum_model"",
     evaluation_strategy=""epoch"",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
     weight_decay=0.01,
     save_total_limit=3,
     num_train_epochs=4,
     predict_with_generate=True,
     fp16=True,
     push_to_hub=True,
 )
trainer = Seq2SeqTrainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_billsum[""train""],
     eval_dataset=tokenized_billsum[""test""],
     tokenizer=tokenizer,
     data_collator=data_collator,
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)

Then you can load T5 with [TFAutoModelForSeq2SeqLM]:

from transformers import TFAutoModelForSeq2SeqLM
model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     tokenized_billsum[""train""],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_test_set = model.prepare_tf_dataset(
     tokenized_billsum[""test""],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

import tensorflow as tf
model.compile(optimizer=optimizer)  # No loss argument!

The last two things to setup before you start training is to compute the ROUGE score from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks.
Pass your compute_metrics function to [~transformers.KerasMetricCallback]:

from transformers.keras_callbacks import KerasMetricCallback
metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)

Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
     output_dir=""my_awesome_billsum_model"",
     tokenizer=tokenizer,
 )

Then bundle your callbacks together:

callbacks = [metric_callback, push_to_hub_callback]

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=callbacks)

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for summarization, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text you'd like to summarize. For T5, you need to prefix your input depending on the task you're working on. For summarization you should prefix your input as shown below:

text = ""summarize: The Inflation Reduction Act lowers prescription drug costs, health care costs, and energy costs. It's the most aggressive action on tackling the climate crisis in American history, which will lift up American workers and create good-paying, union jobs across the country. It'll lower the deficit and ask the ultra-wealthy and corporations to pay their fair share. And no one making under $400,000 per year will pay a penny more in taxes.""

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for summarization with your model, and pass your text to it:

from transformers import pipeline
summarizer = pipeline(""summarization"", model=""stevhliu/my_awesome_billsum_model"")
summarizer(text)
[{""summary_text"": ""The Inflation Reduction Act lowers prescription drug costs, health care costs, and energy costs. It's the most aggressive action on tackling the climate crisis in American history, which will lift up American workers and create good-paying, union jobs across the country.""}]

You can also manually replicate the results of the pipeline if you'd like:

Tokenize the text and return the input_ids as PyTorch tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""stevhliu/my_awesome_billsum_model"")
inputs = tokenizer(text, return_tensors=""pt"").input_ids

Use the [~transformers.generation_utils.GenerationMixin.generate] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the Text Generation API.

from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained(""stevhliu/my_awesome_billsum_model"")
outputs = model.generate(inputs, max_new_tokens=100, do_sample=False)

Decode the generated token ids back into text:

tokenizer.decode(outputs[0], skip_special_tokens=True)
'the inflation reduction act lowers prescription drug costs, health care costs, and energy costs. it's the most aggressive action on tackling the climate crisis in american history. it will ask the ultra-wealthy and corporations to pay their fair share.'
``
</pt>
<tf>
Tokenize the text and return theinput_ids` as TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""stevhliu/my_awesome_billsum_model"")
inputs = tokenizer(text, return_tensors=""tf"").input_ids

Use the [~transformers.generation_tf_utils.TFGenerationMixin.generate] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the Text Generation API.

from transformers import TFAutoModelForSeq2SeqLM
model = TFAutoModelForSeq2SeqLM.from_pretrained(""stevhliu/my_awesome_billsum_model"")
outputs = model.generate(inputs, max_new_tokens=100, do_sample=False)

Decode the generated token ids back into text:

tokenizer.decode(outputs[0], skip_special_tokens=True)
'the inflation reduction act lowers prescription drug costs, health care costs, and energy costs. it's the most aggressive action on tackling the climate crisis in american history. it will ask the ultra-wealthy and corporations to pay their fair share.'

"
"
Efficient Training on CPU
This guide focuses on training large models efficiently on CPU.
Mixed precision with IPEX
IPEX is optimized for CPUs with AVX-512 or above, and functionally works for CPUs with only AVX2. So, it is expected to bring performance benefit for Intel CPU generations with AVX-512 or above while CPUs with only AVX2 (e.g., AMD CPUs or older Intel CPUs) might result in a better performance under IPEX, but not guaranteed. IPEX provides performance optimizations for CPU training with both Float32 and BFloat16. The usage of BFloat16 is the main focus of the following sections.
Low precision data type BFloat16 has been natively supported on the 3rd Generation Xeon® Scalable Processors (aka Cooper Lake) with AVX512 instruction set and will be supported on the next generation of Intel® Xeon® Scalable Processors with Intel® Advanced Matrix Extensions (Intel® AMX) instruction set with further boosted performance. The Auto Mixed Precision for CPU backend has been enabled since PyTorch-1.10. At the same time, the support of Auto Mixed Precision with BFloat16 for CPU and BFloat16 optimization of operators has been massively enabled in Intel® Extension for PyTorch, and partially upstreamed to PyTorch master branch. Users can get better performance and user experience with IPEX Auto Mixed Precision.
Check more detailed information for Auto Mixed Precision.
IPEX installation:
IPEX release is following PyTorch, to install via pip:
| PyTorch Version   | IPEX version   |
| :---------------: | :----------:   |
| 1.13              |  1.13.0+cpu    |
| 1.12              |  1.12.300+cpu  |
| 1.11              |  1.11.200+cpu  |
| 1.10              |  1.10.100+cpu  |
pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
Check more approaches for IPEX installation.
Usage in Trainer
To enable auto mixed precision with IPEX in Trainer, users should add use_ipex, bf16 and no_cuda in training command arguments.
Take an example of the use cases on Transformers question-answering

Training with IPEX using BF16 auto mixed precision on CPU:

 python run_qa.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/ \
--use_ipex \
--bf16 --no_cuda
Practice example
Blog: Accelerating PyTorch Transformers with Intel Sapphire Rapids"
"
Quantize 🤗 Transformers models
bitsandbytes Integration
🤗 Transformers is closely integrated with most used modules on bitsandbytes. You can load your model in 8-bit precision with few lines of code.
This is supported by most of the GPU hardwares since the 0.37.0 release of bitsandbytes.
Learn more about the quantization method in the LLM.int8() paper, or the blogpost about the collaboration.
Since its 0.39.0 release, you can load any model that supports device_map using 4-bit quantization, leveraging FP4 data type.
If you want to quantize your own pytorch model, check out this documentation from 🤗 Accelerate library. 
Here are the things you can do using bitsandbytes integration
FP4 quantization
Requirements
Make sure that you have installed the requirements below before running any of the code snippets below.

Latest bitsandbytes library
pip install bitsandbytes>=0.39.0

Install latest accelerate
pip install --upgrade accelerate

Install latest transformers from source 
pip install --upgrade transformers

Tips and best practices

Advanced usage: Refer to this Google Colab notebook for advanced usage of 4-bit quantization with all the possible options.

Faster inference with batch_size=1 : Since the 0.40.0 release of bitsandbytes, for batch_size=1 you can benefit from fast inference. Check out these release notes and make sure to have a version that is greater than 0.40.0 to benefit from this feature out of the box. 

Training: According to QLoRA paper, for training 4-bit base models (e.g. using LoRA adapters) one should use bnb_4bit_quant_type='nf4'. 

Inference: For inference, bnb_4bit_quant_type does not have a huge impact on the performance. However for consistency with the model's weights, make sure you use the same bnb_4bit_compute_dtype and torch_dtype arguments.

Load a large model in 4bit
By using load_in_4bit=True when calling the .from_pretrained method, you can divide your memory use by 4 (roughly).
thon
pip install transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = ""bigscience/bloom-1b7""
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=""auto"", load_in_4bit=True)

Note that once a model has been loaded in 4-bit it is currently not possible to push the quantized weights on the Hub. Note also that you cannot train 4-bit weights as this is not supported yet. However you can use 4-bit models to train extra parameters, this will be covered in the next section.

Load a large model in 8bit
You can load a model by roughly halving the memory requirements by using load_in_8bit=True argument when calling .from_pretrained method
thon
pip install transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = ""bigscience/bloom-1b7""
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=""auto"", load_in_8bit=True)

Then, use your model as you would usually use a [PreTrainedModel].
You can check the memory footprint of your model with get_memory_footprint method.
python
print(model.get_memory_footprint())
With this integration we were able to load large models on smaller devices and run them without any issue.  

Note that once a model has been loaded in 8-bit it is currently not possible to push the quantized weights on the Hub except if you use the latest transformers and bitsandbytes. Note also that you cannot train 8-bit weights as this is not supported yet. However you can use 8-bit models to train extra parameters, this will be covered in the next section.
Note also that device_map is optional but setting device_map = 'auto' is prefered for inference as it will dispatch efficiently the model on the available ressources.

Advanced usecases
Here we will cover some advanced usecases you can perform with FP4 quantization 
Change the compute dtype
The compute dtype is used to change the dtype that will be used during computation. For example, hidden states could be in float32 but computation can be set to bf16 for speedups. By default, the compute dtype is set to float32.
thon
import torch
from transformers import BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)

Using NF4 (Normal Float 4) data type
You can also use the NF4 data type, which is a new 4bit datatype adapted for weights that have been initialized using a normal distribution. For that run:
thon
from transformers import BitsAndBytesConfig
nf4_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type=""nf4"",
)
model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config)

Use nested quantization for more memory efficient inference
We also advise users to use the nested quantization technique. This saves more memory at no additional performance - from our empirical observations, this enables fine-tuning llama-13b model on an NVIDIA-T4 16GB with a sequence length of 1024, batch size of 1 and gradient accumulation steps of 4.
thon
from transformers import BitsAndBytesConfig
double_quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
)
model_double_quant = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=double_quant_config)

Push quantized models on the 🤗 Hub
You can push a quantized model on the Hub by naively using push_to_hub method. This will first push the quantization configuration file, then push the quantized model weights.
Make sure to use bitsandbytes>0.37.2 (at this time of writing, we tested it on bitsandbytes==0.38.0.post1) to be able to use this feature. 
thon
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(""bigscience/bloom-560m"", device_map=""auto"", load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(""bigscience/bloom-560m"")
model.push_to_hub(""bloom-560m-8bit"")

Pushing 8bit models on the Hub is strongely encouraged for large models. This will allow the community to benefit from the memory footprint reduction and loading for example large models on a Google Colab.

Load a quantized model from the 🤗 Hub
You can load a quantized model from the Hub by using from_pretrained method. Make sure that the pushed weights are quantized, by checking that the attribute quantization_config is present in the model configuration object.
thon
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(""{your_username}/bloom-560m-8bit"", device_map=""auto"")
``
Note that in this case, you don't need to specify the argumentsload_in_8bit=True, but you need to make sure thatbitsandbytesandaccelerateare installed.
Note also thatdevice_mapis optional but settingdevice_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources.
Advanced usecases
This section is intended to advanced users, that want to explore what it is possible to do beyond loading and running 8-bit models.
Offload between cpu and gpu
One of the advanced usecase of this is being able to load a model and dispatch the weights between CPU and GPU. Note that the weights that will be dispatched on CPU will not be converted in 8-bit, thus kept in float32. This feature is intended for users that want to fit a very large model and dispatch the model between GPU and CPU.
First, load a BitsAndBytesConfig from transformers and set the attribute llm_int8_enable_fp32_cpu_offload to True:
thon
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)

Let's say you want to load bigscience/bloom-1b7 model, and you have just enough GPU RAM to fit the entire model except the lm_head. Therefore write a custom device_map as follows:
python
device_map = {
    ""transformer.word_embeddings"": 0,
    ""transformer.word_embeddings_layernorm"": 0,
    ""lm_head"": ""cpu"",
    ""transformer.h"": 0,
    ""transformer.ln_f"": 0,
}
And load your model as follows:
python
model_8bit = AutoModelForCausalLM.from_pretrained(
    ""bigscience/bloom-1b7"",
    device_map=device_map,
    quantization_config=quantization_config,
)
And that's it! Enjoy your model!
Play with llm_int8_threshold
You can play with the llm_int8_threshold argument to change the threshold of the outliers. An ""outlier"" is a hidden state value that is greater than a certain threshold. 
This corresponds to the outlier threshold for outlier detection as described in LLM.int8() paper. Any hidden states value that is above this threshold will be considered an outlier and the operation on those values will be done in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but there are some exceptional systematic outliers that are very differently distributed for large models. These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6, but a lower threshold might be needed for more unstable models (small models, fine-tuning).
This argument can impact the inference speed of the model. We suggest to play with this parameter to find which one is the best for your usecase.
thon
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_id = ""bigscience/bloom-1b7""
quantization_config = BitsAndBytesConfig(
    llm_int8_threshold=10,
)
model_8bit = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map=device_map,
    quantization_config=quantization_config,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

Skip the conversion of some modules
Some models has several modules that needs to be not converted in 8-bit to ensure stability. For example Jukebox model has several lm_head modules that should be skipped. Play with llm_int8_skip_modules 
thon
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_id = ""bigscience/bloom-1b7""
quantization_config = BitsAndBytesConfig(
    llm_int8_skip_modules=[""lm_head""],
)
model_8bit = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map=device_map,
    quantization_config=quantization_config,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

Fine-tune a model that has been loaded in 8-bit
With the official support of adapters in the Hugging Face ecosystem, you can fine-tune models that have been loaded in 8-bit. 
This enables fine-tuning large models such as flan-t5-large or facebook/opt-6.7b in a single google Colab. Please have a look at peft library for more details.
Note that you don't need to pass device_map when loading the model for training. It will automatically load your model on your GPU. You can also set the device map to a specific device if needed (e.g. cuda:0, 0, torch.device('cuda:0')). Please note that device_map=auto should be used for inference only. 
BitsAndBytesConfig
[[autodoc]] BitsAndBytesConfig
Quantization with 🤗 optimum
Please have a look at Optimum documentation to learn more about quantization methods that are supported by optimum and see if these are applicable for your usecase."
"
Hyperparameter Search using Trainer API
🤗 Transformers provides a [Trainer] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [Trainer] provides API for hyperparameter search. This doc shows how to enable it in example. 
Hyperparameter Search backend
[Trainer] supports four hyperparameter search backends currently:
optuna, sigopt, raytune and wandb.
you should install them before using them as the hyperparameter search backend

pip install optuna/sigopt/wandb/ray[tune]
How to enable Hyperparameter search in example
Define the hyperparameter search space, different backends need different format.
For sigopt, see sigopt object_parameter, it's like following:

def sigopt_hp_space(trial):
     return [
         {""bounds"": {""min"": 1e-6, ""max"": 1e-4}, ""name"": ""learning_rate"", ""type"": ""double""},
         {
             ""categorical_values"": [""16"", ""32"", ""64"", ""128""],
             ""name"": ""per_device_train_batch_size"",
             ""type"": ""categorical"",
         },
     ]

For optuna, see optuna object_parameter, it's like following:

def optuna_hp_space(trial):
     return {
         ""learning_rate"": trial.suggest_float(""learning_rate"", 1e-6, 1e-4, log=True),
         ""per_device_train_batch_size"": trial.suggest_categorical(""per_device_train_batch_size"", [16, 32, 64, 128]),
     }

For raytune, see raytune object_parameter, it's like following:

def ray_hp_space(trial):
     return {
         ""learning_rate"": tune.loguniform(1e-6, 1e-4),
         ""per_device_train_batch_size"": tune.choice([16, 32, 64, 128]),
     }

For wandb, see wandb object_parameter, it's like following:

def wandb_hp_space(trial):
     return {
         ""method"": ""random"",
         ""metric"": {""name"": ""objective"", ""goal"": ""minimize""},
         ""parameters"": {
             ""learning_rate"": {""distribution"": ""uniform"", ""min"": 1e-6, ""max"": 1e-4},
             ""per_device_train_batch_size"": {""values"": [16, 32, 64, 128]},
         },
     }

Define a model_init function and pass it to the [Trainer], as an example:

def model_init(trial):
     return AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
         from_tf=bool("".ckpt"" in model_args.model_name_or_path),
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
     )

Create a [Trainer] with your model_init function, training arguments, training and test datasets, and evaluation function:

trainer = Trainer(
     model=None,
     args=training_args,
     train_dataset=small_train_dataset,
     eval_dataset=small_eval_dataset,
     compute_metrics=compute_metrics,
     tokenizer=tokenizer,
     model_init=model_init,
     data_collator=data_collator,
 )

Call hyperparameter search, get the best trial parameters, backend could be ""optuna""/""sigopt""/""wandb""/""ray"". direction can be""minimize"" or ""maximize"", which indicates whether to optimize greater or lower objective.
You could define your own compute_objective function, if not defined, the default compute_objective will be called, and the sum of eval metric like f1 is returned as objective value.

best_trial = trainer.hyperparameter_search(
     direction=""maximize"",
     backend=""optuna"",
     hp_space=optuna_hp_space,
     n_trials=20,
     compute_objective=compute_objective,
 )

Hyperparameter search For DDP finetune
Currently, Hyperparameter search for DDP is enabled for optuna and sigopt. Only the rank-zero process will generate the search trial and pass the argument to other ranks."
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

from huggingface_hub import notebook_login
notebook_login()

Load MInDS-14 dataset
Start by loading the MInDS-14 dataset from the 🤗 Datasets library:

from datasets import load_dataset, Audio
minds = load_dataset(""PolyAI/minds14"", name=""en-US"", split=""train"")

Split the dataset's train split into a smaller train and test set with the [~datasets.Dataset.train_test_split] method. This'll give you a chance to experiment and make sure everything works before spending more time on the full dataset.

minds = minds.train_test_split(test_size=0.2)

Then take a look at the dataset:

minds
DatasetDict({
    train: Dataset({
        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
        num_rows: 450
    })
    test: Dataset({
        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
        num_rows: 113
    })
})

While the dataset contains a lot of useful information, like lang_id and english_transcription, you'll focus on the audio and intent_class in this guide. Remove the other columns with the [~datasets.Dataset.remove_columns] method:

minds = minds.remove_columns([""path"", ""transcription"", ""english_transcription"", ""lang_id""])

Take a look at an example now:

minds[""train""][0]
{'audio': {'array': array([ 0.        ,  0.        ,  0.        , , -0.00048828,
         -0.00024414, -0.00024414], dtype=float32),
  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602b9a5fbb1e6d0fbce91f52.wav',
  'sampling_rate': 8000},
 'intent_class': 2}

There are two fields:

audio: a 1-dimensional array of the speech signal that must be called to load and resample the audio file. 
intent_class: represents the class id of the speaker's intent. 

To make it easier for the model to get the label name from the label id, create a dictionary that maps the label name to an integer and vice versa:

labels = minds[""train""].features[""intent_class""].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
     label2id[label] = str(i)
     id2label[str(i)] = label

Now you can convert the label id to a label name:

id2label[str(2)]
'app_error'

Preprocess
The next step is to load a Wav2Vec2 feature extractor to process the audio signal:

from transformers import AutoFeatureExtractor
feature_extractor = AutoFeatureExtractor.from_pretrained(""facebook/wav2vec2-base"")

The MInDS-14 dataset has a sampling rate of 8000khz (you can find this information in it's dataset card), which means you'll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model:

minds = minds.cast_column(""audio"", Audio(sampling_rate=16_000))
minds[""train""][0]
{'audio': {'array': array([ 2.2098757e-05,  4.6582241e-05, -2.2803260e-05, ,
         -2.8419291e-04, -2.3305941e-04, -1.1425107e-04], dtype=float32),
  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602b9a5fbb1e6d0fbce91f52.wav',
  'sampling_rate': 16000},
 'intent_class': 2}

Now create a preprocessing function that:

Calls the audio column to load, and if necessary, resample the audio file.
Checks if the sampling rate of the audio file matches the sampling rate of the audio data a model was pretrained with. You can find this information in the Wav2Vec2 model card.
Set a maximum input length to batch longer inputs without truncating them.

def preprocess_function(examples):
     audio_arrays = [x[""array""] for x in examples[""audio""]]
     inputs = feature_extractor(
         audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=16000, truncation=True
     )
     return inputs

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up map by setting batched=True to process multiple elements of the dataset at once. Remove the columns you don't need, and rename intent_class to label because that's the name the model expects:

encoded_minds = minds.map(preprocess_function, remove_columns=""audio"", batched=True)
encoded_minds = encoded_minds.rename_column(""intent_class"", ""label"")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
accuracy = evaluate.load(""accuracy"")

Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the accuracy:

import numpy as np
def compute_metrics(eval_pred):
     predictions = np.argmax(eval_pred.predictions, axis=1)
     return accuracy.compute(predictions=predictions, references=eval_pred.label_ids)

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load Wav2Vec2 with [AutoModelForAudioClassification] along with the number of expected labels, and the label mappings:

from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer
num_labels = len(id2label)
model = AutoModelForAudioClassification.from_pretrained(
     ""facebook/wav2vec2-base"", num_labels=num_labels, label2id=label2id, id2label=id2label
 )

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the accuracy and save the training checkpoint.
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_mind_model"",
     evaluation_strategy=""epoch"",
     save_strategy=""epoch"",
     learning_rate=3e-5,
     per_device_train_batch_size=32,
     gradient_accumulation_steps=4,
     per_device_eval_batch_size=32,
     num_train_epochs=10,
     warmup_ratio=0.1,
     logging_steps=10,
     load_best_model_at_end=True,
     metric_for_best_model=""accuracy"",
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=encoded_minds[""train""],
     eval_dataset=encoded_minds[""test""],
     tokenizer=feature_extractor,
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

For a more in-depth example of how to finetune a model for audio classification, take a look at the corresponding PyTorch notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to!

from datasets import load_dataset, Audio
dataset = load_dataset(""PolyAI/minds14"", name=""en-US"", split=""train"")
dataset = dataset.cast_column(""audio"", Audio(sampling_rate=16000))
sampling_rate = dataset.features[""audio""].sampling_rate
audio_file = dataset[0][""audio""][""path""]

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for audio classification with your model, and pass your audio file to it:

from transformers import pipeline
classifier = pipeline(""audio-classification"", model=""stevhliu/my_awesome_minds_model"")
classifier(audio_file)
[
    {'score': 0.09766869246959686, 'label': 'cash_deposit'},
    {'score': 0.07998877018690109, 'label': 'app_error'},
    {'score': 0.0781070664525032, 'label': 'joint_account'},
    {'score': 0.07667109370231628, 'label': 'pay_bill'},
    {'score': 0.0755252093076706, 'label': 'balance'}
]

You can also manually replicate the results of the pipeline if you'd like:

Load a feature extractor to preprocess the audio file and return the input as PyTorch tensors:

from transformers import AutoFeatureExtractor
feature_extractor = AutoFeatureExtractor.from_pretrained(""stevhliu/my_awesome_minds_model"")
inputs = feature_extractor(dataset[0][""audio""][""array""], sampling_rate=sampling_rate, return_tensors=""pt"")

Pass your inputs to the model and return the logits:

from transformers import AutoModelForAudioClassification
model = AutoModelForAudioClassification.from_pretrained(""stevhliu/my_awesome_minds_model"")
with torch.no_grad():
     logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a label:

import torch
predicted_class_ids = torch.argmax(logits).item()
predicted_label = model.config.id2label[predicted_class_ids]
predicted_label
'cash_deposit'

"
"
Callbacks
Callbacks are objects that can customize the behavior of the training loop in the PyTorch
[Trainer] (this feature is not yet implemented in TensorFlow) that can inspect the training loop
state (for progress reporting, logging on TensorBoard or other ML platforms) and take decisions (like early
stopping).
Callbacks are ""read only"" pieces of code, apart from the [TrainerControl] object they return, they
cannot change anything in the training loop. For customizations that require changes in the training loop, you should
subclass [Trainer] and override the methods you need (see trainer for examples).
By default a [Trainer] will use the following callbacks:

[DefaultFlowCallback] which handles the default behavior for logging, saving and evaluation.
[PrinterCallback] or [ProgressCallback] to display progress and print the
  logs (the first one is used if you deactivate tqdm through the [TrainingArguments], otherwise
  it's the second one).
[~integrations.TensorBoardCallback] if tensorboard is accessible (either through PyTorch >= 1.4
  or tensorboardX).
[~integrations.WandbCallback] if wandb is installed.
[~integrations.CometCallback] if comet_ml is installed.
[~integrations.MLflowCallback] if mlflow is installed.
[~integrations.NeptuneCallback] if neptune is installed.
[~integrations.AzureMLCallback] if azureml-sdk is
  installed.
[~integrations.CodeCarbonCallback] if codecarbon is
  installed.
[~integrations.ClearMLCallback] if clearml is installed.
[~integrations.DagsHubCallback] if dagshub is installed.
[~integrations.FlyteCallback] if flyte is installed.

The main class that implements callbacks is [TrainerCallback]. It gets the
[TrainingArguments] used to instantiate the [Trainer], can access that
Trainer's internal state via [TrainerState], and can take some actions on the training loop via
[TrainerControl].
Available Callbacks
Here is the list of the available [TrainerCallback] in the library:
[[autodoc]] integrations.CometCallback
    - setup
[[autodoc]] DefaultFlowCallback
[[autodoc]] PrinterCallback
[[autodoc]] ProgressCallback
[[autodoc]] EarlyStoppingCallback
[[autodoc]] integrations.TensorBoardCallback
[[autodoc]] integrations.WandbCallback
    - setup
[[autodoc]] integrations.MLflowCallback
    - setup
[[autodoc]] integrations.AzureMLCallback
[[autodoc]] integrations.CodeCarbonCallback
[[autodoc]] integrations.NeptuneCallback
[[autodoc]] integrations.ClearMLCallback
[[autodoc]] integrations.DagsHubCallback
[[autodoc]] integrations.FlyteCallback
TrainerCallback
[[autodoc]] TrainerCallback
Here is an example of how to register a custom callback with the PyTorch [Trainer]:
thon
class MyCallback(TrainerCallback):
    ""A callback that prints a message at the beginning of training""
def on_train_begin(self, args, state, control, **kwargs):
    print(""Starting training"")

trainer = Trainer(
    model,
    args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    callbacks=[MyCallback],  # We can either pass the callback class this way or an instance of it (MyCallback())
)

Another way to register a callback is to call trainer.add_callback() as follows:
thon
trainer = Trainer()
trainer.add_callback(MyCallback)
Alternatively, we can pass an instance of the callback class
trainer.add_callback(MyCallback())

TrainerState
[[autodoc]] TrainerState
TrainerControl
[[autodoc]] TrainerControl"
"
Zero-shot object detection
[[open-in-colab]]
Traditionally, models used for object detection require labeled image datasets for training,
and are limited to detecting the set of classes from the training data.
Zero-shot object detection is supported by the OWL-ViT model which uses a different approach. OWL-ViT
is an open-vocabulary object detector. It means that it can detect objects in images based on free-text queries without
the need to fine-tune the model on labeled datasets.
OWL-ViT leverages multi-modal representations to perform open-vocabulary detection. It combines CLIP with
lightweight object classification and localization heads. Open-vocabulary detection is achieved by embedding free-text queries with the text encoder of CLIP and using them as input to the object classification and localization heads.
associate images and their corresponding textual descriptions, and ViT processes image patches as inputs. The authors
of OWL-ViT first trained CLIP from scratch and then fine-tuned OWL-ViT end to end on standard object detection datasets using
a bipartite matching loss.
With this approach, the model can detect objects based on textual descriptions without prior training on labeled datasets.
In this guide, you will learn how to use OWL-ViT:
- to detect objects based on text prompts
- for batch object detection
- for image-guided object detection
Before you begin, make sure you have all the necessary libraries installed:

pip install -q transformers
Zero-shot object detection pipeline
The simplest way to try out inference with OWL-ViT is to use it in a [pipeline]. Instantiate a pipeline
for zero-shot object detection from a checkpoint on the Hugging Face Hub:
thon

from transformers import pipeline
checkpoint = ""google/owlvit-base-patch32""
detector = pipeline(model=checkpoint, task=""zero-shot-object-detection"")

Next, choose an image you'd like to detect objects in. Here we'll use the image of astronaut Eileen Collins that is
a part of the NASA Great Images dataset.

import skimage
import numpy as np
from PIL import Image
image = skimage.data.astronaut()
image = Image.fromarray(np.uint8(image)).convert(""RGB"")
image

Pass the image and the candidate object labels to look for to the pipeline.
Here we pass the image directly; other suitable options include a local path to an image or an image url. We also pass text descriptions for all items we want to query the image for. 

predictions = detector(
     image,
     candidate_labels=[""human face"", ""rocket"", ""nasa badge"", ""star-spangled banner""],
 )
predictions
[{'score': 0.3571370542049408,
  'label': 'human face',
  'box': {'xmin': 180, 'ymin': 71, 'xmax': 271, 'ymax': 178}},
 {'score': 0.28099656105041504,
  'label': 'nasa badge',
  'box': {'xmin': 129, 'ymin': 348, 'xmax': 206, 'ymax': 427}},
 {'score': 0.2110239565372467,
  'label': 'rocket',
  'box': {'xmin': 350, 'ymin': -1, 'xmax': 468, 'ymax': 288}},
 {'score': 0.13790413737297058,
  'label': 'star-spangled banner',
  'box': {'xmin': 1, 'ymin': 1, 'xmax': 105, 'ymax': 509}},
 {'score': 0.11950037628412247,
  'label': 'nasa badge',
  'box': {'xmin': 277, 'ymin': 338, 'xmax': 327, 'ymax': 380}},
 {'score': 0.10649408400058746,
  'label': 'rocket',
  'box': {'xmin': 358, 'ymin': 64, 'xmax': 424, 'ymax': 280}}]

Let's visualize the predictions:

from PIL import ImageDraw
draw = ImageDraw.Draw(image)
for prediction in predictions:
     box = prediction[""box""]
     label = prediction[""label""]
     score = prediction[""score""]

     xmin, ymin, xmax, ymax = box.values()
     draw.rectangle((xmin, ymin, xmax, ymax), outline=""red"", width=1)
     draw.text((xmin, ymin), f""{label}: {round(score,2)}"", fill=""white"")

image

Text-prompted zero-shot object detection by hand
Now that you've seen how to use the zero-shot object detection pipeline, let's replicate the same
result manually.
Start by loading the model and associated processor from a checkpoint on the Hugging Face Hub.
Here we'll use the same checkpoint as before:

from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
model = AutoModelForZeroShotObjectDetection.from_pretrained(checkpoint)
processor = AutoProcessor.from_pretrained(checkpoint)

Let's take a different image to switch things up.

import requests
url = ""https://unsplash.com/photos/oj0zeY2Ltk4/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MTR8fHBpY25pY3xlbnwwfHx8fDE2Nzc0OTE1NDk&force=true&w=640""
im = Image.open(requests.get(url, stream=True).raw)
im

Use the processor to prepare the inputs for the model. The processor combines an image processor that prepares the
image for the model by resizing and normalizing it, and a [CLIPTokenizer] that takes care of the text inputs.

text_queries = [""hat"", ""book"", ""sunglasses"", ""camera""]
inputs = processor(text=text_queries, images=im, return_tensors=""pt"")

Pass the inputs through the model, post-process, and visualize the results. Since the image processor resized images before
feeding them to the model, you need to use the [~OwlViTImageProcessor.post_process_object_detection] method to make sure the predicted bounding
boxes have the correct coordinates relative to the original image:

import torch
with torch.no_grad():
     outputs = model(**inputs)
     target_sizes = torch.tensor([im.size[::-1]])
     results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)[0]
draw = ImageDraw.Draw(im)
scores = results[""scores""].tolist()
labels = results[""labels""].tolist()
boxes = results[""boxes""].tolist()
for box, score, label in zip(boxes, scores, labels):
     xmin, ymin, xmax, ymax = box
     draw.rectangle((xmin, ymin, xmax, ymax), outline=""red"", width=1)
     draw.text((xmin, ymin), f""{text_queries[label]}: {round(score,2)}"", fill=""white"")
im

Batch processing
You can pass multiple sets of images and text queries to search for different (or same) objects in several images.
Let's use both an astronaut image and the beach image together.
For batch processing, you should pass text queries as a nested list to the processor and images as lists of PIL images,
PyTorch tensors, or NumPy arrays.

images = [image, im]
text_queries = [
     [""human face"", ""rocket"", ""nasa badge"", ""star-spangled banner""],
     [""hat"", ""book"", ""sunglasses"", ""camera""],
 ]
inputs = processor(text=text_queries, images=images, return_tensors=""pt"")

Previously for post-processing you passed the single image's size as a tensor, but you can also pass a tuple, or, in case
of several images, a list of tuples. Let's create predictions for the two examples, and visualize the second one (image_idx = 1).

with torch.no_grad():
     outputs = model(**inputs)
     target_sizes = [x.size[::-1] for x in images]
     results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)
image_idx = 1
draw = ImageDraw.Draw(images[image_idx])
scores = results[image_idx][""scores""].tolist()
labels = results[image_idx][""labels""].tolist()
boxes = results[image_idx][""boxes""].tolist()
for box, score, label in zip(boxes, scores, labels):
     xmin, ymin, xmax, ymax = box
     draw.rectangle((xmin, ymin, xmax, ymax), outline=""red"", width=1)
     draw.text((xmin, ymin), f""{text_queries[image_idx][label]}: {round(score,2)}"", fill=""white"")
images[image_idx]

Image-guided object detection
In addition to zero-shot object detection with text queries, OWL-ViT offers image-guided object detection. This means
you can use an image query to find similar objects in the target image.
Unlike text queries, only a single example image is allowed.
Let's take an image with two cats on a couch as a target image, and an image of a single cat
as a query:

url = ""http://images.cocodataset.org/val2017/000000039769.jpg""
image_target = Image.open(requests.get(url, stream=True).raw)
query_url = ""http://images.cocodataset.org/val2017/000000524280.jpg""
query_image = Image.open(requests.get(query_url, stream=True).raw)

Let's take a quick look at the images:

import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 2)
ax[0].imshow(image_target)
ax[1].imshow(query_image)

In the preprocessing step, instead of text queries, you now need to use query_images:

inputs = processor(images=image_target, query_images=query_image, return_tensors=""pt"")

For predictions, instead of passing the inputs to the model, pass them to [~OwlViTForObjectDetection.image_guided_detection]. Draw the predictions
as before except now there are no labels.

with torch.no_grad():
     outputs = model.image_guided_detection(**inputs)
     target_sizes = torch.tensor([image_target.size[::-1]])
     results = processor.post_process_image_guided_detection(outputs=outputs, target_sizes=target_sizes)[0]
draw = ImageDraw.Draw(image_target)
scores = results[""scores""].tolist()
boxes = results[""boxes""].tolist()
for box, score, label in zip(boxes, scores, labels):
     xmin, ymin, xmax, ymax = box
     draw.rectangle((xmin, ymin, xmax, ymax), outline=""white"", width=4)
image_target

If you'd like to interactively try out inference with OWL-ViT, check out this demo:

"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate sacrebleu
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

from huggingface_hub import notebook_login
notebook_login()

Load OPUS Books dataset
Start by loading the English-French subset of the OPUS Books dataset from the 🤗 Datasets library:

from datasets import load_dataset
books = load_dataset(""opus_books"", ""en-fr"")

Split the dataset into a train and test set with the [~datasets.Dataset.train_test_split] method:

books = books[""train""].train_test_split(test_size=0.2)

Then take a look at an example:

books[""train""][0]
{'id': '90560',
 'translation': {'en': 'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.',
  'fr': 'Mais ce plateau élevé ne mesurait que quelques toises, et bientôt nous fûmes rentrés dans notre élément.'}}

translation: an English and French translation of the text.
Preprocess

The next step is to load a T5 tokenizer to process the English-French language pairs:

from transformers import AutoTokenizer
checkpoint = ""t5-small""
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

The preprocessing function you want to create needs to:

Prefix the input with a prompt so T5 knows this is a translation task. Some models capable of multiple NLP tasks require prompting for specific tasks.
Tokenize the input (English) and target (French) separately because you can't tokenize French text with a tokenizer pretrained on an English vocabulary.
Truncate sequences to be no longer than the maximum length set by the max_length parameter.

source_lang = ""en""
target_lang = ""fr""
prefix = ""translate English to French: ""
def preprocess_function(examples):
     inputs = [prefix + example[source_lang] for example in examples[""translation""]]
     targets = [example[target_lang] for example in examples[""translation""]]
     model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True)
     return model_inputs

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once:

tokenized_books = books.map(preprocess_function, batched=True)

Now create a batch of examples using [DataCollatorForSeq2Seq]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.

from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)

from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors=""tf"")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the SacreBLEU metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
metric = evaluate.load(""sacrebleu"")

Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the SacreBLEU score:

import numpy as np
def postprocess_text(preds, labels):
     preds = [pred.strip() for pred in preds]
     labels = [[label.strip()] for label in labels]

     return preds, labels

def compute_metrics(eval_preds):
     preds, labels = eval_preds
     if isinstance(preds, tuple):
         preds = preds[0]
     decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)

     labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
     decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
     decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
     result = metric.compute(predictions=decoded_preds, references=decoded_labels)
     result = {""bleu"": result[""score""]}
     prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
     result[""gen_len""] = np.mean(prediction_lens)
     result = {k: round(v, 4) for k, v in result.items()}
     return result

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load T5 with [AutoModelForSeq2SeqLM]:

from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)

At this point, only three steps remain:

Define your training hyperparameters in [Seq2SeqTrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the SacreBLEU metric and save the training checkpoint.
Pass the training arguments to [Seq2SeqTrainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = Seq2SeqTrainingArguments(
     output_dir=""my_awesome_opus_books_model"",
     evaluation_strategy=""epoch"",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
     weight_decay=0.01,
     save_total_limit=3,
     num_train_epochs=2,
     predict_with_generate=True,
     fp16=True,
     push_to_hub=True,
 )
trainer = Seq2SeqTrainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_books[""train""],
     eval_dataset=tokenized_books[""test""],
     tokenizer=tokenizer,
     data_collator=data_collator,
     compute_metrics=compute_metrics,
 )
trainer.train()
`

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)

Then you can load T5 with [TFAutoModelForSeq2SeqLM]:

from transformers import TFAutoModelForSeq2SeqLM
model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     tokenized_books[""train""],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_test_set = model.prepare_tf_dataset(
     tokenized_books[""test""],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

import tensorflow as tf
model.compile(optimizer=optimizer)  # No loss argument!

The last two things to setup before you start training is to compute the SacreBLEU metric from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks.
Pass your compute_metrics function to [~transformers.KerasMetricCallback]:

from transformers.keras_callbacks import KerasMetricCallback
metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)

Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
     output_dir=""my_awesome_opus_books_model"",
     tokenizer=tokenizer,
 )

Then bundle your callbacks together:

callbacks = [metric_callback, push_to_hub_callback]

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=callbacks)

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for translation, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text you'd like to translate to another language. For T5, you need to prefix your input depending on the task you're working on. For translation from English to French, you should prefix your input as shown below:

text = ""translate English to French: Legumes share resources with nitrogen-fixing bacteria.""

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for translation with your model, and pass your text to it:

from transformers import pipeline
translator = pipeline(""translation"", model=""my_awesome_opus_books_model"")
translator(text)
[{'translation_text': 'Legumes partagent des ressources avec des bactéries azotantes.'}]

You can also manually replicate the results of the pipeline if you'd like:

Tokenize the text and return the input_ids as PyTorch tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""my_awesome_opus_books_model"")
inputs = tokenizer(text, return_tensors=""pt"").input_ids

Use the [~transformers.generation_utils.GenerationMixin.generate] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the Text Generation API.

from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained(""my_awesome_opus_books_model"")
outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)

Decode the generated token ids back into text:

tokenizer.decode(outputs[0], skip_special_tokens=True)
'Les lignées partagent des ressources avec des bactéries enfixant l'azote.'
``
</pt>
<tf>
Tokenize the text and return theinput_ids` as TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""my_awesome_opus_books_model"")
inputs = tokenizer(text, return_tensors=""tf"").input_ids

Use the [~transformers.generation_tf_utils.TFGenerationMixin.generate] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the Text Generation API.

from transformers import TFAutoModelForSeq2SeqLM
model = TFAutoModelForSeq2SeqLM.from_pretrained(""my_awesome_opus_books_model"")
outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)

Decode the generated token ids back into text:

tokenizer.decode(outputs[0], skip_special_tokens=True)
'Les lugumes partagent les ressources avec des bactéries fixatrices d'azote.'

"
"

LayoutLMv2 solves the document question-answering task by adding a question-answering head on top of the final hidden
states of the tokens, to predict the positions of the start and end tokens of the
answer. In other words, the problem is treated as extractive question answering: given the context, extract which piece
of information answers the question. The context comes from the output of an OCR engine, here it is Google's Tesseract.
Before you begin, make sure you have all the necessary libraries installed. LayoutLMv2 depends on detectron2, torchvision and tesseract.

pip install -q transformers datasets

pip install 'git+https://github.com/facebookresearch/detectron2.git'
pip install torchvision

sudo apt install tesseract-ocr
pip install -q pytesseract
Once you have installed all of the dependencies, restart your runtime.
We encourage you to share your model with the community. Log in to your Hugging Face account to upload it to the 🤗 Hub.
When prompted, enter your token to log in:

from huggingface_hub import notebook_login
notebook_login()

Let's define some global variables.

model_checkpoint = ""microsoft/layoutlmv2-base-uncased""
batch_size = 4

Load the data
In this guide we use a small sample of preprocessed DocVQA that you can find on 🤗 Hub. If you'd like to use the full
DocVQA dataset, you can register and download it on DocVQA homepage. If you do so, to
proceed with this guide check out how to load files into a 🤗 dataset.

from datasets import load_dataset
dataset = load_dataset(""nielsr/docvqa_1200_examples"")
dataset
DatasetDict({
    train: Dataset({
        features: ['id', 'image', 'query', 'answers', 'words', 'bounding_boxes', 'answer'],
        num_rows: 1000
    })
    test: Dataset({
        features: ['id', 'image', 'query', 'answers', 'words', 'bounding_boxes', 'answer'],
        num_rows: 200
    })
})

As you can see, the dataset is split into train and test sets already. Take a look at a random example to familiarize
yourself with the features.

dataset[""train""].features

Here's what the individual fields represent:
* id: the example's id
* image: a PIL.Image.Image object containing the document image
* query: the question string - natural language asked question, in several languages
* answers: a list of correct answers provided by human annotators
* words and bounding_boxes: the results of OCR, which we will not use here
* answer: an answer matched by a different model which we will not use here
Let's leave only English questions, and drop the answer feature which appears to contain predictions by another model.
We'll also take the first of the answers from the set provided by the annotators. Alternatively, you can randomly sample it.

updated_dataset = dataset.map(lambda example: {""question"": example[""query""][""en""]}, remove_columns=[""query""])
updated_dataset = updated_dataset.map(
     lambda example: {""answer"": example[""answers""][0]}, remove_columns=[""answer"", ""answers""]
 )

Note that the LayoutLMv2 checkpoint that we use in this guide has been trained with max_position_embeddings = 512 (you can
find this information in the checkpoint's config.json file).
We can truncate the examples but to avoid the situation where the answer might be at the end of a large document and end up truncated,
here we'll remove the few examples where the embedding is likely to end up longer than 512.
If most of the documents in your dataset are long, you can implement a sliding window strategy - check out this notebook for details.

updated_dataset = updated_dataset.filter(lambda x: len(x[""words""]) + len(x[""question""].split()) < 512)

At this point let's also remove the OCR features from this dataset. These are a result of OCR for fine-tuning a different
model. They would still require some processing if we wanted to use them, as they do not match the input requirements
of the model we use in this guide. Instead, we can use the [LayoutLMv2Processor] on the original data for both OCR and
tokenization. This way we'll get the inputs that match model's expected input. If you want to process images manually,
check out the LayoutLMv2 model documentation to learn what input format the model expects.

updated_dataset = updated_dataset.remove_columns(""words"")
updated_dataset = updated_dataset.remove_columns(""bounding_boxes"")

Finally, the data exploration won't be complete if we don't peek at an image example.

updated_dataset[""train""][11][""image""]

Preprocess the data
The Document Question Answering task is a multimodal task, and you need to make sure that the inputs from each modality
are preprocessed according to the model's expectations. Let's start by loading the [LayoutLMv2Processor], which internally combines an image processor that can handle image data and a tokenizer that can encode text data.

from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained(model_checkpoint)

Preprocessing document images
First, let's prepare the document images for the model with the help of the image_processor from the processor.
By default, image processor resizes the images to 224x224, makes sure they have the correct order of color channels,
applies OCR with tesseract to get words and normalized bounding boxes. In this tutorial, all of these defaults are exactly what we need.
Write a function that applies the default image processing to a batch of images and returns the results of OCR.

image_processor = processor.image_processor
def get_ocr_words_and_boxes(examples):
     images = [image.convert(""RGB"") for image in examples[""image""]]
     encoded_inputs = image_processor(images)

     examples[""image""] = encoded_inputs.pixel_values
     examples[""words""] = encoded_inputs.words
     examples[""boxes""] = encoded_inputs.boxes
     return examples

To apply this preprocessing to the entire dataset in a fast way, use [~datasets.Dataset.map].

dataset_with_ocr = updated_dataset.map(get_ocr_words_and_boxes, batched=True, batch_size=2)

Preprocessing text data
Once we have applied OCR to the images, we need to encode the text part of the dataset to prepare it for the model.
This involves converting the words and boxes that we got in the previous step to token-level input_ids, attention_mask,
token_type_ids and bbox. For preprocessing text, we'll need the tokenizer from the processor.

tokenizer = processor.tokenizer

On top of the preprocessing mentioned above, we also need to add the labels for the model. For xxxForQuestionAnswering models
in 🤗 Transformers, the labels consist of the start_positions and end_positions, indicating which token is at the
start and which token is at the end of the answer.
Let's start with that. Define a helper function that can find a sublist (the answer split into words) in a larger list (the words list).
This function will take two lists as input, words_list and answer_list. It will then iterate over the words_list and check
if the current word in the words_list (words_list[i]) is equal to the first word of answer_list (answer_list[0]) and if
the sublist of words_list starting from the current word and of the same length as answer_list is equal to answer_list.
If this condition is true, it means that a match has been found, and the function will record the match, its starting index (idx),
and its ending index (idx + len(answer_list) - 1). If more than one match was found, the function will return only the first one.
If no match is found, the function returns (None, 0, and 0).

def subfinder(words_list, answer_list):
     matches = []
     start_indices = []
     end_indices = []
     for idx, i in enumerate(range(len(words_list))):
         if words_list[i] == answer_list[0] and words_list[i : i + len(answer_list)] == answer_list:
             matches.append(answer_list)
             start_indices.append(idx)
             end_indices.append(idx + len(answer_list) - 1)
     if matches:
         return matches[0], start_indices[0], end_indices[0]
     else:
         return None, 0, 0

To illustrate how this function finds the position of the answer, let's use it on an example:

example = dataset_with_ocr[""train""][1]
words = [word.lower() for word in example[""words""]]
match, word_idx_start, word_idx_end = subfinder(words, example[""answer""].lower().split())
print(""Question: "", example[""question""])
print(""Words:"", words)
print(""Answer: "", example[""answer""])
print(""start_index"", word_idx_start)
print(""end_index"", word_idx_end)
Question:  Who is in  cc in this letter?
Words: ['wie', 'baw', 'brown', '&', 'williamson', 'tobacco', 'corporation', 'research', '&', 'development', 'internal', 'correspondence', 'to:', 'r.', 'h.', 'honeycutt', 'ce:', 't.f.', 'riehl', 'from:', '.', 'c.j.', 'cook', 'date:', 'may', '8,', '1995', 'subject:', 'review', 'of', 'existing', 'brainstorming', 'ideas/483', 'the', 'major', 'function', 'of', 'the', 'product', 'innovation', 'graup', 'is', 'to', 'develop', 'marketable', 'nove!', 'products', 'that', 'would', 'be', 'profitable', 'to', 'manufacture', 'and', 'sell.', 'novel', 'is', 'defined', 'as:', 'of', 'a', 'new', 'kind,', 'or', 'different', 'from', 'anything', 'seen', 'or', 'known', 'before.', 'innovation', 'is', 'defined', 'as:', 'something', 'new', 'or', 'different', 'introduced;', 'act', 'of', 'innovating;', 'introduction', 'of', 'new', 'things', 'or', 'methods.', 'the', 'products', 'may', 'incorporate', 'the', 'latest', 'technologies,', 'materials', 'and', 'know-how', 'available', 'to', 'give', 'then', 'a', 'unique', 'taste', 'or', 'look.', 'the', 'first', 'task', 'of', 'the', 'product', 'innovation', 'group', 'was', 'to', 'assemble,', 'review', 'and', 'categorize', 'a', 'list', 'of', 'existing', 'brainstorming', 'ideas.', 'ideas', 'were', 'grouped', 'into', 'two', 'major', 'categories', 'labeled', 'appearance', 'and', 'taste/aroma.', 'these', 'categories', 'are', 'used', 'for', 'novel', 'products', 'that', 'may', 'differ', 'from', 'a', 'visual', 'and/or', 'taste/aroma', 'point', 'of', 'view', 'compared', 'to', 'canventional', 'cigarettes.', 'other', 'categories', 'include', 'a', 'combination', 'of', 'the', 'above,', 'filters,', 'packaging', 'and', 'brand', 'extensions.', 'appearance', 'this', 'category', 'is', 'used', 'for', 'novel', 'cigarette', 'constructions', 'that', 'yield', 'visually', 'different', 'products', 'with', 'minimal', 'changes', 'in', 'smoke', 'chemistry', 'two', 'cigarettes', 'in', 'cne.', 'emulti-plug', 'te', 'build', 'yaur', 'awn', 'cigarette.', 'eswitchable', 'menthol', 'or', 'non', 'menthol', 'cigarette.', 'cigarettes', 'with', 'interspaced', 'perforations', 'to', 'enable', 'smoker', 'to', 'separate', 'unburned', 'section', 'for', 'future', 'smoking.', '«short', 'cigarette,', 'tobacco', 'section', '30', 'mm.', '«extremely', 'fast', 'buming', 'cigarette.', '«novel', 'cigarette', 'constructions', 'that', 'permit', 'a', 'significant', 'reduction', 'iretobacco', 'weight', 'while', 'maintaining', 'smoking', 'mechanics', 'and', 'visual', 'characteristics.', 'higher', 'basis', 'weight', 'paper:', 'potential', 'reduction', 'in', 'tobacco', 'weight.', '«more', 'rigid', 'tobacco', 'column;', 'stiffing', 'agent', 'for', 'tobacco;', 'e.g.', 'starch', 'colored', 'tow', 'and', 'cigarette', 'papers;', 'seasonal', 'promotions,', 'e.g.', 'pastel', 'colored', 'cigarettes', 'for', 'easter', 'or', 'in', 'an', 'ebony', 'and', 'ivory', 'brand', 'containing', 'a', 'mixture', 'of', 'all', 'black', '(black', 'paper', 'and', 'tow)', 'and', 'ail', 'white', 'cigarettes.', '499150498']
Answer:  T.F. Riehl
start_index 17
end_index 18

Once examples are encoded, however, they will look like this:

encoding = tokenizer(example[""question""], example[""words""], example[""boxes""])
tokenizer.decode(encoding[""input_ids""])
[CLS] who is in cc in this letter? [SEP] wie baw brown & williamson tobacco corporation research & development 

We'll need to find the position of the answer in the encoded input.
* token_type_ids tells us which tokens are part of the question, and which ones are part of the document's words.
* tokenizer.cls_token_id will help find the special token at the beginning of the input.
* word_ids will help match the answer found in the original words to the same answer in the full encoded input and determine
the start/end position of the answer in the encoded input.
With that in mind, let's create a function to encode a batch of examples in the dataset:

def encode_dataset(examples, max_length=512):
     questions = examples[""question""]
     words = examples[""words""]
     boxes = examples[""boxes""]
     answers = examples[""answer""]

     # encode the batch of examples and initialize the start_positions and end_positions
     encoding = tokenizer(questions, words, boxes, max_length=max_length, padding=""max_length"", truncation=True)
     start_positions = []
     end_positions = []
     # loop through the examples in the batch
     for i in range(len(questions)):
         cls_index = encoding[""input_ids""][i].index(tokenizer.cls_token_id)
         # find the position of the answer in example's words
         words_example = [word.lower() for word in words[i]]
         answer = answers[i]
         match, word_idx_start, word_idx_end = subfinder(words_example, answer.lower().split())
         if match:
             # if match is found, use token_type_ids to find where words start in the encoding
             token_type_ids = encoding[""token_type_ids""][i]
             token_start_index = 0
             while token_type_ids[token_start_index] != 1:
                 token_start_index += 1
             token_end_index = len(encoding[""input_ids""][i]) - 1
             while token_type_ids[token_end_index] != 1:
                 token_end_index -= 1
             word_ids = encoding.word_ids(i)[token_start_index : token_end_index + 1]
             start_position = cls_index
             end_position = cls_index
             # loop over word_ids and increase token_start_index until it matches the answer position in words
             # once it matches, save the token_start_index as the start_position of the answer in the encoding
             for id in word_ids:
                 if id == word_idx_start:
                     start_position = token_start_index
                 else:
                     token_start_index += 1
             # similarly loop over word_ids starting from the end to find the end_position of the answer
             for id in word_ids[::-1]:
                 if id == word_idx_end:
                     end_position = token_end_index
                 else:
                     token_end_index -= 1
             start_positions.append(start_position)
             end_positions.append(end_position)
         else:
             start_positions.append(cls_index)
             end_positions.append(cls_index)
     encoding[""image""] = examples[""image""]
     encoding[""start_positions""] = start_positions
     encoding[""end_positions""] = end_positions
     return encoding

Now that we have this preprocessing function, we can encode the entire dataset:

encoded_train_dataset = dataset_with_ocr[""train""].map(
     encode_dataset, batched=True, batch_size=2, remove_columns=dataset_with_ocr[""train""].column_names
 )
encoded_test_dataset = dataset_with_ocr[""test""].map(
     encode_dataset, batched=True, batch_size=2, remove_columns=dataset_with_ocr[""test""].column_names
 )

Let's check what the features of the encoded dataset look like:

encoded_train_dataset.features
{'image': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='uint8', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),
 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
 'bbox': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None),
 'start_positions': Value(dtype='int64', id=None),
 'end_positions': Value(dtype='int64', id=None)}

Evaluation
Evaluation for document question answering requires a significant amount of postprocessing. To avoid taking up too much
of your time, this guide skips the evaluation step. The [Trainer] still calculates the evaluation loss during training so
you're not completely in the dark about your model's performance. Extractive question answering is typically evaluated using F1/exact match.
If you'd like to implement it yourself, check out the Question Answering chapter
of the Hugging Face course for inspiration.
Train
Congratulations! You've successfully navigated the toughest part of this guide and now you are ready to train your own model.
Training involves the following steps:
* Load the model with [AutoModelForDocumentQuestionAnswering] using the same checkpoint as in the preprocessing.
* Define your training hyperparameters in [TrainingArguments].
* Define a function to batch examples together, here the [DefaultDataCollator] will do just fine
* Pass the training arguments to [Trainer] along with the model, dataset, and data collator.
* Call [~Trainer.train] to finetune your model.

from transformers import AutoModelForDocumentQuestionAnswering
model = AutoModelForDocumentQuestionAnswering.from_pretrained(model_checkpoint)

In the [TrainingArguments] use output_dir to specify where to save your model, and configure hyperparameters as you see fit.
If you wish to share your model with the community, set push_to_hub to True (you must be signed in to Hugging Face to upload your model).
In this case the output_dir will also be the name of the repo where your model checkpoint will be pushed.

from transformers import TrainingArguments
REPLACE THIS WITH YOUR REPO ID
repo_id = ""MariaK/layoutlmv2-base-uncased_finetuned_docvqa""
training_args = TrainingArguments(
     output_dir=repo_id,
     per_device_train_batch_size=4,
     num_train_epochs=20,
     save_steps=200,
     logging_steps=50,
     evaluation_strategy=""steps"",
     learning_rate=5e-5,
     save_total_limit=2,
     remove_unused_columns=False,
     push_to_hub=True,
 )

Define a simple data collator to batch examples together.

from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()

Finally, bring everything together, and call [~Trainer.train]:

from transformers import Trainer
trainer = Trainer(
     model=model,
     args=training_args,
     data_collator=data_collator,
     train_dataset=encoded_train_dataset,
     eval_dataset=encoded_test_dataset,
     tokenizer=processor,
 )
trainer.train()

To add the final model to 🤗 Hub, create a model card and call push_to_hub:

trainer.create_model_card()
trainer.push_to_hub()

Inference
Now that you have finetuned a LayoutLMv2 model, and uploaded it to the 🤗 Hub, you can use it for inference. The simplest
way to try out your finetuned model for inference is to use it in a [Pipeline].
Let's take an example:

example = dataset[""test""][2]
question = example[""query""][""en""]
image = example[""image""]
print(question)
print(example[""answers""])
'Who is ‘presiding’ TRRF GENERAL SESSION (PART 1)?'
['TRRF Vice President', 'lee a. waller']

Next, instantiate a pipeline for
document question answering with your model, and pass the image + question combination to it.

from transformers import pipeline
qa_pipeline = pipeline(""document-question-answering"", model=""MariaK/layoutlmv2-base-uncased_finetuned_docvqa"")
qa_pipeline(image, question)
[{'score': 0.9949808120727539,
  'answer': 'Lee A. Waller',
  'start': 55,
  'end': 57}]

You can also manually replicate the results of the pipeline if you'd like:
1. Take an image and a question, prepare them for the model using the processor from your model.
2. Forward the result or preprocessing through the model.
3. The model returns start_logits and end_logits, which indicate which token is at the start of the answer and
which token is at the end of the answer. Both have shape (batch_size, sequence_length).
4. Take an argmax on the last dimension of both the start_logits and end_logits to get the predicted start_idx and end_idx.
5. Decode the answer with the tokenizer.

import torch
from transformers import AutoProcessor
from transformers import AutoModelForDocumentQuestionAnswering
processor = AutoProcessor.from_pretrained(""MariaK/layoutlmv2-base-uncased_finetuned_docvqa"")
model = AutoModelForDocumentQuestionAnswering.from_pretrained(""MariaK/layoutlmv2-base-uncased_finetuned_docvqa"")
with torch.no_grad():
     encoding = processor(image.convert(""RGB""), question, return_tensors=""pt"")
     outputs = model(**encoding)
     start_logits = outputs.start_logits
     end_logits = outputs.end_logits
     predicted_start_idx = start_logits.argmax(-1).item()
     predicted_end_idx = end_logits.argmax(-1).item()
processor.tokenizer.decode(encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1])
'lee a. waller'

"
"
Utilities for Generation
This page lists all the utility functions used by [~generation.GenerationMixin.generate],
[~generation.GenerationMixin.greedy_search],
[~generation.GenerationMixin.contrastive_search],
[~generation.GenerationMixin.sample],
[~generation.GenerationMixin.beam_search],
[~generation.GenerationMixin.beam_sample],
[~generation.GenerationMixin.group_beam_search], and
[~generation.GenerationMixin.constrained_beam_search].
Most of those are only useful if you are studying the code of the generate methods in the library.
Generate Outputs
The output of [~generation.GenerationMixin.generate] is an instance of a subclass of
[~utils.ModelOutput]. This output is a data structure containing all the information returned
by [~generation.GenerationMixin.generate], but that can also be used as tuple or dictionary.
Here's an example:
thon
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained(""gpt2"")
model = GPT2LMHeadModel.from_pretrained(""gpt2"")
inputs = tokenizer(""Hello, my dog is cute and "", return_tensors=""pt"")
generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)

The generation_output object is a [~generation.GreedySearchDecoderOnlyOutput], as we can
see in the documentation of that class below, it means it has the following attributes:

sequences: the generated sequences of tokens
scores (optional): the prediction scores of the language modelling head, for each generation step
hidden_states (optional): the hidden states of the model, for each generation step
attentions (optional): the attention weights of the model, for each generation step

Here we have the scores since we passed along output_scores=True, but we don't have hidden_states and
attentions because we didn't pass output_hidden_states=True or output_attentions=True.
You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get None. Here for instance generation_output.scores are all the generated prediction scores of the
language modeling head, and generation_output.attentions is None.
When using our generation_output object as a tuple, it only keeps the attributes that don't have None values.
Here, for instance, it has two elements, loss then logits, so
python
generation_output[:2]
will return the tuple (generation_output.sequences, generation_output.scores) for instance.
When using our generation_output object as a dictionary, it only keeps the attributes that don't have None
values. Here, for instance, it has two keys that are sequences and scores.
We document here all output types.
GreedySearchOutput
[[autodoc]] generation.GreedySearchDecoderOnlyOutput
[[autodoc]] generation.GreedySearchEncoderDecoderOutput
[[autodoc]] generation.FlaxGreedySearchOutput
SampleOutput
[[autodoc]] generation.SampleDecoderOnlyOutput
[[autodoc]] generation.SampleEncoderDecoderOutput
[[autodoc]] generation.FlaxSampleOutput
BeamSearchOutput
[[autodoc]] generation.BeamSearchDecoderOnlyOutput
[[autodoc]] generation.BeamSearchEncoderDecoderOutput
BeamSampleOutput
[[autodoc]] generation.BeamSampleDecoderOnlyOutput
[[autodoc]] generation.BeamSampleEncoderDecoderOutput
LogitsProcessor
A [LogitsProcessor] can be used to modify the prediction scores of a language model head for
generation.
[[autodoc]] LogitsProcessor
    - call
[[autodoc]] LogitsProcessorList
    - call
[[autodoc]] LogitsWarper
    - call
[[autodoc]] MinLengthLogitsProcessor
    - call
[[autodoc]] MinNewTokensLengthLogitsProcessor
    - call
[[autodoc]] TemperatureLogitsWarper
    - call
[[autodoc]] RepetitionPenaltyLogitsProcessor
    - call
[[autodoc]] TopPLogitsWarper
    - call
[[autodoc]] TopKLogitsWarper
    - call
[[autodoc]] TypicalLogitsWarper
    - call
[[autodoc]] NoRepeatNGramLogitsProcessor
    - call
[[autodoc]] SequenceBiasLogitsProcessor
    - call
[[autodoc]] NoBadWordsLogitsProcessor
    - call
[[autodoc]] PrefixConstrainedLogitsProcessor
    - call
[[autodoc]] HammingDiversityLogitsProcessor
    - call
[[autodoc]] ForcedBOSTokenLogitsProcessor
    - call
[[autodoc]] ForcedEOSTokenLogitsProcessor
    - call
[[autodoc]] InfNanRemoveLogitsProcessor
    - call
[[autodoc]] TFLogitsProcessor
    - call
[[autodoc]] TFLogitsProcessorList
    - call
[[autodoc]] TFLogitsWarper
    - call
[[autodoc]] TFTemperatureLogitsWarper
    - call
[[autodoc]] TFTopPLogitsWarper
    - call
[[autodoc]] TFTopKLogitsWarper
    - call
[[autodoc]] TFMinLengthLogitsProcessor
    - call
[[autodoc]] TFNoBadWordsLogitsProcessor
    - call
[[autodoc]] TFNoRepeatNGramLogitsProcessor
    - call
[[autodoc]] TFRepetitionPenaltyLogitsProcessor
    - call
[[autodoc]] TFForcedBOSTokenLogitsProcessor
    - call
[[autodoc]] TFForcedEOSTokenLogitsProcessor
    - call
[[autodoc]] FlaxLogitsProcessor
    - call
[[autodoc]] FlaxLogitsProcessorList
    - call
[[autodoc]] FlaxLogitsWarper
    - call
[[autodoc]] FlaxTemperatureLogitsWarper
    - call
[[autodoc]] FlaxTopPLogitsWarper
    - call
[[autodoc]] FlaxTopKLogitsWarper
    - call
[[autodoc]] FlaxForcedBOSTokenLogitsProcessor
    - call
[[autodoc]] FlaxForcedEOSTokenLogitsProcessor
    - call
[[autodoc]] FlaxMinLengthLogitsProcessor
    - call
StoppingCriteria
A [StoppingCriteria] can be used to change when to stop generation (other than EOS token).
[[autodoc]] StoppingCriteria
    - call
[[autodoc]] StoppingCriteriaList
    - call
[[autodoc]] MaxLengthCriteria
    - call
[[autodoc]] MaxTimeCriteria
    - call
Constraints
A [Constraint] can be used to force the generation to include specific tokens or sequences in the output.
[[autodoc]] Constraint
[[autodoc]] PhrasalConstraint
[[autodoc]] DisjunctiveConstraint
[[autodoc]] ConstraintListState
BeamSearch
[[autodoc]] BeamScorer
    - process
    - finalize
[[autodoc]] BeamSearchScorer
    - process
    - finalize
[[autodoc]] ConstrainedBeamSearchScorer
    - process
    - finalize
Utilities
[[autodoc]] top_k_top_p_filtering
[[autodoc]] tf_top_k_top_p_filtering
Streamers
[[autodoc]] TextStreamer
[[autodoc]] TextIteratorStreamer"
"
Contribute to 🤗 Transformers
Everyone is welcome to contribute, and we value everybody's contribution. Code
contributions are not the only way to help the community. Answering questions, helping
others, and improving the documentation are also immensely valuable.
It also helps us if you spread the word! Reference the library in blog posts
about the awesome projects it made possible, shout out on Twitter every time it has
helped you, or simply ⭐️ the repository to say thank you.
However you choose to contribute, please be mindful and respect our
code of conduct.
This guide was heavily inspired by the awesome scikit-learn guide to contributing.
Ways to contribute
There are several ways you can contribute to 🤗 Transformers:

Fix outstanding issues with the existing code.
Submit issues related to bugs or desired new features.
Implement new models.
Contribute to the examples or to the documentation.

If you don't know where to start, there is a special Good First
Issue listing. It will give you a list of
open issues that are beginner-friendly and help you start contributing to open-source. Just comment in the issue that you'd like to work
on it. 
For something slightly more challenging, you can also take a look at the Good Second Issue list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀

All contributions are equally valuable to the community. 🥰

Fixing outstanding issues
If you notice an issue with the existing code and have a fix in mind, feel free to start contributing and open a Pull Request!
Submitting a bug-related issue or feature request
Do your best to follow these guidelines when submitting a bug-related issue or a feature
request. It will make it easier for us to come back to you quickly and with good
feedback.
Did you find a bug?
The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter.
Before you report an issue, we would really appreciate it if you could make sure the bug was not
already reported (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask on the forum first. This helps us respond quicker to fixing issues related to the library versus general questions.
Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:

Your OS type and version and Python, PyTorch and
  TensorFlow versions when applicable.
A short, self-contained, code snippet that allows us to reproduce the bug in
  less than 30s.
The full traceback if an exception is raised.
Attach any other additional information, like screenshots, you think may help.

To get the OS and software versions automatically, run the following command:

transformers-cli env
You can also run the same command from the root of the repository:

python src/transformers/commands/transformers_cli.py env
Do you want a new feature?
If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe:

What is the motivation behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community?

Whatever it is, we'd love to hear about it!

Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you.
Provide a code snippet that demonstrates the features usage.
If the feature is related to a paper, please include a link.

If your issue is well written we're already 80% of the way there by the time you create it.
We have added templates to help you get started with your issue.
Do you want to implement a new model?
New models are constantly released and if you want to implement a new model, please provide the following information

A short description of the model and link to the paper.
Link to the implementation if it is open-sourced.
Link to the model weights if they are available.

If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers!
We have added a detailed guide and templates to help you get started with adding a new model, and we also have a more technical guide for how to add a model to 🤗 Transformers.
Do you want to add documentation?
We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested!
For more details about how to generate, build, and write the documentation, take a look at the documentation README.
Create a Pull Request
Before writing any code, we strongly advise you to search through the existing PRs or
issues to make sure nobody is already working on the same thing. If you are
unsure, it is always a good idea to open an issue to get some feedback.
You will need basic git proficiency to contribute to
🤗 Transformers. While git is not the easiest tool to use, it has the greatest
manual. Type git --help in a shell and enjoy! If you prefer books, Pro
Git is a very good reference.
You'll need Python 3.8 or above to contribute to 🤗 Transformers. Follow the steps below to start contributing:

Fork the repository by
   clicking on the Fork button on the repository's page. This creates a copy of the code
   under your GitHub user account.

Clone your fork to your local disk, and add the base repository as a remote:

   git clone git@github.com:<your Github handle>/transformers.git
   cd transformers
   git remote add upstream https://github.com/huggingface/transformers.git

Create a new branch to hold your development changes:

   git checkout -b a-descriptive-name-for-my-changes
🚨 Do not work on the main branch!

Set up a development environment by running the following command in a virtual environment:

   pip install -e "".[dev]""
If 🤗 Transformers was already installed in the virtual environment, remove
   it with pip uninstall transformers before reinstalling it in editable
   mode with the -e flag.
Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
   failure with this command. If that's the case make sure to install the Deep Learning framework you are working with
   (PyTorch, TensorFlow and/or Flax) then do:

   pip install -e "".[quality]""
which should be enough for most use cases.

Develop the features on your branch.

As you work on your code, you should make sure the test suite
   passes. Run the tests impacted by your changes like this:

   pytest tests/<TEST_TO_RUN>.py
For more information about tests, check out the
   Testing guide.
🤗 Transformers relies on black and ruff to format its source code
   consistently. After you make changes, apply automatic style corrections and code verifications
   that can't be automated in one go with:

   make fixup
This target is also optimized to only work with files modified by the PR you're working on.
If you prefer to run the checks one after the other, the following command applies the
   style corrections:

   make style
🤗 Transformers also uses ruff and a few custom scripts to check for coding mistakes. Quality
   controls are run by the CI, but you can run the same checks with:

   make quality
Finally, we have a lot of scripts to make sure we didn't forget to update
   some files when adding a new model. You can run these scripts with:

   make repo-consistency
To learn more about those checks and how to fix any issues with them, check out the
   Checks on a Pull Request guide.
If you're modifying documents under docs/source directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check
   make sure you install the documentation builder:

   pip install "".[docs]""
Run the following command from the root of the repository:

   doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build
This will build the documentation in the ~/tmp/test-build folder where you can inspect the generated
   Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request.
Once you're happy with your changes, add changed files with git add and
   record your changes locally with git commit:

   git add modified_file.py
   git commit
Please remember to write good commit
   messages to clearly communicate the changes you made!
To keep your copy of the code up to date with the original
   repository, rebase your branch on upstream/branch before you open a pull request or if requested by a maintainer:

   git fetch upstream
   git rebase upstream/main
Push your changes to your branch:

   git push -u origin a-descriptive-name-for-my-changes
If you've already opened a pull request, you'll need to force push with the --force flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally.

Now you can go to your fork of the repository on GitHub and click on Pull request to open a pull request. Make sure you tick off all the boxes in our checklist below. When you're ready, you can send your changes to the project maintainers for review.

It's ok if maintainers request changes, it happens to our core contributors
   too! So everyone can see the changes in the pull request, work in your local
   branch and push the changes to your fork. They will automatically appear in
   the pull request.

Pull request checklist
☐ The pull request title should summarize your contribution.
☐ If your pull request addresses an issue, please mention the issue number in the pull
request description to make sure they are linked (and people viewing the issue know you
are working on it).
☐ To indicate a work in progress please prefix the title with [WIP]. These are
useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.
☐ Make sure existing tests pass.
☐ If adding a new feature, also add tests for it.
   - If you are adding a new model, make sure you use
     ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,) to trigger the common tests.
   - If you are adding new @slow tests, make sure they pass using
     RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py.
   - If you are adding a new tokenizer, write tests and make sure
     RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py passes.
   - CircleCI does not run the slow tests, but GitHub Actions does every night!
☐ All public methods must have informative docstrings (see
modeling_bert.py
for an example).
☐ Due to the rapidly growing repository, don't add any images, videos and other
non-text files that'll significantly weigh down the repository. Instead, use a Hub
repository such as hf-internal-testing
to host these files and reference them by URL. We recommend placing documentation
related images in the following repository:
huggingface/documentation-images.
You can open a PR on this dataset repostitory and ask a Hugging Face member to merge it.
For more information about the checks run on a pull request, take a look at our Checks on a Pull Request guide.
Tests
An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
the tests folder and examples tests in the
examples folder.
We like pytest and pytest-xdist because it's faster. From the root of the
repository, specify a path to a subfolder or a test file to run the test.

python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model
Similarly, for the examples directory, specify a path to a subfolder or test file to run the test. For example, the following command tests the text classification subfolder in the PyTorch examples directory:

pip install -r examples/xxx/requirements.txt  # only needed the first time
python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification
In fact, this is actually how our make test and make test-examples commands are implemented (not including the pip install)!
You can also specify a smaller set of tests in order to test only the feature
you're working on.
By default, slow tests are skipped but you can set the RUN_SLOW environment variable to
yes to run them. This will download many gigabytes of models so make sure you
have enough disk space, a good internet connection or a lot of patience!

Remember to specify a path to a subfolder or a test file to run the test. Otherwise, you'll run all the tests in the tests or examples folder, which will take a very long time!

RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model
RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification
Like the slow tests, there are other environment variables available which not enabled by default during testing:
- RUN_CUSTOM_TOKENIZERS: Enables tests for custom tokenizers.
- RUN_PT_FLAX_CROSS_TESTS: Enables tests for PyTorch + Flax integration.
- RUN_PT_TF_CROSS_TESTS: Enables tests for TensorFlow + PyTorch integration.
More environment variables and additional information can be found in the testing_utils.py.
🤗 Transformers uses pytest as a test runner only. It doesn't use any
pytest-specific features in the test suite itself.
This means unittest is fully supported. Here's how to run tests with
unittest:

python -m unittest discover -s tests -t . -v
python -m unittest discover -s examples -t examples -v
Style guide
For documentation strings, 🤗 Transformers follows the Google Python Style Guide.
Check our documentation writing guide
for more information.
Develop on Windows
On Windows (unless you're working in Windows Subsystem for Linux or WSL), you need to configure git to transform Windows CRLF line endings to Linux LF line endings:

git config core.autocrlf input
One way to run the make command on Windows is with MSYS2:

Download MSYS2, and we assume it's installed in C:\msys64.
Open the command line C:\msys64\msys2.exe (it should be available from the Start menu).
Run in the shell: pacman -Syu and install make with pacman -S make.
Add C:\msys64\usr\bin to your PATH environment variable.

You can now use make from any terminal (Powershell, cmd.exe, etc.)! 🎉
Sync a forked repository with upstream main (the Hugging Face repository)
When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs.

When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.
If a PR is absolutely necessary, use the following steps after checking out your branch:

git checkout -b your-branch-for-syncing
git pull --squash --no-commit upstream main
git commit -m '<your message without GitHub references>'
git push --set-upstream origin your-branch-for-syncing"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

from huggingface_hub import notebook_login
notebook_login()

Load SQuAD dataset
Start by loading a smaller subset of the SQuAD dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.

from datasets import load_dataset
squad = load_dataset(""squad"", split=""train[:5000]"")

Split the dataset's train split into a train and test set with the [~datasets.Dataset.train_test_split] method:

squad = squad.train_test_split(test_size=0.2)

Then take a look at an example:

squad[""train""][0]
{'answers': {'answer_start': [515], 'text': ['Saint Bernadette Soubirous']},
 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend ""Venite Ad Me Omnes"". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
 'id': '5733be284776f41900661182',
 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
 'title': 'University_of_Notre_Dame'
}

There are several important fields here:

answers: the starting location of the answer token and the answer text.
context: background information from which the model needs to extract the answer.
question: the question a model should answer.

Preprocess

The next step is to load a DistilBERT tokenizer to process the question and context fields:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""distilbert-base-uncased"")

There are a few preprocessing steps particular to question answering tasks you should be aware of:

Some examples in a dataset may have a very long context that exceeds the maximum input length of the model. To deal with longer sequences, truncate only the context by setting truncation=""only_second"".
Next, map the start and end positions of the answer to the original context by setting
   return_offset_mapping=True.
With the mapping in hand, now you can find the start and end tokens of the answer. Use the [~tokenizers.Encoding.sequence_ids] method to
   find which part of the offset corresponds to the question and which corresponds to the context.

Here is how you can create a function to truncate and map the start and end tokens of the answer to the context:

def preprocess_function(examples):
     questions = [q.strip() for q in examples[""question""]]
     inputs = tokenizer(
         questions,
         examples[""context""],
         max_length=384,
         truncation=""only_second"",
         return_offsets_mapping=True,
         padding=""max_length"",
     )

     offset_mapping = inputs.pop(""offset_mapping"")
     answers = examples[""answers""]
     start_positions = []
     end_positions = []
     for i, offset in enumerate(offset_mapping):
         answer = answers[i]
         start_char = answer[""answer_start""][0]
         end_char = answer[""answer_start""][0] + len(answer[""text""][0])
         sequence_ids = inputs.sequence_ids(i)
         # Find the start and end of the context
         idx = 0
         while sequence_ids[idx] != 1:
             idx += 1
         context_start = idx
         while sequence_ids[idx] == 1:
             idx += 1
         context_end = idx - 1
         # If the answer is not fully inside the context, label it (0, 0)
         if offset[context_start][0] > end_char or offset[context_end][1] < start_char:
             start_positions.append(0)
             end_positions.append(0)
         else:
             # Otherwise it's the start and end token positions
             idx = context_start
             while idx <= context_end and offset[idx][0] <= start_char:
                 idx += 1
             start_positions.append(idx - 1)
             idx = context_end
             while idx >= context_start and offset[idx][1] >= end_char:
                 idx -= 1
             end_positions.append(idx + 1)
     inputs[""start_positions""] = start_positions
     inputs[""end_positions""] = end_positions
     return inputs

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once. Remove any columns you don't need:

tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad[""train""].column_names)

Now create a batch of examples using [DefaultDataCollator]. Unlike other data collators in 🤗 Transformers, the [DefaultDataCollator] does not apply any additional preprocessing such as padding.

from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()
</pt>
<tf>py
from transformers import DefaultDataCollator
data_collator = DefaultDataCollator(return_tensors=""tf"")

Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load DistilBERT with [AutoModelForQuestionAnswering]:

from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
model = AutoModelForQuestionAnswering.from_pretrained(""distilbert-base-uncased"")

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model).
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, and data collator.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_qa_model"",
     evaluation_strategy=""epoch"",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
     num_train_epochs=3,
     weight_decay=0.01,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_squad[""train""],
     eval_dataset=tokenized_squad[""test""],
     tokenizer=tokenizer,
     data_collator=data_collator,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer
batch_size = 16
num_epochs = 2
total_train_steps = (len(tokenized_squad[""train""]) // batch_size) * num_epochs
optimizer, schedule = create_optimizer(
     init_lr=2e-5,
     num_warmup_steps=0,
     num_train_steps=total_train_steps,
 )

Then you can load DistilBERT with [TFAutoModelForQuestionAnswering]:

from transformers import TFAutoModelForQuestionAnswering
model = TFAutoModelForQuestionAnswering(""distilbert-base-uncased"")

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     tokenized_squad[""train""],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_validation_set = model.prepare_tf_dataset(
     tokenized_squad[""test""],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile:

import tensorflow as tf
model.compile(optimizer=optimizer)

The last thing to setup before you start training is to provide a way to push your model to the Hub. This can be done by specifying where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
callback = PushToHubCallback(
     output_dir=""my_awesome_qa_model"",
     tokenizer=tokenizer,
 )

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callback to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=[callback])

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for question answering, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Evaluate
Evaluation for question answering requires a significant amount of postprocessing. To avoid taking up too much of your time, this guide skips the evaluation step. The [Trainer] still calculates the evaluation loss during training so you're not completely in the dark about your model's performance.
If have more time and you're interested in how to evaluate your model for question answering, take a look at the Question answering chapter from the 🤗 Hugging Face Course!
Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with a question and some context you'd like the model to predict:

question = ""How many programming languages does BLOOM support?""
context = ""BLOOM has 176 billion parameters and can generate text in 46 languages natural languages and 13 programming languages.""

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for question answering with your model, and pass your text to it:

from transformers import pipeline
question_answerer = pipeline(""question-answering"", model=""my_awesome_qa_model"")
question_answerer(question=question, context=context)
{'score': 0.2058267742395401,
 'start': 10,
 'end': 95,
 'answer': '176 billion parameters and can generate text in 46 languages natural languages and 13'}

You can also manually replicate the results of the pipeline if you'd like:

Tokenize the text and return PyTorch tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""my_awesome_qa_model"")
inputs = tokenizer(question, context, return_tensors=""pt"")

Pass your inputs to the model and return the logits:

import torch
from transformers import AutoModelForQuestionAnswering
model = AutoModelForQuestionAnswering.from_pretrained(""my_awesome_qa_model"")
with torch.no_grad():
     outputs = model(**inputs)

Get the highest probability from the model output for the start and end positions:

answer_start_index = outputs.start_logits.argmax()
answer_end_index = outputs.end_logits.argmax()

Decode the predicted tokens to get the answer:

predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
tokenizer.decode(predict_answer_tokens)
'176 billion parameters and can generate text in 46 languages natural languages and 13'

Tokenize the text and return TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""my_awesome_qa_model"")
inputs = tokenizer(question, text, return_tensors=""tf"")

Pass your inputs to the model and return the logits:

from transformers import TFAutoModelForQuestionAnswering
model = TFAutoModelForQuestionAnswering.from_pretrained(""my_awesome_qa_model"")
outputs = model(**inputs)

Get the highest probability from the model output for the start and end positions:

answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])

Decode the predicted tokens to get the answer:

predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
tokenizer.decode(predict_answer_tokens)
'176 billion parameters and can generate text in 46 languages natural languages and 13'

"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install -q datasets transformers evaluate timm albumentations
You'll use 🤗 Datasets to load a dataset from the Hugging Face Hub, 🤗 Transformers to train your model,
and albumentations to augment the data. timm is currently required to load a convolutional backbone for the DETR model.
We encourage you to share your model with the community. Log in to your Hugging Face account to upload it to the Hub.
When prompted, enter your token to log in:

from huggingface_hub import notebook_login
notebook_login()

Load the CPPE-5 dataset
The CPPE-5 dataset contains images with
annotations identifying medical personal protective equipment (PPE) in the context of the COVID-19 pandemic.
Start by loading the dataset:

from datasets import load_dataset
cppe5 = load_dataset(""cppe-5"")
cppe5
DatasetDict({
    train: Dataset({
        features: ['image_id', 'image', 'width', 'height', 'objects'],
        num_rows: 1000
    })
    test: Dataset({
        features: ['image_id', 'image', 'width', 'height', 'objects'],
        num_rows: 29
    })
})

You'll see that this dataset already comes with a training set containing 1000 images and a test set with 29 images.
To get familiar with the data, explore what the examples look like.

cppe5[""train""][0]
{'image_id': 15,
 'image': ,
 'width': 943,
 'height': 663,
 'objects': {'id': [114, 115, 116, 117],
  'area': [3796, 1596, 152768, 81002],
  'bbox': [[302.0, 109.0, 73.0, 52.0],
   [810.0, 100.0, 57.0, 28.0],
   [160.0, 31.0, 248.0, 616.0],
   [741.0, 68.0, 202.0, 401.0]],
  'category': [4, 4, 0, 0]}}

The examples in the dataset have the following fields:
- image_id: the example image id
- image: a PIL.Image.Image object containing the image
- width: width of the image
- height: height of the image
- objects: a dictionary containing bounding box metadata for the objects in the image:
  - id: the annotation id
  - area: the area of the bounding box
  - bbox: the object's bounding box (in the COCO format )
  - category: the object's category, with possible values including Coverall (0), Face_Shield (1), Gloves (2), Goggles (3) and Mask (4)
You may notice that the bbox field follows the COCO format, which is the format that the DETR model expects.
However, the grouping of the fields inside objects differs from the annotation format DETR requires. You will
need to apply some preprocessing transformations before using this data for training.
To get an even better understanding of the data, visualize an example in the dataset.

import numpy as np
import os
from PIL import Image, ImageDraw
image = cppe5[""train""][0][""image""]
annotations = cppe5[""train""][0][""objects""]
draw = ImageDraw.Draw(image)
categories = cppe5[""train""].features[""objects""].feature[""category""].names
id2label = {index: x for index, x in enumerate(categories, start=0)}
label2id = {v: k for k, v in id2label.items()}
for i in range(len(annotations[""id""])):
     box = annotations[""bbox""][i - 1]
     class_idx = annotations[""category""][i - 1]
     x, y, w, h = tuple(box)
     draw.rectangle((x, y, x + w, y + h), outline=""red"", width=1)
     draw.text((x, y), id2label[class_idx], fill=""white"")
image

To visualize the bounding boxes with associated labels, you can get the labels from the dataset's metadata, specifically
the category field.
You'll also want to create dictionaries that map a label id to a label class (id2label) and the other way around (label2id).
You can use them later when setting up the model. Including these maps will make your model reusable by others if you share
it on the Hugging Face Hub.
As a final step of getting familiar with the data, explore it for potential issues. One common problem with datasets for
object detection is bounding boxes that ""stretch"" beyond the edge of the image. Such ""runaway"" bounding boxes can raise
errors during training and should be addressed at this stage. There are a few examples with this issue in this dataset.
To keep things simple in this guide, we remove these images from the data.

remove_idx = [590, 821, 822, 875, 876, 878, 879]
keep = [i for i in range(len(cppe5[""train""])) if i not in remove_idx]
cppe5[""train""] = cppe5[""train""].select(keep)

Preprocess the data
To finetune a model, you must preprocess the data you plan to use to match precisely the approach used for the pre-trained model.
[AutoImageProcessor] takes care of processing image data to create pixel_values, pixel_mask, and
labels that a DETR model can train with. The image processor has some attributes that you won't have to worry about:

image_mean = [0.485, 0.456, 0.406 ]
image_std = [0.229, 0.224, 0.225]

These are the mean and standard deviation used to normalize images during the model pre-training. These values are crucial
to replicate when doing inference or finetuning a pre-trained image model.
Instantiate the image processor from the same checkpoint as the model you want to finetune.

from transformers import AutoImageProcessor
checkpoint = ""facebook/detr-resnet-50""
image_processor = AutoImageProcessor.from_pretrained(checkpoint)

Before passing the images to the image_processor, apply two preprocessing transformations to the dataset:
- Augmenting images
- Reformatting annotations to meet DETR expectations
First, to make sure the model does not overfit on the training data, you can apply image augmentation with any data augmentation library. Here we use Albumentations 
This library ensures that transformations affect the image and update the bounding boxes accordingly.
The 🤗 Datasets library documentation has a detailed guide on how to augment images for object detection,
and it uses the exact same dataset as an example. Apply the same approach here, resize each image to (480, 480),
flip it horizontally, and brighten it:

import albumentations
import numpy as np
import torch
transform = albumentations.Compose(
     [
         albumentations.Resize(480, 480),
         albumentations.HorizontalFlip(p=1.0),
         albumentations.RandomBrightnessContrast(p=1.0),
     ],
     bbox_params=albumentations.BboxParams(format=""coco"", label_fields=[""category""]),
 )

The image_processor expects the annotations to be in the following format: {'image_id': int, 'annotations': List[Dict]},
 where each dictionary is a COCO object annotation. Let's add a function to reformat annotations for a single example:

def formatted_anns(image_id, category, area, bbox):
     annotations = []
     for i in range(0, len(category)):
         new_ann = {
             ""image_id"": image_id,
             ""category_id"": category[i],
             ""isCrowd"": 0,
             ""area"": area[i],
             ""bbox"": list(bbox[i]),
         }
         annotations.append(new_ann)

     return annotations

Now you can combine the image and annotation transformations to use on a batch of examples:

transforming a batch
def transform_aug_ann(examples):
     image_ids = examples[""image_id""]
     images, bboxes, area, categories = [], [], [], []
     for image, objects in zip(examples[""image""], examples[""objects""]):
         image = np.array(image.convert(""RGB""))[:, :, ::-1]
         out = transform(image=image, bboxes=objects[""bbox""], category=objects[""category""])

         area.append(objects[""area""])
         images.append(out[""image""])
         bboxes.append(out[""bboxes""])
         categories.append(out[""category""])
     targets = [
         {""image_id"": id_, ""annotations"": formatted_anns(id_, cat_, ar_, box_)}
         for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes)
     ]
     return image_processor(images=images, annotations=targets, return_tensors=""pt"")

Apply this preprocessing function to the entire dataset using 🤗 Datasets [~datasets.Dataset.with_transform] method. This method applies
transformations on the fly when you load an element of the dataset.
At this point, you can check what an example from the dataset looks like after the transformations. You should see a tensor
with pixel_values, a tensor with pixel_mask, and labels.

cppe5[""train""] = cppe5[""train""].with_transform(transform_aug_ann)
cppe5[""train""][15]
{'pixel_values': tensor([[[ 0.9132,  0.9132,  0.9132,  , -1.9809, -1.9809, -1.9809],
          [ 0.9132,  0.9132,  0.9132,  , -1.9809, -1.9809, -1.9809],
          [ 0.9132,  0.9132,  0.9132,  , -1.9638, -1.9638, -1.9638],
          ,
          [-1.5699, -1.5699, -1.5699,  , -1.9980, -1.9980, -1.9980],
          [-1.5528, -1.5528, -1.5528,  , -1.9980, -1.9809, -1.9809],
          [-1.5528, -1.5528, -1.5528,  , -1.9980, -1.9809, -1.9809]],

     [[ 1.3081,  1.3081,  1.3081,  , -1.8431, -1.8431, -1.8431],
      [ 1.3081,  1.3081,  1.3081,  , -1.8431, -1.8431, -1.8431],
      [ 1.3081,  1.3081,  1.3081,  , -1.8256, -1.8256, -1.8256],
      ,
      [-1.3179, -1.3179, -1.3179,  , -1.8606, -1.8606, -1.8606],
      [-1.3004, -1.3004, -1.3004,  , -1.8606, -1.8431, -1.8431],
      [-1.3004, -1.3004, -1.3004,  , -1.8606, -1.8431, -1.8431]],

     [[ 1.4200,  1.4200,  1.4200,  , -1.6476, -1.6476, -1.6476],
      [ 1.4200,  1.4200,  1.4200,  , -1.6476, -1.6476, -1.6476],
      [ 1.4200,  1.4200,  1.4200,  , -1.6302, -1.6302, -1.6302],
      ,
      [-1.0201, -1.0201, -1.0201,  , -1.5604, -1.5604, -1.5604],
      [-1.0027, -1.0027, -1.0027,  , -1.5604, -1.5430, -1.5430],
      [-1.0027, -1.0027, -1.0027,  , -1.5604, -1.5430, -1.5430]]]),

'pixel_mask': tensor([[1, 1, 1,  , 1, 1, 1],
         [1, 1, 1,  , 1, 1, 1],
         [1, 1, 1,  , 1, 1, 1],
         ,
         [1, 1, 1,  , 1, 1, 1],
         [1, 1, 1,  , 1, 1, 1],
         [1, 1, 1,  , 1, 1, 1]]),
 'labels': {'size': tensor([800, 800]), 'image_id': tensor([756]), 'class_labels': tensor([4]), 'boxes': tensor([[0.7340, 0.6986, 0.3414, 0.5944]]), 'area': tensor([519544.4375]), 'iscrowd': tensor([0]), 'orig_size': tensor([480, 480])}}

You have successfully augmented the individual images and prepared their annotations. However, preprocessing isn't
complete yet. In the final step, create a custom collate_fn to batch images together.
Pad images (which are now pixel_values) to the largest image in a batch, and create a corresponding pixel_mask
to indicate which pixels are real (1) and which are padding (0).

def collate_fn(batch):
     pixel_values = [item[""pixel_values""] for item in batch]
     encoding = image_processor.pad(pixel_values, return_tensors=""pt"")
     labels = [item[""labels""] for item in batch]
     batch = {}
     batch[""pixel_values""] = encoding[""pixel_values""]
     batch[""pixel_mask""] = encoding[""pixel_mask""]
     batch[""labels""] = labels
     return batch

Training the DETR model
You have done most of the heavy lifting in the previous sections, so now you are ready to train your model!
The images in this dataset are still quite large, even after resizing. This means that finetuning this model will
require at least one GPU.
Training involves the following steps:
1. Load the model with [AutoModelForObjectDetection] using the same checkpoint as in the preprocessing.
2. Define your training hyperparameters in [TrainingArguments].
3. Pass the training arguments to [Trainer] along with the model, dataset, image processor, and data collator.
4. Call [~Trainer.train] to finetune your model.
When loading the model from the same checkpoint that you used for the preprocessing, remember to pass the label2id
and id2label maps that you created earlier from the dataset's metadata. Additionally, we specify ignore_mismatched_sizes=True to replace the existing classification head with a new one.

from transformers import AutoModelForObjectDetection
model = AutoModelForObjectDetection.from_pretrained(
     checkpoint,
     id2label=id2label,
     label2id=label2id,
     ignore_mismatched_sizes=True,
 )

In the [TrainingArguments] use output_dir to specify where to save your model, then configure hyperparameters as you see fit.
It is important you do not remove unused columns because this will drop the image column. Without the image column, you
can't create pixel_values. For this reason, set remove_unused_columns to False.
If you wish to share your model by pushing to the Hub, set push_to_hub to True (you must be signed in to Hugging
Face to upload your model).

from transformers import TrainingArguments
training_args = TrainingArguments(
     output_dir=""detr-resnet-50_finetuned_cppe5"",
     per_device_train_batch_size=8,
     num_train_epochs=10,
     fp16=True,
     save_steps=200,
     logging_steps=50,
     learning_rate=1e-5,
     weight_decay=1e-4,
     save_total_limit=2,
     remove_unused_columns=False,
     push_to_hub=True,
 )

Finally, bring everything together, and call [~transformers.Trainer.train]:

from transformers import Trainer
trainer = Trainer(
     model=model,
     args=training_args,
     data_collator=collate_fn,
     train_dataset=cppe5[""train""],
     tokenizer=image_processor,
 )
trainer.train()

If you have set push_to_hub to True in the training_args, the training checkpoints are pushed to the
Hugging Face Hub. Upon training completion, push the final model to the Hub as well by calling the [~transformers.Trainer.push_to_hub] method.

trainer.push_to_hub()

Evaluate
Object detection models are commonly evaluated with a set of COCO-style metrics.
You can use one of the existing metrics implementations, but here you'll use the one from torchvision to evaluate the final
model that you pushed to the Hub.
To use the torchvision evaluator, you'll need to prepare a ground truth COCO dataset. The API to build a COCO dataset
requires the data to be stored in a certain format, so you'll need to save images and annotations to disk first. Just like
when you prepared your data for training, the annotations from the cppe5[""test""] need to be formatted. However, images
should stay as they are.
The evaluation step requires a bit of work, but it can be split in three major steps.
First, prepare the cppe5[""test""] set: format the annotations and save the data to disk.

import json
format annotations the same as for training, no need for data augmentation
def val_formatted_anns(image_id, objects):
     annotations = []
     for i in range(0, len(objects[""id""])):
         new_ann = {
             ""id"": objects[""id""][i],
             ""category_id"": objects[""category""][i],
             ""iscrowd"": 0,
             ""image_id"": image_id,
             ""area"": objects[""area""][i],
             ""bbox"": objects[""bbox""][i],
         }
         annotations.append(new_ann)

     return annotations

Save images and annotations into the files torchvision.datasets.CocoDetection expects
def save_cppe5_annotation_file_images(cppe5):
     output_json = {}
     path_output_cppe5 = f""{os.getcwd()}/cppe5/""

     if not os.path.exists(path_output_cppe5):
         os.makedirs(path_output_cppe5)
     path_anno = os.path.join(path_output_cppe5, ""cppe5_ann.json"")
     categories_json = [{""supercategory"": ""none"", ""id"": id, ""name"": id2label[id]} for id in id2label]
     output_json[""images""] = []
     output_json[""annotations""] = []
     for example in cppe5:
         ann = val_formatted_anns(example[""image_id""], example[""objects""])
         output_json[""images""].append(
             {
                 ""id"": example[""image_id""],
                 ""width"": example[""image""].width,
                 ""height"": example[""image""].height,
                 ""file_name"": f""{example['image_id']}.png"",
             }
         )
         output_json[""annotations""].extend(ann)
     output_json[""categories""] = categories_json
     with open(path_anno, ""w"") as file:
         json.dump(output_json, file, ensure_ascii=False, indent=4)
     for im, img_id in zip(cppe5[""image""], cppe5[""image_id""]):
         path_img = os.path.join(path_output_cppe5, f""{img_id}.png"")
         im.save(path_img)
     return path_output_cppe5, path_anno

Next, prepare an instance of a CocoDetection class that can be used with cocoevaluator.

import torchvision
class CocoDetection(torchvision.datasets.CocoDetection):
     def init(self, img_folder, image_processor, ann_file):
         super().init(img_folder, ann_file)
         self.image_processor = image_processor

     def getitem(self, idx):
         # read in PIL image and target in COCO format
         img, target = super(CocoDetection, self).getitem(idx)
         # preprocess image and target: converting target to DETR format,
         # resizing + normalization of both image and target)
         image_id = self.ids[idx]
         target = {""image_id"": image_id, ""annotations"": target}
         encoding = self.image_processor(images=img, annotations=target, return_tensors=""pt"")
         pixel_values = encoding[""pixel_values""].squeeze()  # remove batch dimension
         target = encoding[""labels""][0]  # remove batch dimension
         return {""pixel_values"": pixel_values, ""labels"": target}

im_processor = AutoImageProcessor.from_pretrained(""devonho/detr-resnet-50_finetuned_cppe5"")
path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5[""test""])
test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno)

Finally, load the metrics and run the evaluation.

import evaluate
from tqdm import tqdm
model = AutoModelForObjectDetection.from_pretrained(""devonho/detr-resnet-50_finetuned_cppe5"")
module = evaluate.load(""ybelkada/cocoevaluate"", coco=test_ds_coco_format.coco)
val_dataloader = torch.utils.data.DataLoader(
     test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn
 )
with torch.no_grad():
     for idx, batch in enumerate(tqdm(val_dataloader)):
         pixel_values = batch[""pixel_values""]
         pixel_mask = batch[""pixel_mask""]

         labels = [
             {k: v for k, v in t.items()} for t in batch[""labels""]
         ]  # these are in DETR format, resized + normalized
         # forward pass
         outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
         orig_target_sizes = torch.stack([target[""orig_size""] for target in labels], dim=0)
         results = im_processor.post_process(outputs, orig_target_sizes)  # convert outputs of model to COCO api
         module.add(prediction=results, reference=labels)
         del batch

results = module.compute()
print(results)
Accumulating evaluation results
DONE (t=0.08s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.352
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.681
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.292
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.274
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.484
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.501
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590
``
These results can be further improved by adjusting the hyperparameters in [~transformers.TrainingArguments`]. Give it a go!

Inference
Now that you have finetuned a DETR model, evaluated it, and uploaded it to the Hugging Face Hub, you can use it for inference.
The simplest way to try out your finetuned model for inference is to use it in a [Pipeline]. Instantiate a pipeline
for object detection with your model, and pass an image to it:

from transformers import pipeline
import requests
url = ""https://i.imgur.com/2lnWoly.jpg""
image = Image.open(requests.get(url, stream=True).raw)
obj_detector = pipeline(""object-detection"", model=""devonho/detr-resnet-50_finetuned_cppe5"")
obj_detector(image)

You can also manually replicate the results of the pipeline if you'd like:

image_processor = AutoImageProcessor.from_pretrained(""devonho/detr-resnet-50_finetuned_cppe5"")
model = AutoModelForObjectDetection.from_pretrained(""devonho/detr-resnet-50_finetuned_cppe5"")
with torch.no_grad():
     inputs = image_processor(images=image, return_tensors=""pt"")
     outputs = model(**inputs)
     target_sizes = torch.tensor([image.size[::-1]])
     results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
for score, label, box in zip(results[""scores""], results[""labels""], results[""boxes""]):
     box = [round(i, 2) for i in box.tolist()]
     print(
         f""Detected {model.config.id2label[label.item()]} with confidence ""
         f""{round(score.item(), 3)} at location {box}""
     )
Detected Coverall with confidence 0.566 at location [1215.32, 147.38, 4401.81, 3227.08]
Detected Mask with confidence 0.584 at location [2449.06, 823.19, 3256.43, 1413.9]

Let's plot the result:

draw = ImageDraw.Draw(image)
for score, label, box in zip(results[""scores""], results[""labels""], results[""boxes""]):
     box = [round(i, 2) for i in box.tolist()]
     x, y, x2, y2 = tuple(box)
     draw.rectangle((x, y, x2, y2), outline=""red"", width=1)
     draw.text((x, y), model.config.id2label[label.item()], fill=""white"")
image

"
"

In this guide you'll learn how to:

create a depth estimation pipeline
run depth estimation inference by hand

Before you begin, make sure you have all the necessary libraries installed:

pip install -q transformers
Depth estimation pipeline
The simplest way to try out inference with a model supporting depth estimation is to use the corresponding [pipeline].
Instantiate a pipeline from a checkpoint on the Hugging Face Hub:

from transformers import pipeline
checkpoint = ""vinvino02/glpn-nyu""
depth_estimator = pipeline(""depth-estimation"", model=checkpoint)

Next, choose an image to analyze:

from PIL import Image
import requests
url = ""https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640""
image = Image.open(requests.get(url, stream=True).raw)
image

Pass the image to the pipeline.

predictions = depth_estimator(image)

The pipeline returns a dictionary with two entries. The first one, called predicted_depth, is a tensor with the values
being the depth expressed in meters for each pixel.
The second one, depth, is a PIL image that visualizes the depth estimation result.
Let's take a look at the visualized result:

predictions[""depth""]

Depth estimation inference by hand
Now that you've seen how to use the depth estimation pipeline, let's see how we can replicate the same result by hand.
Start by loading the model and associated processor from a checkpoint on the Hugging Face Hub.
Here we'll use the same checkpoint as before:

from transformers import AutoImageProcessor, AutoModelForDepthEstimation
checkpoint = ""vinvino02/glpn-nyu""
image_processor = AutoImageProcessor.from_pretrained(checkpoint)
model = AutoModelForDepthEstimation.from_pretrained(checkpoint)

Prepare the image input for the model using the image_processor that will take care of the necessary image transformations
such as resizing and normalization:

pixel_values = image_processor(image, return_tensors=""pt"").pixel_values

Pass the prepared inputs through the model:

import torch
with torch.no_grad():
     outputs = model(pixel_values)
     predicted_depth = outputs.predicted_depth

Visualize the results:

import numpy as np
interpolate to original size
prediction = torch.nn.functional.interpolate(
     predicted_depth.unsqueeze(1),
     size=image.size[::-1],
     mode=""bicubic"",
     align_corners=False,
 ).squeeze()
output = prediction.numpy()
formatted = (output * 255 / np.max(output)).astype(""uint8"")
depth = Image.fromarray(formatted)
depth

"
"
Troubleshoot
Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we've seen and how you can resolve them. However, this guide isn't meant to be a comprehensive collection of every 🤗 Transformers issue. For more help with troubleshooting your issue, try:

Asking for help on the forums. There are specific categories you can post your question to, like Beginners or 🤗 Transformers. Make sure you write a good descriptive forum post with some reproducible code to maximize the likelihood that your problem is solved!

Create an Issue on the 🤗 Transformers repository if it is a bug related to the library. Try to include as much information describing the bug as possible to help us better figure out what's wrong and how we can fix it.

Check the Migration guide if you use an older version of 🤗 Transformers since some important changes have been introduced between versions.

For more details about troubleshooting and getting help, take a look at Chapter 8 of the Hugging Face course.
Firewalled environments
Some GPU instances on cloud and intranet setups are firewalled to external connections, resulting in a connection error. When your script attempts to download model weights or datasets, the download will hang and then timeout with the following message:
ValueError: Connection error, and we cannot find the requested files in the cached path.
Please try again or make sure your Internet connection is on.
In this case, you should try to run 🤗 Transformers on offline mode to avoid the connection error.
CUDA out of memory
Training large models with millions of parameters can be challenging without the appropriate hardware. A common error you may encounter when the GPU runs out of memory is:
CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 11.17 GiB total capacity; 9.70 GiB already allocated; 179.81 MiB free; 9.85 GiB reserved in total by PyTorch)
Here are some potential solutions you can try to lessen memory use:

Reduce the per_device_train_batch_size value in [TrainingArguments].
Try using gradient_accumulation_steps in [TrainingArguments] to effectively increase overall batch size.

Refer to the Performance guide for more details about memory-saving techniques.

Unable to load a saved TensorFlow model
TensorFlow's model.save method will save the entire model - architecture, weights, training configuration - in a single file. However, when you load the model file again, you may run into an error because 🤗 Transformers may not load all the TensorFlow-related objects in the model file. To avoid issues with saving and loading TensorFlow models, we recommend you:

Save the model weights as a h5 file extension with model.save_weights and then reload the model with [~TFPreTrainedModel.from_pretrained]:

from transformers import TFPreTrainedModel
from tensorflow import keras
model.save_weights(""some_folder/tf_model.h5"")
model = TFPreTrainedModel.from_pretrained(""some_folder"")

Save the model with [~TFPretrainedModel.save_pretrained] and load it again with [~TFPreTrainedModel.from_pretrained]:

from transformers import TFPreTrainedModel
model.save_pretrained(""path_to/model"")
model = TFPreTrainedModel.from_pretrained(""path_to/model"")

ImportError
Another common error you may encounter, especially if it is a newly released model, is ImportError:
ImportError: cannot import name 'ImageGPTImageProcessor' from 'transformers' (unknown location)
For these error types, check to make sure you have the latest version of 🤗 Transformers installed to access the most recent models:

pip install transformers --upgrade
CUDA error: device-side assert triggered
Sometimes you may run into a generic CUDA error about an error in the device code.
RuntimeError: CUDA error: device-side assert triggered
You should try to run the code on a CPU first to get a more descriptive error message. Add the following environment variable to the beginning of your code to switch to a CPU:

import os
os.environ[""CUDA_VISIBLE_DEVICES""] = """"

Another option is to get a better traceback from the GPU. Add the following environment variable to the beginning of your code to get the traceback to point to the source of the error:

import os
os.environ[""CUDA_LAUNCH_BLOCKING""] = ""1""

Incorrect output when padding tokens aren't masked
In some cases, the output hidden_state may be incorrect if the input_ids include padding tokens. To demonstrate, load a model and tokenizer. You can access a model's pad_token_id to see its value. The pad_token_id may be None for some models, but you can always manually set it.

from transformers import AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained(""bert-base-uncased"")
model.config.pad_token_id
0

The following example shows the output without masking the padding tokens:

input_ids = torch.tensor([[7592, 2057, 2097, 2393, 9611, 2115], [7592, 0, 0, 0, 0, 0]])
output = model(input_ids)
print(output.logits)
tensor([[ 0.0082, -0.2307],
        [ 0.1317, -0.1683]], grad_fn=)

Here is the actual output of the second sequence:

input_ids = torch.tensor([[7592]])
output = model(input_ids)
print(output.logits)
tensor([[-0.1008, -0.4061]], grad_fn=)

Most of the time, you should provide an attention_mask to your model to ignore the padding tokens to avoid this silent error. Now the output of the second sequence matches its actual output:

By default, the tokenizer creates an attention_mask for you based on your specific tokenizer's defaults.

attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0]])
output = model(input_ids, attention_mask=attention_mask)
print(output.logits)
tensor([[ 0.0082, -0.2307],
        [-0.1008, -0.4061]], grad_fn=)

🤗 Transformers doesn't automatically create an attention_mask to mask a padding token if it is provided because:

Some models don't have a padding token.
For some use-cases, users want a model to attend to a padding token.

ValueError: Unrecognized configuration class XYZ for this kind of AutoModel
Generally, we recommend using the [AutoModel] class to load pretrained instances of models. This class
can automatically infer and load the correct architecture from a given checkpoint based on the configuration. If you see
this ValueError when loading a model from a checkpoint, this means the Auto class couldn't find a mapping from
the configuration in the given checkpoint to the kind of model you are trying to load. Most commonly, this happens when a
checkpoint doesn't support a given task.
For instance, you'll see this error in the following example because there is no GPT2 for question answering:

from transformers import AutoProcessor, AutoModelForQuestionAnswering
processor = AutoProcessor.from_pretrained(""gpt2-medium"")
model = AutoModelForQuestionAnswering.from_pretrained(""gpt2-medium"")
ValueError: Unrecognized configuration class  for this kind of AutoModel: AutoModelForQuestionAnswering.
Model type should be one of AlbertConfig, BartConfig, BertConfig, BigBirdConfig, BigBirdPegasusConfig, BloomConfig, 

"
""
"
Generation
Each framework has a generate method for text generation implemented in their respective GenerationMixin class:

PyTorch [~generation.GenerationMixin.generate] is implemented in [~generation.GenerationMixin].
TensorFlow [~generation.TFGenerationMixin.generate] is implemented in [~generation.TFGenerationMixin].
Flax/JAX [~generation.FlaxGenerationMixin.generate] is implemented in [~generation.FlaxGenerationMixin].

Regardless of your framework of choice, you can parameterize the generate method with a [~generation.GenerationConfig]
class instance. Please refer to this class for the complete list of generation parameters, which control the behavior
of the generation method.
To learn how to inspect a model's generation configuration, what are the defaults, how to change the parameters ad hoc,
and how to create and save a customized generation configuration, refer to the
text generation strategies guide. The guide also explains how to use related features,
like token streaming.
GenerationConfig
[[autodoc]] generation.GenerationConfig
    - from_pretrained
    - from_model_config
    - save_pretrained
GenerationMixin
[[autodoc]] generation.GenerationMixin
    - generate
    - compute_transition_scores
    - greedy_search
    - sample
    - beam_search
    - beam_sample
    - contrastive_search
    - group_beam_search
    - constrained_beam_search
TFGenerationMixin
[[autodoc]] generation.TFGenerationMixin
    - generate
    - compute_transition_scores
FlaxGenerationMixin
[[autodoc]] generation.FlaxGenerationMixin
    - generate"
"
Efficient Inference on a Single GPU
In addition to this guide, relevant information can be found as well in the guide for training on a single GPU and the guide for inference on CPUs.
Better Transformer: PyTorch-native transformer fastpath
PyTorch-native nn.MultiHeadAttention attention fastpath, called BetterTransformer, can be used with Transformers through the integration in the 🤗 Optimum library.
PyTorch's attention fastpath allows to speed up inference through kernel fusions and the use of nested tensors. Detailed benchmarks can be found in this blog post.
After installing the optimum package, to use Better Transformer during inference, the relevant internal modules are replaced by calling [~PreTrainedModel.to_bettertransformer]:
python
model = model.to_bettertransformer()
The method [~PreTrainedModel.reverse_bettertransformer] allows to go back to the original modeling, which should be used before saving the model in order to use the canonical transformers modeling:
python
model = model.reverse_bettertransformer()
model.save_pretrained(""saved_model"")
As of PyTorch 2.0, the attention fastpath is supported for both encoders and decoders. The list of supported architectures can be found here.
bitsandbytes integration for FP4 mixed-precision inference
You can install bitsandbytes and benefit from easy model compression on GPUs. Using FP4 quantization you can expect to reduce up to 8x the model size compared to its native full precision version. Check out below how to get started.

Note that this feature can also be used in a multi GPU setup.

Requirements

Latest bitsandbytes library
pip install bitsandbytes>=0.39.0

Install latest accelerate from source
pip install git+https://github.com/huggingface/accelerate.git

Install latest transformers from source
pip install git+https://github.com/huggingface/transformers.git

Running FP4 models - single GPU setup - Quickstart
You can quickly run a FP4 model on a single GPU by running the following code:

from transformers import AutoModelForCausalLM
model_name = ""bigscience/bloom-2b5""
model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map=""auto"", load_in_4bit=True)
``
Note thatdevice_mapis optional but settingdevice_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources.
Running FP4 models - multi GPU setup
The way to load your mixed 4-bit model in multiple GPUs is as follows (same command as single GPU setup):
py
model_name = ""bigscience/bloom-2b5""
model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map=""auto"", load_in_4bit=True)
But you can control the GPU RAM you want to allocate on each GPU using accelerate. Use the max_memory argument as follows:
py
max_memory_mapping = {0: ""600MB"", 1: ""1GB""}
model_name = ""bigscience/bloom-3b""
model_4bit = AutoModelForCausalLM.from_pretrained(
    model_name, device_map=""auto"", load_in_4bit=True, max_memory=max_memory_mapping
)
In this example, the first GPU will use 600MB of memory and the second 1GB.
Advanced usage
For more advanced usage of this method, please have a look at the quantization documentation page.
bitsandbytes integration for Int8 mixed-precision matrix decomposition

Note that this feature can also be used in a multi GPU setup.

From the paper LLM.int8() : 8-bit Matrix Multiplication for Transformers at Scale, we support Hugging Face integration for all models in the Hub with a few lines of code.
The method reduces nn.Linear size by 2 for float16 and bfloat16 weights and by 4 for float32 weights, with close to no impact to the quality by operating on the outliers in half-precision.

Int8 mixed-precision matrix decomposition works by separating a matrix multiplication into two streams: (1) a systematic feature outlier stream matrix multiplied in fp16 (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). With this method, int8 inference with no predictive degradation is possible for very large models.
For more details regarding the method, check out the paper or our blogpost about the integration.

Note, that you would require a GPU to run mixed-8bit models as the kernels have been compiled for GPUs only. Make sure that you have enough GPU memory to store the quarter (or half if your model weights are in half precision) of the model before using this feature.
Below are some notes to help you use this module, or follow the demos on Google colab.
Requirements

If you have bitsandbytes<0.37.0, make sure you run on NVIDIA GPUs that support 8-bit tensor cores (Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100). For bitsandbytes>=0.37.0, all GPUs should be supported.
Install the correct version of bitsandbytes by running:
pip install bitsandbytes>=0.31.5
Install accelerate
pip install accelerate>=0.12.0

Running mixed-Int8 models - single GPU setup
After installing the required libraries, the way to load your mixed 8-bit model is as follows:

from transformers import AutoModelForCausalLM
model_name = ""bigscience/bloom-2b5""
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map=""auto"", load_in_8bit=True)

For text generation, we recommend:

using the model's generate() method instead of the pipeline() function. Although inference is possible with the pipeline() function, it is not optimized for mixed-8bit models, and will be slower than using the generate() method. Moreover, some sampling strategies are like nucleaus sampling are not supported by the pipeline() function for mixed-8bit models.
placing all inputs on the same device as the model.

Here is a simple example:

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = ""bigscience/bloom-2b5""
tokenizer = AutoTokenizer.from_pretrained(model_name)
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map=""auto"", load_in_8bit=True)
prompt = ""Hello, my llama is cute""
inputs = tokenizer(prompt, return_tensors=""pt"").to(""cuda"")
generated_ids = model.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

Running mixed-int8 models - multi GPU setup
The way to load your mixed 8-bit model in multiple GPUs is as follows (same command as single GPU setup):
py
model_name = ""bigscience/bloom-2b5""
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map=""auto"", load_in_8bit=True)
But you can control the GPU RAM you want to allocate on each GPU using accelerate. Use the max_memory argument as follows:
py
max_memory_mapping = {0: ""1GB"", 1: ""2GB""}
model_name = ""bigscience/bloom-3b""
model_8bit = AutoModelForCausalLM.from_pretrained(
    model_name, device_map=""auto"", load_in_8bit=True, max_memory=max_memory_mapping
)
In this example, the first GPU will use 1GB of memory and the second 2GB.
Colab demos
With this method you can infer on models that were not possible to infer on a Google Colab before.
Check out the demo for running T5-11b (42GB in fp32)! Using 8-bit quantization on Google Colab:

Or this demo for BLOOM-3B:
"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install -q pytorchvideo transformers evaluate
You will use PyTorchVideo (dubbed pytorchvideo) to process and prepare the videos.
We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:

from huggingface_hub import notebook_login
notebook_login()

Load UCF101 dataset
Start by loading a subset of the UCF-101 dataset. This will give you a chance to experiment and make sure everything works before spending more time training on the full dataset.

from huggingface_hub import hf_hub_download
hf_dataset_identifier = ""sayakpaul/ucf101-subset""
filename = ""UCF101_subset.tar.gz""
file_path = hf_hub_download(repo_id=hf_dataset_identifier, filename=filename, repo_type=""dataset"")

After the subset has been downloaded, you need to extract the compressed archive:
 

import tarfile
with tarfile.open(file_path) as t:
      t.extractall(""."")

At a high level, the dataset is organized like so:

UCF101_subset/
    train/
        BandMarching/
            video_1.mp4
            video_2.mp4
            
        Archery
            video_1.mp4
            video_2.mp4
            
        
    val/
        BandMarching/
            video_1.mp4
            video_2.mp4
            
        Archery
            video_1.mp4
            video_2.mp4
            
        
    test/
        BandMarching/
            video_1.mp4
            video_2.mp4
            
        Archery
            video_1.mp4
            video_2.mp4
            
        
The (sorted) video paths appear like so:

'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c04.avi',
'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c06.avi',
'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c01.avi',
'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c02.avi',
'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c06.avi'

You will notice that there are video clips belonging to the same group / scene where group is denoted by g in the video file paths. v_ApplyEyeMakeup_g07_c04.avi and v_ApplyEyeMakeup_g07_c06.avi, for example.
For the validation and evaluation splits, you wouldn't want to have video clips from the same group / scene to prevent data leakage. The subset that you are using in this tutorial takes this information into account.
Next up, you will derive the set of labels present in the dataset. Also, create two dictionaries that'll be helpful when initializing the model:

label2id: maps the class names to integers.
id2label: maps the integers to class names. 

 

class_labels = sorted({str(path).split(""/"")[2] for path in all_video_file_paths})
label2id = {label: i for i, label in enumerate(class_labels)}
id2label = {i: label for label, i in label2id.items()}
print(f""Unique classes: {list(label2id.keys())}."")

Unique classes: ['ApplyEyeMakeup', 'ApplyLipstick', 'Archery', 'BabyCrawling', 'BalanceBeam', 'BandMarching', 'BaseballPitch', 'Basketball', 'BasketballDunk', 'BenchPress'].

There are 10 unique classes. For each class, there are 30 videos in the training set.
Load a model to fine-tune
Instantiate a video classification model from a pretrained checkpoint and its associated image processor. The model's encoder comes with pre-trained parameters, and the classification head is randomly initialized. The image processor will come in handy when writing the preprocessing pipeline for our dataset.
 

from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification
model_ckpt = ""MCG-NJU/videomae-base""
image_processor = VideoMAEImageProcessor.from_pretrained(model_ckpt)
model = VideoMAEForVideoClassification.from_pretrained(
     model_ckpt,
     label2id=label2id,
     id2label=id2label,
     ignore_mismatched_sizes=True,  # provide this in case you're planning to fine-tune an already fine-tuned checkpoint
 )

While the model is loading, you might notice the following warning:

Some weights of the model checkpoint at MCG-NJU/videomae-base were not used when initializing VideoMAEForVideoClassification: [, 'decoder.decoder_layers.1.attention.output.dense.bias', 'decoder.decoder_layers.2.attention.attention.key.weight']
- This IS expected if you are initializing VideoMAEForVideoClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing VideoMAEForVideoClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of VideoMAEForVideoClassification were not initialized from the model checkpoint at MCG-NJU/videomae-base and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
The warning is telling us we are throwing away some weights (e.g. the weights and bias of the classifier layer) and randomly initializing some others (the weights and bias of a new classifier layer). This is expected in this case, because we are adding a new head for which we don't have pretrained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do.
Note that this checkpoint leads to better performance on this task as the checkpoint was obtained fine-tuning on a similar downstream task having considerable domain overlap. You can check out this checkpoint which was obtained by fine-tuning MCG-NJU/videomae-base-finetuned-kinetics.  
Prepare the datasets for training
For preprocessing the videos, you will leverage the PyTorchVideo library. Start by importing the dependencies we need. 
 

import pytorchvideo.data
from pytorchvideo.transforms import (
     ApplyTransformToKey,
     Normalize,
     RandomShortSideScale,
     RemoveKey,
     ShortSideScale,
     UniformTemporalSubsample,
 )
from torchvision.transforms import (
     Compose,
     Lambda,
     RandomCrop,
     RandomHorizontalFlip,
     Resize,
 )

For the training dataset transformations, use a combination of uniform temporal subsampling, pixel normalization, random cropping, and random horizontal flipping. For the validation and evaluation dataset transformations, keep the same transformation chain except for random cropping and horizontal flipping. To learn more about the details of these transformations check out the official documentation of PyTorchVideo.  
Use the image_processor associated with the pre-trained model to obtain the following information:

Image mean and standard deviation with which the video frame pixels will be normalized.
Spatial resolution to which the video frames will be resized.

Start by defining some constants.

mean = image_processor.image_mean
std = image_processor.image_std
if ""shortest_edge"" in image_processor.size:
     height = width = image_processor.size[""shortest_edge""]
else:
     height = image_processor.size[""height""]
     width = image_processor.size[""width""]
resize_to = (height, width)
num_frames_to_sample = model.config.num_frames
sample_rate = 4
fps = 30
clip_duration = num_frames_to_sample * sample_rate / fps

Now, define the dataset-specific transformations and the datasets respectively. Starting with the training set: 
 

train_transform = Compose(
     [
         ApplyTransformToKey(
             key=""video"",
             transform=Compose(
                 [
                     UniformTemporalSubsample(num_frames_to_sample),
                     Lambda(lambda x: x / 255.0),
                     Normalize(mean, std),
                     RandomShortSideScale(min_size=256, max_size=320),
                     RandomCrop(resize_to),
                     RandomHorizontalFlip(p=0.5),
                 ]
             ),
         ),
     ]
 )
train_dataset = pytorchvideo.data.Ucf101(
     data_path=os.path.join(dataset_root_path, ""train""),
     clip_sampler=pytorchvideo.data.make_clip_sampler(""random"", clip_duration),
     decode_audio=False,
     transform=train_transform,
 )

The same sequence of workflow can be applied to the validation and evaluation sets: 
 

val_transform = Compose(
     [
         ApplyTransformToKey(
             key=""video"",
             transform=Compose(
                 [
                     UniformTemporalSubsample(num_frames_to_sample),
                     Lambda(lambda x: x / 255.0),
                     Normalize(mean, std),
                     Resize(resize_to),
                 ]
             ),
         ),
     ]
 )
val_dataset = pytorchvideo.data.Ucf101(
     data_path=os.path.join(dataset_root_path, ""val""),
     clip_sampler=pytorchvideo.data.make_clip_sampler(""uniform"", clip_duration),
     decode_audio=False,
     transform=val_transform,
 )
test_dataset = pytorchvideo.data.Ucf101(
     data_path=os.path.join(dataset_root_path, ""test""),
     clip_sampler=pytorchvideo.data.make_clip_sampler(""uniform"", clip_duration),
     decode_audio=False,
     transform=val_transform,
 )

Note: The above dataset pipelines are taken from the official PyTorchVideo example. We're using the pytorchvideo.data.Ucf101() function because it's tailored for the UCF-101 dataset. Under the hood, it returns a pytorchvideo.data.labeled_video_dataset.LabeledVideoDataset object. LabeledVideoDataset class is the base class for all things video in the PyTorchVideo dataset. So, if you want to use a custom dataset not supported off-the-shelf by PyTorchVideo, you can extend the LabeledVideoDataset class accordingly. Refer to the data API documentation to learn more. Also, if your dataset follows a similar structure (as shown above), then using the pytorchvideo.data.Ucf101() should work just fine. 
You can access the num_videos argument to know the number of videos in the dataset.

print(train_dataset.num_videos, val_dataset.num_videos, test_dataset.num_videos)

(300, 30, 75)

Visualize the preprocessed video for better debugging
 

import imageio
import numpy as np
from IPython.display import Image
def unnormalize_img(img):
     """"""Un-normalizes the image pixels.""""""
     img = (img * std) + mean
     img = (img * 255).astype(""uint8"")
     return img.clip(0, 255)
def create_gif(video_tensor, filename=""sample.gif""):
     """"""Prepares a GIF from a video tensor.
   
     The video tensor is expected to have the following shape:
     (num_frames, num_channels, height, width).
     """"""
     frames = []
     for video_frame in video_tensor:
         frame_unnormalized = unnormalize_img(video_frame.permute(1, 2, 0).numpy())
         frames.append(frame_unnormalized)
     kargs = {""duration"": 0.25}
     imageio.mimsave(filename, frames, ""GIF"", **kargs)
     return filename
def display_gif(video_tensor, gif_name=""sample.gif""):
     """"""Prepares and displays a GIF from a video tensor.""""""
     video_tensor = video_tensor.permute(1, 0, 2, 3)
     gif_filename = create_gif(video_tensor, gif_name)
     return Image(filename=gif_filename)
sample_video = next(iter(train_dataset))
video_tensor = sample_video[""video""]
display_gif(video_tensor)

Train the model
Leverage Trainer from  🤗 Transformers for training the model. To instantiate a Trainer, you need to define the training configuration and an evaluation metric. The most important is the TrainingArguments, which is a class that contains all the attributes to configure the training. It requires an output folder name, which will be used to save the checkpoints of the model. It also helps sync all the information in the model repository on 🤗 Hub.
Most of the training arguments are self-explanatory, but one that is quite important here is remove_unused_columns=False. This one will drop any features not used by the model's call function. By default it's True because usually it's ideal to drop unused feature columns, making it easier to unpack inputs into the model's call function. But, in this case, you need the unused features ('video' in particular) in order to create pixel_values (which is a mandatory key our model expects in its inputs).
 

from transformers import TrainingArguments, Trainer
model_name = model_ckpt.split(""/"")[-1]
new_model_name = f""{model_name}-finetuned-ucf101-subset""
num_epochs = 4
args = TrainingArguments(
     new_model_name,
     remove_unused_columns=False,
     evaluation_strategy=""epoch"",
     save_strategy=""epoch"",
     learning_rate=5e-5,
     per_device_train_batch_size=batch_size,
     per_device_eval_batch_size=batch_size,
     warmup_ratio=0.1,
     logging_steps=10,
     load_best_model_at_end=True,
     metric_for_best_model=""accuracy"",
     push_to_hub=True,
     max_steps=(train_dataset.num_videos // batch_size) * num_epochs,
 )

The dataset returned by pytorchvideo.data.Ucf101() doesn't implement the __len__ method. As such, we must define max_steps when instantiating TrainingArguments. 
Next, you need to define a function to compute the metrics from the predictions, which will use the metric you'll load now. The only preprocessing you have to do is to take the argmax of our predicted logits:

import evaluate
metric = evaluate.load(""accuracy"")
def compute_metrics(eval_pred):
    predictions = np.argmax(eval_pred.predictions, axis=1)
    return metric.compute(predictions=predictions, references=eval_pred.label_ids)

A note on evaluation:
In the VideoMAE paper, the authors use the following evaluation strategy. They evaluate the model on several clips from test videos and apply different crops to those clips and report the aggregate score. However, in the interest of simplicity and brevity, we don't consider that in this tutorial.
Also, define a collate_fn, which will be used to batch examples together. Each batch consists of 2 keys, namely pixel_values and labels.
 

def collate_fn(examples):
     # permute to (num_frames, num_channels, height, width)
     pixel_values = torch.stack(
         [example[""video""].permute(1, 0, 2, 3) for example in examples]
     )
     labels = torch.tensor([example[""label""] for example in examples])
     return {""pixel_values"": pixel_values, ""labels"": labels}

Then you just pass all of this along with the datasets to Trainer:
 

trainer = Trainer(
     model,
     args,
     train_dataset=train_dataset,
     eval_dataset=val_dataset,
     tokenizer=image_processor,
     compute_metrics=compute_metrics,
     data_collator=collate_fn,
 )

You might wonder why you passed along the image_processor as a tokenizer when you preprocessed the data already. This is only to make sure the image processor configuration file (stored as JSON) will also be uploaded to the repo on the Hub.
Now fine-tune our model by calling the train method:
 

train_results = trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

Inference
Great, now that you have fine-tuned a model, you can use it for inference!
Load a video for inference:
 

sample_test_video = next(iter(test_dataset))

The simplest way to try out your fine-tuned model for inference is to use it in a pipeline. Instantiate a pipeline for video classification with your model, and pass your video to it:

from transformers import pipeline
video_cls = pipeline(model=""my_awesome_video_cls_model"")
video_cls(""https://huggingface.co/datasets/sayakpaul/ucf101-subset/resolve/main/v_BasketballDunk_g14_c06.avi"")
[{'score': 0.9272987842559814, 'label': 'BasketballDunk'},
 {'score': 0.017777055501937866, 'label': 'BabyCrawling'},
 {'score': 0.01663011871278286, 'label': 'BalanceBeam'},
 {'score': 0.009560945443809032, 'label': 'BandMarching'},
 {'score': 0.0068979403004050255, 'label': 'BaseballPitch'}]

You can also manually replicate the results of the pipeline if you'd like.

def run_inference(model, video):
     # (num_frames, num_channels, height, width)
     perumuted_sample_test_video = video.permute(1, 0, 2, 3)
     inputs = {
         ""pixel_values"": perumuted_sample_test_video.unsqueeze(0),
         ""labels"": torch.tensor(
             [sample_test_video[""label""]]
         ),  # this can be skipped if you don't have labels available.
     }

     device = torch.device(""cuda"" if torch.cuda.is_available() else ""cpu"")
     inputs = {k: v.to(device) for k, v in inputs.items()}
     model = model.to(device)
     # forward pass
     with torch.no_grad():
         outputs = model(**inputs)
         logits = outputs.logits
     return logits

Now, pass your input to the model and return the logits:

logits = run_inference(trained_model, sample_test_video[""video""])

Decoding the logits, we get: 
 

predicted_class_idx = logits.argmax(-1).item()
print(""Predicted class:"", model.config.id2label[predicted_class_idx])

Predicted class: BasketballDunk
```"
"
Export to TFLite
TensorFlow Lite is a lightweight framework for deploying machine learning models 
on resource-constrained devices, such as mobile phones, embedded systems, and Internet of Things (IoT) devices. 
TFLite is designed to optimize and run models efficiently on these devices with limited computational power, memory, and 
power consumption.
A TensorFlow Lite model is represented in a special efficient portable format identified by the .tflite file extension. 
🤗 Optimum offers functionality to export 🤗 Transformers models to TFLite through the exporters.tflite module. 
For the list of supported model architectures, please refer to 🤗 Optimum documentation.
To export a model to TFLite, install the required dependencies:

pip install optimum[exporters-tf]
To check out all available arguments, refer to the 🤗 Optimum docs, 
or view help in command line:

optimum-cli export tflite --help
To export a model's checkpoint from the 🤗 Hub, for example, bert-base-uncased, run the following command:

optimum-cli export tflite --model bert-base-uncased --sequence_length 128 bert_tflite/
You should see the logs indicating progress and showing where the resulting model.tflite is saved, like this:

Validating TFLite model
    -[✓] TFLite model output names match reference model (logits)
    - Validating TFLite Model output ""logits"":
        -[✓] (1, 128, 30522) matches (1, 128, 30522)
        -[x] values not close enough, max diff: 5.817413330078125e-05 (atol: 1e-05)
The TensorFlow Lite export succeeded with the warning: The maximum absolute difference between the output of the reference model and the TFLite exported model is not within the set tolerance 1e-05:
- logits: max diff = 5.817413330078125e-05.
 The exported model was saved at: bert_tflite
The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you 
saved both the model's weights and tokenizer files in the same directory (local_path). When using CLI, pass the 
local_path to the model argument instead of the checkpoint name on 🤗 Hub. "
"
sections:
local: index
    title: 🤗 Transformers
local: quicktour
    title: Quick tour
local: installation
    title: Installation
  title: Get started
sections:
local: pipeline_tutorial
    title: Run inference with pipelines
local: autoclass_tutorial
    title: Write portable code with AutoClass
local: preprocessing
    title: Preprocess data
local: training
    title: Fine-tune a pretrained model
local: run_scripts
    title: Train with a script
local: accelerate
    title: Set up distributed training with 🤗 Accelerate
local: model_sharing
    title: Share your model
local: transformers_agents
    title: Agents
  title: Tutorials
sections:
sections:
local: tasks/sequence_classification
    title: Text classification
local: tasks/token_classification
    title: Token classification
local: tasks/question_answering
    title: Question answering
local: tasks/language_modeling
    title: Causal language modeling
local: tasks/masked_language_modeling
    title: Masked language modeling
local: tasks/translation
    title: Translation
local: tasks/summarization
    title: Summarization
local: tasks/multiple_choice
    title: Multiple choice
title: Natural Language Processing
isExpanded: false

sections:
local: tasks/audio_classification
    title: Audio classification
local: tasks/asr
    title: Automatic speech recognition
title: Audio
isExpanded: false

sections:
local: tasks/image_classification
    title: Image classification
local: tasks/semantic_segmentation
    title: Semantic segmentation
local: tasks/video_classification
    title: Video classification
local: tasks/object_detection
    title: Object detection
local: tasks/zero_shot_object_detection
    title: Zero-shot object detection
local: tasks/zero_shot_image_classification
    title: Zero-shot image classification
local: tasks/monocular_depth_estimation
    title: Depth estimation
title: Computer Vision
isExpanded: false

sections:
local: tasks/image_captioning
    title: Image captioning
local: tasks/document_question_answering
    title: Document Question Answering
local: tasks/text-to-speech
    title: Text to speech
title: Multimodal
isExpanded: false
  title: Task Guides

sections:
local: fast_tokenizers
  title: Use fast tokenizers from 🤗 Tokenizers
local: multilingual
  title: Run inference with multilingual models
local: generation_strategies
  title: Customize text generation strategy
local: create_a_model
  title: Use model-specific APIs
local: custom_models
  title: Share a custom model
local: sagemaker
  title: Run training on Amazon SageMaker
local: serialization
  title: Export to ONNX
local: tflite
  title: Export to TFLite
local: torchscript
  title: Export to TorchScript
local: benchmarks
  title: Benchmarks
local: notebooks
  title: Notebooks with examples
local: community
  title: Community resources
local: custom_tools
  title: Custom Tools and Prompts
local: troubleshooting
  title: Troubleshoot
  title: Developer guides

sections:
local: performance
  title: Overview
sections:
local: perf_train_gpu_one
  title: Methods and tools for efficient training on a single GPU
local: perf_train_gpu_many
  title: Multiple GPUs and parallelism
local: perf_train_cpu
  title: Efficient training on CPU
local: perf_train_cpu_many
  title: Distributed CPU training
local: perf_train_tpu
  title: Training on TPUs
local: perf_train_tpu_tf
  title: Training on TPU with TensorFlow
local: perf_train_special
  title: Training on Specialized Hardware
local: perf_hardware
  title: Custom hardware for training
local: hpo_train
  title: Hyperparameter Search using Trainer API
  title: Efficient training techniques

sections:
local: perf_infer_cpu
  title: Inference on CPU
local: perf_infer_gpu_one
  title: Inference on one GPU
local: perf_infer_gpu_many
  title: Inference on many GPUs
local: perf_infer_special
  title: Inference on Specialized Hardware
  title: Optimizing inference

local: big_models
  title: Instantiating a big model
local: debugging
  title: Troubleshooting
local: tf_xla
  title: XLA Integration for TensorFlow Models
  title: Performance and scalability

sections:

local: contributing
  title: How to contribute to transformers?
local: add_new_model
  title: How to add a model to 🤗 Transformers?
local: add_tensorflow_model
  title: How to convert a 🤗 Transformers model to TensorFlow?
local: add_new_pipeline
  title: How to add a pipeline to 🤗 Transformers?
local: testing
  title: Testing
local: pr_checks
  title: Checks on a Pull Request
  title: Contribute

sections:

local: philosophy
    title: Philosophy
local: glossary
    title: Glossary
local: task_summary
    title: What 🤗 Transformers can do
local: tasks_explained
    title: How 🤗 Transformers solve tasks
local: model_summary
    title: The Transformer model family
local: tokenizer_summary
    title: Summary of the tokenizers
local: attention
    title: Attention mechanisms
local: pad_truncation
    title: Padding and truncation
local: bertology
    title: BERTology
local: perplexity
    title: Perplexity of fixed-length models
local: pipeline_webserver
    title: Pipelines for webserver inference
local: model_memory_anatomy
    title: Model training anatomy
  title: Conceptual guides
sections:
sections:
local: main_classes/agent
  title: Agents and Tools
local: model_doc/auto
  title: Auto Classes
local: main_classes/callback
  title: Callbacks
local: main_classes/configuration
  title: Configuration
local: main_classes/data_collator
  title: Data Collator
local: main_classes/keras_callbacks
  title: Keras callbacks
local: main_classes/logging
  title: Logging
local: main_classes/model
  title: Models
local: main_classes/text_generation
  title: Text Generation
local: main_classes/onnx
  title: ONNX
local: main_classes/optimizer_schedules
  title: Optimization
local: main_classes/output
  title: Model outputs
local: main_classes/pipelines
  title: Pipelines
local: main_classes/processors
  title: Processors
local: main_classes/quantization
  title: Quantization
local: main_classes/tokenizer
  title: Tokenizer
local: main_classes/trainer
  title: Trainer
local: main_classes/deepspeed
  title: DeepSpeed Integration
local: main_classes/feature_extractor
  title: Feature Extractor
local: main_classes/image_processor
  title: Image Processor
title: Main Classes

sections:
isExpanded: false
  sections:
local: model_doc/albert
    title: ALBERT
local: model_doc/bart
    title: BART
local: model_doc/barthez
    title: BARThez
local: model_doc/bartpho
    title: BARTpho
local: model_doc/bert
    title: BERT
local: model_doc/bert-generation
    title: BertGeneration
local: model_doc/bert-japanese
    title: BertJapanese
local: model_doc/bertweet
    title: Bertweet
local: model_doc/big_bird
    title: BigBird
local: model_doc/bigbird_pegasus
    title: BigBirdPegasus
local: model_doc/biogpt
    title: BioGpt
local: model_doc/blenderbot
    title: Blenderbot
local: model_doc/blenderbot-small
    title: Blenderbot Small
local: model_doc/bloom
    title: BLOOM
local: model_doc/bort
    title: BORT
local: model_doc/byt5
    title: ByT5
local: model_doc/camembert
    title: CamemBERT
local: model_doc/canine
    title: CANINE
local: model_doc/codegen
    title: CodeGen
local: model_doc/convbert
    title: ConvBERT
local: model_doc/cpm
    title: CPM
local: model_doc/cpmant
    title: CPMANT
local: model_doc/ctrl
    title: CTRL
local: model_doc/deberta
    title: DeBERTa
local: model_doc/deberta-v2
    title: DeBERTa-v2
local: model_doc/dialogpt
    title: DialoGPT
local: model_doc/distilbert
    title: DistilBERT
local: model_doc/dpr
    title: DPR
local: model_doc/electra
    title: ELECTRA
local: model_doc/encoder-decoder
    title: Encoder Decoder Models
local: model_doc/ernie
    title: ERNIE
local: model_doc/ernie_m
    title: ErnieM
local: model_doc/esm
    title: ESM
local: model_doc/flan-t5
    title: FLAN-T5
local: model_doc/flan-ul2
    title: FLAN-UL2
local: model_doc/flaubert
    title: FlauBERT
local: model_doc/fnet
    title: FNet
local: model_doc/fsmt
    title: FSMT
local: model_doc/funnel
    title: Funnel Transformer
local: model_doc/openai-gpt
    title: GPT
local: model_doc/gpt_neo
    title: GPT Neo
local: model_doc/gpt_neox
    title: GPT NeoX
local: model_doc/gpt_neox_japanese
    title: GPT NeoX Japanese
local: model_doc/gptj
    title: GPT-J
local: model_doc/gpt2
    title: GPT2
local: model_doc/gpt_bigcode
    title: GPTBigCode
local: model_doc/gptsan-japanese
    title: GPTSAN Japanese
local: model_doc/gpt-sw3
    title: GPTSw3
local: model_doc/herbert
    title: HerBERT
local: model_doc/ibert
    title: I-BERT
local: model_doc/jukebox
    title: Jukebox
local: model_doc/led
    title: LED
local: model_doc/llama
    title: LLaMA
local: model_doc/llama2
    title: Llama2
local: model_doc/longformer
    title: Longformer
local: model_doc/longt5
    title: LongT5
local: model_doc/luke
    title: LUKE
local: model_doc/m2m_100
    title: M2M100
local: model_doc/marian
    title: MarianMT
local: model_doc/markuplm
    title: MarkupLM
local: model_doc/mbart
    title: MBart and MBart-50
local: model_doc/mega
    title: MEGA
local: model_doc/megatron-bert
    title: MegatronBERT
local: model_doc/megatron_gpt2
    title: MegatronGPT2
local: model_doc/mluke
    title: mLUKE
local: model_doc/mobilebert
    title: MobileBERT
local: model_doc/mpnet
    title: MPNet
local: model_doc/mra
    title: MRA
local: model_doc/mt5
    title: MT5
local: model_doc/mvp
    title: MVP
local: model_doc/nezha
    title: NEZHA
local: model_doc/nllb
    title: NLLB
local: model_doc/nllb-moe
    title: NLLB-MoE
local: model_doc/nystromformer
    title: Nyströmformer
local: model_doc/open-llama
    title: Open-Llama
local: model_doc/opt
    title: OPT
local: model_doc/pegasus
    title: Pegasus
local: model_doc/pegasus_x
    title: PEGASUS-X
local: model_doc/phobert
    title: PhoBERT
local: model_doc/plbart
    title: PLBart
local: model_doc/prophetnet
    title: ProphetNet
local: model_doc/qdqbert
    title: QDQBert
local: model_doc/rag
    title: RAG
local: model_doc/realm
    title: REALM
local: model_doc/reformer
    title: Reformer
local: model_doc/rembert
    title: RemBERT
local: model_doc/retribert
    title: RetriBERT
local: model_doc/roberta
    title: RoBERTa
local: model_doc/roberta-prelayernorm
    title: RoBERTa-PreLayerNorm
local: model_doc/roc_bert
    title: RoCBert
local: model_doc/roformer
    title: RoFormer
local: model_doc/rwkv
    title: RWKV
local: model_doc/splinter
    title: Splinter
local: model_doc/squeezebert
    title: SqueezeBERT
local: model_doc/switch_transformers
    title: SwitchTransformers
local: model_doc/t5
    title: T5
local: model_doc/t5v1.1
    title: T5v1.1
local: model_doc/tapex
    title: TAPEX
local: model_doc/transfo-xl
    title: Transformer XL
local: model_doc/ul2
    title: UL2
local: model_doc/umt5
    title: UMT5
local: model_doc/xmod
    title: X-MOD
local: model_doc/xglm
    title: XGLM
local: model_doc/xlm
    title: XLM
local: model_doc/xlm-prophetnet
    title: XLM-ProphetNet
local: model_doc/xlm-roberta
    title: XLM-RoBERTa
local: model_doc/xlm-roberta-xl
    title: XLM-RoBERTa-XL
local: model_doc/xlm-v
    title: XLM-V
local: model_doc/xlnet
    title: XLNet
local: model_doc/yoso
    title: YOSO
  title: Text models
isExpanded: false
  sections:
local: model_doc/beit
    title: BEiT
local: model_doc/bit
    title: BiT
local: model_doc/conditional_detr
    title: Conditional DETR
local: model_doc/convnext
    title: ConvNeXT
local: model_doc/convnextv2
    title: ConvNeXTV2
local: model_doc/cvt
    title: CvT
local: model_doc/deformable_detr
    title: Deformable DETR
local: model_doc/deit
    title: DeiT
local: model_doc/deta
    title: DETA
local: model_doc/detr
    title: DETR
local: model_doc/dinat
    title: DiNAT
local: model_doc/dinov2
    title: DINO V2
local: model_doc/dit
    title: DiT
local: model_doc/dpt
    title: DPT
local: model_doc/efficientformer
    title: EfficientFormer
local: model_doc/efficientnet
    title: EfficientNet
local: model_doc/focalnet
    title: FocalNet
local: model_doc/glpn
    title: GLPN
local: model_doc/imagegpt
    title: ImageGPT
local: model_doc/levit
    title: LeViT
local: model_doc/mask2former
    title: Mask2Former
local: model_doc/maskformer
    title: MaskFormer
local: model_doc/mobilenet_v1
    title: MobileNetV1
local: model_doc/mobilenet_v2
    title: MobileNetV2
local: model_doc/mobilevit
    title: MobileViT
local: model_doc/mobilevitv2
    title: MobileViTV2
local: model_doc/nat
    title: NAT
local: model_doc/poolformer
    title: PoolFormer
local: model_doc/pvt
    title: Pyramid Vision Transformer (PVT)
local: model_doc/regnet
    title: RegNet
local: model_doc/resnet
    title: ResNet
local: model_doc/segformer
    title: SegFormer
local: model_doc/swiftformer
    title: SwiftFormer
local: model_doc/swin
    title: Swin Transformer
local: model_doc/swinv2
    title: Swin Transformer V2
local: model_doc/swin2sr
    title: Swin2SR
local: model_doc/table-transformer
    title: Table Transformer
local: model_doc/timesformer
    title: TimeSformer
local: model_doc/upernet
    title: UperNet
local: model_doc/van
    title: VAN
local: model_doc/videomae
    title: VideoMAE
local: model_doc/vit
    title: Vision Transformer (ViT)
local: model_doc/vit_hybrid
    title: ViT Hybrid
local: model_doc/vit_mae
    title: ViTMAE
local: model_doc/vit_msn
    title: ViTMSN
local: model_doc/vivit
    title: ViViT
local: model_doc/yolos
    title: YOLOS
  title: Vision models
isExpanded: false
  sections:
local: model_doc/audio-spectrogram-transformer
    title: Audio Spectrogram Transformer
local: model_doc/bark
    title: Bark
local: model_doc/clap
    title: CLAP
local: model_doc/encodec
    title: EnCodec
local: model_doc/hubert
    title: Hubert
local: model_doc/mctct
    title: MCTCT
local: model_doc/mms
    title: MMS
local: model_doc/musicgen
    title: MusicGen
local: model_doc/sew
    title: SEW
local: model_doc/sew-d
    title: SEW-D
local: model_doc/speech_to_text
    title: Speech2Text
local: model_doc/speech_to_text_2
    title: Speech2Text2
local: model_doc/speecht5
    title: SpeechT5
local: model_doc/unispeech
    title: UniSpeech
local: model_doc/unispeech-sat
    title: UniSpeech-SAT
local: model_doc/wav2vec2
    title: Wav2Vec2
local: model_doc/wav2vec2-conformer
    title: Wav2Vec2-Conformer
local: model_doc/wav2vec2_phoneme
    title: Wav2Vec2Phoneme
local: model_doc/wavlm
    title: WavLM
local: model_doc/whisper
    title: Whisper
local: model_doc/xls_r
    title: XLS-R
local: model_doc/xlsr_wav2vec2
    title: XLSR-Wav2Vec2
  title: Audio models
isExpanded: false
  sections:
local: model_doc/align
    title: ALIGN
local: model_doc/altclip
    title: AltCLIP
local: model_doc/blip
    title: BLIP
local: model_doc/blip-2
    title: BLIP-2
local: model_doc/bridgetower
    title: BridgeTower
local: model_doc/chinese_clip
    title: Chinese-CLIP
local: model_doc/clip
    title: CLIP
local: model_doc/clipseg
    title: CLIPSeg
local: model_doc/data2vec
    title: Data2Vec
local: model_doc/deplot
    title: DePlot
local: model_doc/donut
    title: Donut
local: model_doc/flava
    title: FLAVA
local: model_doc/git
    title: GIT
local: model_doc/groupvit
    title: GroupViT
local: model_doc/instructblip
    title: InstructBLIP
local: model_doc/layoutlm
    title: LayoutLM
local: model_doc/layoutlmv2
    title: LayoutLMV2
local: model_doc/layoutlmv3
    title: LayoutLMV3
local: model_doc/layoutxlm
    title: LayoutXLM
local: model_doc/lilt
    title: LiLT
local: model_doc/lxmert
    title: LXMERT
local: model_doc/matcha
    title: MatCha
local: model_doc/mgp-str
    title: MGP-STR
local: model_doc/oneformer
    title: OneFormer
local: model_doc/owlvit
    title: OWL-ViT
local: model_doc/perceiver
    title: Perceiver
local: model_doc/pix2struct
    title: Pix2Struct
local: model_doc/sam
    title: Segment Anything
local: model_doc/speech-encoder-decoder
    title: Speech Encoder Decoder Models
local: model_doc/tapas
    title: TAPAS
local: model_doc/trocr
    title: TrOCR
local: model_doc/tvlt
    title: TVLT
local: model_doc/vilt
    title: ViLT
local: model_doc/vision-encoder-decoder
    title: Vision Encoder Decoder Models
local: model_doc/vision-text-dual-encoder
    title: Vision Text Dual Encoder
local: model_doc/visual_bert
    title: VisualBERT
local: model_doc/xclip
    title: X-CLIP
  title: Multimodal models
isExpanded: false
  sections:
local: model_doc/decision_transformer
    title: Decision Transformer
local: model_doc/trajectory_transformer
    title: Trajectory Transformer
  title: Reinforcement learning models
isExpanded: false
  sections:
local: model_doc/autoformer
    title: Autoformer
local: model_doc/informer
    title: Informer
local: model_doc/time_series_transformer
    title: Time Series Transformer
  title: Time series models
isExpanded: false
  sections:
local: model_doc/graphormer
    title: Graphormer
  title: Graph models
title: Models

sections:
local: internal/modeling_utils
  title: Custom Layers and Utilities
local: internal/pipelines_utils
  title: Utilities for pipelines
local: internal/tokenization_utils
  title: Utilities for Tokenizers
local: internal/trainer_utils
  title: Utilities for Trainer
local: internal/generation_utils
  title: Utilities for Generation
local: internal/image_processing_utils
  title: Utilities for Image Processors
local: internal/audio_utils
  title: Utilities for Audio processing
local: internal/file_utils
  title: General Utilities
local: internal/time_series_utils
  title: Utilities for Time Series
title: Internal Helpers
  title: API

"
"
Optimization
The .optimization module provides:

an optimizer with weight decay fixed that can be used to fine-tuned models, and
several schedules in the form of schedule objects that inherit from _LRSchedule:
a gradient accumulation class to accumulate the gradients of multiple batches

AdamW (PyTorch)
[[autodoc]] AdamW
AdaFactor (PyTorch)
[[autodoc]] Adafactor
AdamWeightDecay (TensorFlow)
[[autodoc]] AdamWeightDecay
[[autodoc]] create_optimizer
Schedules
Learning Rate Schedules (Pytorch)
[[autodoc]] SchedulerType
[[autodoc]] get_scheduler
[[autodoc]] get_constant_schedule
[[autodoc]] get_constant_schedule_with_warmup

[[autodoc]] get_cosine_schedule_with_warmup

[[autodoc]] get_cosine_with_hard_restarts_schedule_with_warmup

[[autodoc]] get_linear_schedule_with_warmup

[[autodoc]] get_polynomial_decay_schedule_with_warmup
[[autodoc]] get_inverse_sqrt_schedule
Warmup (TensorFlow)
[[autodoc]] WarmUp
Gradient Strategies
GradientAccumulator (TensorFlow)
[[autodoc]] GradientAccumulator"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate
We encourage you to log in to your Hugging Face account to upload and share your model with the community. When prompted, enter your token to log in:

from huggingface_hub import notebook_login
notebook_login()

Load Food-101 dataset
Start by loading a smaller subset of the Food-101 dataset from the 🤗 Datasets library. This will give you a chance to
experiment and make sure everything works before spending more time training on the full dataset.

from datasets import load_dataset
food = load_dataset(""food101"", split=""train[:5000]"")

Split the dataset's train split into a train and test set with the [~datasets.Dataset.train_test_split] method:

food = food.train_test_split(test_size=0.2)

Then take a look at an example:

food[""train""][0]
{'image': ,
 'label': 79}

Each example in the dataset has two fields:

image: a PIL image of the food item
label: the label class of the food item

To make it easier for the model to get the label name from the label id, create a dictionary that maps the label name
to an integer and vice versa:

labels = food[""train""].features[""label""].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
     label2id[label] = str(i)
     id2label[str(i)] = label

Now you can convert the label id to a label name:

id2label[str(79)]
'prime_rib'

Preprocess
The next step is to load a ViT image processor to process the image into a tensor:

from transformers import AutoImageProcessor
checkpoint = ""google/vit-base-patch16-224-in21k""
image_processor = AutoImageProcessor.from_pretrained(checkpoint)

Apply some image transformations to the images to make the model more robust against overfitting. Here you'll use torchvision's transforms module, but you can also use any image library you like.
Crop a random part of the image, resize it, and normalize it with the image mean and standard deviation:

from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
size = (
     image_processor.size[""shortest_edge""]
     if ""shortest_edge"" in image_processor.size
     else (image_processor.size[""height""], image_processor.size[""width""])
 )
_transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])

Then create a preprocessing function to apply the transforms and return the pixel_values - the inputs to the model - of the image:

def transforms(examples):
     examples[""pixel_values""] = [_transforms(img.convert(""RGB"")) for img in examples[""image""]]
     del examples[""image""]
     return examples

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.with_transform] method. The transforms are applied on the fly when you load an element of the dataset:

food = food.with_transform(transforms)

Now create a batch of examples using [DefaultDataCollator]. Unlike other data collators in 🤗 Transformers, the DefaultDataCollator does not apply additional preprocessing such as padding.

from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()

To avoid overfitting and to make the model more robust, add some data augmentation to the training part of the dataset.
Here we use Keras preprocessing layers to define the transformations for the training data (includes data augmentation),
and transformations for the validation data (only center cropping, resizing and normalizing). You can use tf.imageor
any other library you prefer.

from tensorflow import keras
from tensorflow.keras import layers
size = (image_processor.size[""height""], image_processor.size[""width""])
train_data_augmentation = keras.Sequential(
     [
         layers.RandomCrop(size[0], size[1]),
         layers.Rescaling(scale=1.0 / 127.5, offset=-1),
         layers.RandomFlip(""horizontal""),
         layers.RandomRotation(factor=0.02),
         layers.RandomZoom(height_factor=0.2, width_factor=0.2),
     ],
     name=""train_data_augmentation"",
 )
val_data_augmentation = keras.Sequential(
     [
         layers.CenterCrop(size[0], size[1]),
         layers.Rescaling(scale=1.0 / 127.5, offset=-1),
     ],
     name=""val_data_augmentation"",
 )

Next, create functions to apply appropriate transformations to a batch of images, instead of one image at a time.

import numpy as np
import tensorflow as tf
from PIL import Image
def convert_to_tf_tensor(image: Image):
     np_image = np.array(image)
     tf_image = tf.convert_to_tensor(np_image)
     # expand_dims() is used to add a batch dimension since
     # the TF augmentation layers operates on batched inputs.
     return tf.expand_dims(tf_image, 0)
def preprocess_train(example_batch):
     """"""Apply train_transforms across a batch.""""""
     images = [
         train_data_augmentation(convert_to_tf_tensor(image.convert(""RGB""))) for image in example_batch[""image""]
     ]
     example_batch[""pixel_values""] = [tf.transpose(tf.squeeze(image)) for image in images]
     return example_batch

 def preprocess_val(example_batch):
     """"""Apply val_transforms across a batch.""""""
     images = [
         val_data_augmentation(convert_to_tf_tensor(image.convert(""RGB""))) for image in example_batch[""image""]
     ]
     example_batch[""pixel_values""] = [tf.transpose(tf.squeeze(image)) for image in images]
     return example_batch

Use 🤗 Datasets [~datasets.Dataset.set_transform] to apply the transformations on the fly:
py
food[""train""].set_transform(preprocess_train)
food[""test""].set_transform(preprocess_val)
As a final preprocessing step, create a batch of examples using DefaultDataCollator. Unlike other data collators in 🤗 Transformers, the
DefaultDataCollator does not apply additional preprocessing, such as padding.

from transformers import DefaultDataCollator
data_collator = DefaultDataCollator(return_tensors=""tf"")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an
evaluation method with the 🤗 Evaluate library. For this task, load
the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
accuracy = evaluate.load(""accuracy"")

Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the accuracy:

import numpy as np
def compute_metrics(eval_pred):
     predictions, labels = eval_pred
     predictions = np.argmax(predictions, axis=1)
     return accuracy.compute(predictions=predictions, references=labels)

Your compute_metrics function is ready to go now, and you'll return to it when you set up your training.
Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load ViT with [AutoModelForImageClassification]. Specify the number of labels along with the number of expected labels, and the label mappings:

from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
model = AutoModelForImageClassification.from_pretrained(
     checkpoint,
     num_labels=len(labels),
     id2label=id2label,
     label2id=label2id,
 )

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. It is important you don't remove unused columns because that'll drop the image column. Without the image column, you can't create pixel_values. Set remove_unused_columns=False to prevent this behavior! The only other required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the accuracy and save the training checkpoint.
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_food_model"",
     remove_unused_columns=False,
     evaluation_strategy=""epoch"",
     save_strategy=""epoch"",
     learning_rate=5e-5,
     per_device_train_batch_size=16,
     gradient_accumulation_steps=4,
     per_device_eval_batch_size=16,
     num_train_epochs=3,
     warmup_ratio=0.1,
     logging_steps=10,
     load_best_model_at_end=True,
     metric_for_best_model=""accuracy"",
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     data_collator=data_collator,
     train_dataset=food[""train""],
     eval_dataset=food[""test""],
     tokenizer=image_processor,
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you are unfamiliar with fine-tuning a model with Keras, check out the basic tutorial first!

To fine-tune a model in TensorFlow, follow these steps:
1. Define the training hyperparameters, and set up an optimizer and a learning rate schedule.
2. Instantiate a pre-trained model.
3. Convert a 🤗 Dataset to a tf.data.Dataset.
4. Compile your model.
5. Add callbacks and use the fit() method to run the training.
6. Upload your model to 🤗 Hub to share with the community.
Start by defining the hyperparameters, optimizer and learning rate schedule:

from transformers import create_optimizer
batch_size = 16
num_epochs = 5
num_train_steps = len(food[""train""]) * num_epochs
learning_rate = 3e-5
weight_decay_rate = 0.01
optimizer, lr_schedule = create_optimizer(
     init_lr=learning_rate,
     num_train_steps=num_train_steps,
     weight_decay_rate=weight_decay_rate,
     num_warmup_steps=0,
 )

Then, load ViT with [TFAutoModelForImageClassification] along with the label mappings:

from transformers import TFAutoModelForImageClassification
model = TFAutoModelForImageClassification.from_pretrained(
     checkpoint,
     id2label=id2label,
     label2id=label2id,
 )

Convert your datasets to the tf.data.Dataset format using the [~datasets.Dataset.to_tf_dataset] and your data_collator:

converting our train dataset to tf.data.Dataset
tf_train_dataset = food[""train""].to_tf_dataset(
     columns=""pixel_values"", label_cols=""label"", shuffle=True, batch_size=batch_size, collate_fn=data_collator
 )
converting our test dataset to tf.data.Dataset
tf_eval_dataset = food[""test""].to_tf_dataset(
     columns=""pixel_values"", label_cols=""label"", shuffle=True, batch_size=batch_size, collate_fn=data_collator
 )

Configure the model for training with compile():

from tensorflow.keras.losses import SparseCategoricalCrossentropy
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss)

To compute the accuracy from the predictions and push your model to the 🤗 Hub, use Keras callbacks.
Pass your compute_metrics function to KerasMetricCallback,
and use the PushToHubCallback to upload the model:

from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback
metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)
push_to_hub_callback = PushToHubCallback(
     output_dir=""food_classifier"",
     tokenizer=image_processor,
     save_strategy=""no"",
 )
callbacks = [metric_callback, push_to_hub_callback]

Finally, you are ready to train your model! Call fit() with your training and validation datasets, the number of epochs,
and your callbacks to fine-tune the model:

model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)
Epoch 1/5
250/250 [==============================] - 313s 1s/step - loss: 2.5623 - val_loss: 1.4161 - accuracy: 0.9290
Epoch 2/5
250/250 [==============================] - 265s 1s/step - loss: 0.9181 - val_loss: 0.6808 - accuracy: 0.9690
Epoch 3/5
250/250 [==============================] - 252s 1s/step - loss: 0.3910 - val_loss: 0.4303 - accuracy: 0.9820
Epoch 4/5
250/250 [==============================] - 251s 1s/step - loss: 0.2028 - val_loss: 0.3191 - accuracy: 0.9900
Epoch 5/5
250/250 [==============================] - 238s 949ms/step - loss: 0.1232 - val_loss: 0.3259 - accuracy: 0.9890

Congratulations! You have fine-tuned your model and shared it on the 🤗 Hub. You can now use it for inference!

For a more in-depth example of how to finetune a model for image classification, take a look at the corresponding PyTorch notebook.

Inference
Great, now that you've fine-tuned a model, you can use it for inference!
Load an image you'd like to run inference on:

ds = load_dataset(""food101"", split=""validation[:10]"")
image = ds[""image""][0]

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for image classification with your model, and pass your image to it:

from transformers import pipeline
classifier = pipeline(""image-classification"", model=""my_awesome_food_model"")
classifier(image)
[{'score': 0.31856709718704224, 'label': 'beignets'},
 {'score': 0.015232225880026817, 'label': 'bruschetta'},
 {'score': 0.01519392803311348, 'label': 'chicken_wings'},
 {'score': 0.013022331520915031, 'label': 'pork_chop'},
 {'score': 0.012728818692266941, 'label': 'prime_rib'}]

You can also manually replicate the results of the pipeline if you'd like:

Load an image processor to preprocess the image and return the input as PyTorch tensors:

from transformers import AutoImageProcessor
import torch
image_processor = AutoImageProcessor.from_pretrained(""my_awesome_food_model"")
inputs = image_processor(image, return_tensors=""pt"")

Pass your inputs to the model and return the logits:

from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained(""my_awesome_food_model"")
with torch.no_grad():
     logits = model(**inputs).logits

Get the predicted label with the highest probability, and use the model's id2label mapping to convert it to a label:

predicted_label = logits.argmax(-1).item()
model.config.id2label[predicted_label]
'beignets'

Load an image processor to preprocess the image and return the input as TensorFlow tensors:

from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained(""MariaK/food_classifier"")
inputs = image_processor(image, return_tensors=""tf"")

Pass your inputs to the model and return the logits:

from transformers import TFAutoModelForImageClassification
model = TFAutoModelForImageClassification.from_pretrained(""MariaK/food_classifier"")
logits = model(**inputs).logits

Get the predicted label with the highest probability, and use the model's id2label mapping to convert it to a label:

predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
model.config.id2label[predicted_class_id]
'beignets'

"
"
Keras callbacks
When training a Transformers model with Keras, there are some library-specific callbacks available to automate common
tasks:
KerasMetricCallback
[[autodoc]] KerasMetricCallback
PushToHubCallback
[[autodoc]] PushToHubCallback"
"
XLA Integration for TensorFlow Models
[[open-in-colab]]
Accelerated Linear Algebra, dubbed XLA, is a compiler for accelerating the runtime of TensorFlow Models. From the official documentation:
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes.
Using XLA in TensorFlow is simple – it comes packaged inside the tensorflow library, and it can be triggered with the jit_compile argument in any graph-creating function such as tf.function. When using Keras methods like fit() and predict(), you can enable XLA simply by passing the jit_compile argument to model.compile(). However, XLA is not limited to these methods - it can also be used to accelerate any arbitrary tf.function.
Several TensorFlow methods in 🤗 Transformers have been rewritten to be XLA-compatible, including text generation for models such as GPT2, T5 and OPT, as well as speech processing for models such as Whisper.
While the exact amount of speed-up is very much model-dependent, for TensorFlow text generation models inside 🤗 Transformers, we noticed a speed-up of ~100x. This document will explain how you can use XLA for these models to get the maximum amount of performance. We’ll also provide links to additional resources if you’re interested to learn more about the benchmarks and our design philosophy behind the XLA integration.
Running TF functions with XLA
Let us consider the following model in TensorFlow:

import tensorflow as tf
model = tf.keras.Sequential(
    [tf.keras.layers.Dense(10, input_shape=(10,), activation=""relu""), tf.keras.layers.Dense(5, activation=""softmax"")]
)

The above model accepts inputs having a dimension of (10, ). We can use the model for running a forward pass like so:

Generate random inputs for the model.
batch_size = 16
input_vector_dim = 10
random_inputs = tf.random.normal((batch_size, input_vector_dim))
Run a forward pass.
_ = model(random_inputs)

In order to run the forward pass with an XLA-compiled function, we’d need to do:
py
xla_fn = tf.function(model, jit_compile=True)
_ = xla_fn(random_inputs)
The default call() function of the model is used for compiling the XLA graph. But if there’s any other model function you want to compile into XLA that’s also possible with:
py
my_xla_fn = tf.function(model.my_xla_fn, jit_compile=True)
Running a TF text generation model with XLA from 🤗 Transformers
To enable XLA-accelerated generation within 🤗 Transformers, you need to have a recent version of transformers installed. You can install it by running:

pip install transformers --upgrade
And then you can run the following code:

import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
Will error if the minimal version of Transformers is not installed.
from transformers.utils import check_min_version
check_min_version(""4.21.0"")
tokenizer = AutoTokenizer.from_pretrained(""gpt2"", padding_side=""left"", pad_token="""")
model = TFAutoModelForCausalLM.from_pretrained(""gpt2"")
input_string = [""TensorFlow is""]
One line to create an XLA generation function
xla_generate = tf.function(model.generate, jit_compile=True)
tokenized_input = tokenizer(input_string, return_tensors=""tf"")
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
print(f""Generated -- {decoded_text}"")
Generated -- TensorFlow is an open-source, open-source, distributed-source application # framework for the

As you can notice, enabling XLA on generate() is just a single line of code. The rest of the code remains unchanged. However, there are a couple of gotchas in the above code snippet that are specific to XLA. You need to be aware of those to realize the speed-ups that XLA can bring in. We discuss these in the following section. 
Gotchas to be aware of
When you are executing an XLA-enabled function (like xla_generate() above) for the first time, it will internally try to infer the computation graph, which is time-consuming.  This process is known as “tracing”. 
You might notice that the generation time is not fast. Successive calls of xla_generate() (or any other XLA-enabled function) won’t have to infer the computation graph, given the inputs to the function follow the same shape with which the computation graph was initially built. While this is not a problem for modalities with fixed input shapes (e.g., images), you must pay attention if you are working with variable input shape modalities (e.g., text).
To ensure xla_generate() always operates with the same input shapes, you can specify the padding arguments when calling the tokenizer. 

import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(""gpt2"", padding_side=""left"", pad_token="""")
model = TFAutoModelForCausalLM.from_pretrained(""gpt2"")
input_string = [""TensorFlow is""]
xla_generate = tf.function(model.generate, jit_compile=True)
Here, we call the tokenizer with padding options.
tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors=""tf"")
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
print(f""Generated -- {decoded_text}"")

This way, you can ensure that the inputs to xla_generate() will always receive inputs with the shape it was traced with and thus leading to speed-ups in the generation time. You can verify this with the code below:

import time
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(""gpt2"", padding_side=""left"", pad_token="""")
model = TFAutoModelForCausalLM.from_pretrained(""gpt2"")
xla_generate = tf.function(model.generate, jit_compile=True)
for input_string in [""TensorFlow is"", ""TensorFlow is a"", ""TFLite is a""]:
    tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors=""tf"")
    start = time.time_ns()
    generated_tokens = xla_generate(**tokenized_input, num_beams=2)
    end = time.time_ns()
    print(f""Execution time -- {(end - start) / 1e6:.1f} ms\n"")

On a Tesla T4 GPU, you can expect the outputs like so:
```bash
Execution time -- 30819.6 ms
Execution time -- 79.0 ms
Execution time -- 78.9 ms
``
The first call toxla_generate()` is time-consuming because of tracing, but the successive calls are orders of magnitude faster. Keep in mind that any change in the generation options at any point with trigger re-tracing and thus leading to slow-downs in the generation time. 
We didn’t cover all the text generation options 🤗 Transformers provides in this document. We encourage you to read the documentation for advanced use cases.
Additional Resources
Here, we leave you with some additional resources if you want to delve deeper into XLA in 🤗 Transformers and in general. 

This Colab Notebook provides an interactive demonstration if you want to fiddle with the XLA-compatible encoder-decoder (like T5) and decoder-only (like GPT2) text generation models. 
This blog post provides an overview of the comparison benchmarks for XLA-compatible models along with a friendly introduction to XLA in TensorFlow. 
This blog post discusses our design philosophy behind adding XLA support to the TensorFlow models in 🤗 Transformers. 
Recommended posts for learning more about XLA and TensorFlow graphs in general:
XLA: Optimizing Compiler for Machine Learning
Introduction to graphs and tf.function
Better performance with tf.function 

"
"
Data Collator
Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
the same type as the elements of train_dataset or eval_dataset.
To be able to build batches, data collators may apply some processing (like padding). Some of them (like
[DataCollatorForLanguageModeling]) also apply some random data augmentation (like random masking)
on the formed batch.
Examples of use can be found in the example scripts or example notebooks.
Default data collator
[[autodoc]] data.data_collator.default_data_collator
DefaultDataCollator
[[autodoc]] data.data_collator.DefaultDataCollator
DataCollatorWithPadding
[[autodoc]] data.data_collator.DataCollatorWithPadding
DataCollatorForTokenClassification
[[autodoc]] data.data_collator.DataCollatorForTokenClassification
DataCollatorForSeq2Seq
[[autodoc]] data.data_collator.DataCollatorForSeq2Seq
DataCollatorForLanguageModeling
[[autodoc]] data.data_collator.DataCollatorForLanguageModeling
    - numpy_mask_tokens
    - tf_mask_tokens
    - torch_mask_tokens
DataCollatorForWholeWordMask
[[autodoc]] data.data_collator.DataCollatorForWholeWordMask
    - numpy_mask_tokens
    - tf_mask_tokens
    - torch_mask_tokens
DataCollatorForPermutationLanguageModeling
[[autodoc]] data.data_collator.DataCollatorForPermutationLanguageModeling
    - numpy_mask_tokens
    - tf_mask_tokens
    - torch_mask_tokens"
"
Using pipelines for a webserver

Creating an inference engine is a complex topic, and the ""best"" solution 
will most likely depend on your problem space. Are you on CPU or GPU? Do
you want the lowest latency, the highest throughput, support for
many models, or just highly optimize 1 specific model?
There are many ways to tackle this topic, so what we are going to present is a good default
to get started which may not necessarily be the most optimal solution for you.

The key thing to understand is that we can use an iterator, just like you would on a
dataset, since a webserver is basically a system that waits for requests and
treats them as they come in.
Usually webservers are multiplexed (multithreaded, async, etc..) to handle various
requests concurrently. Pipelines on the other hand (and mostly the underlying models)
are not really great for parallelism; they take up a lot of RAM, so it's best to give them all the available resources when they are running or it's a compute-intensive job.
We are going to solve that by having the webserver handle the light load of receiving
and sending requests, and having a single thread handling the actual work.
This example is going to use starlette. The actual framework is not really
important, but you might have to tune or change the code if you are using another
one to achieve the same effect.
Create server.py:

from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.routing import Route
from transformers import pipeline
import asyncio
async def homepage(request):
    payload = await request.body()
    string = payload.decode(""utf-8"")
    response_q = asyncio.Queue()
    await request.app.model_queue.put((string, response_q))
    output = await response_q.get()
    return JSONResponse(output)
async def server_loop(q):
    pipe = pipeline(model=""bert-base-uncased"")
    while True:
        (string, response_q) = await q.get()
        out = pipe(string)
        await response_q.put(out)
app = Starlette(
    routes=[
        Route(""/"", homepage, methods=[""POST""]),
    ],
)
@app.on_event(""startup"")
async def startup_event():
    q = asyncio.Queue()
    app.model_queue = q
    asyncio.create_task(server_loop(q))

Now you can start it with:

uvicorn server:app
And you can query it:
```bash
curl -X POST -d ""test [MASK]"" http://localhost:8000/
[{""score"":0.7742936015129089,""token"":1012,""token_str"":""."",""sequence"":""test.""},]

And there you go, now you have a good idea of how to create a webserver!
What is really important is that we load the model only once, so there are no copies
of the model on the webserver. This way, no unnecessary RAM is being used.
Then the queuing mechanism allows you to do fancy stuff like maybe accumulating a few
items before inferring to use dynamic batching:
py
(string, rq) = await q.get()
strings = []
queues = []
while True:
    try:
        (string, rq) = await asyncio.wait_for(q.get(), timeout=0.001)  # 1ms
    except asyncio.exceptions.TimeoutError:
        break
    strings.append(string)
    queues.append(rq)
strings
outs = pipe(strings, batch_size=len(strings))
for rq, out in zip(queues, outs):
    await rq.put(out)

Do not activate this without checking it makes sense for your load!

The proposed code is optimized for readability, not for being the best code.
First of all, there's no batch size limit which is usually not a 
great idea. Next, the timeout is reset on every queue fetch, meaning you could
wait much more than 1ms before running the inference (delaying the first request 
by that much). 
It would be better to have a single 1ms deadline.
This will always wait for 1ms even if the queue is empty, which might not be the
best since you probably want to start doing inference if there's nothing in the queue.
But maybe it does make sense if batching is really crucial for your use case.
Again, there's really no one best solution.
Few things you might want to consider
Error checking
There's a lot that can go wrong in production: out of memory, out of space,
loading the model might fail, the query might be wrong, the query might be
correct but still fail to run because of a model misconfiguration, and so on.
Generally, it's good if the server outputs the errors to the user, so
adding a lot of try..except statements to show those errors is a good
idea. But keep in mind it may also be a security risk to reveal all those errors depending 
on your security context.
Circuit breaking
Webservers usually look better when they do circuit breaking. It means they 
return proper errors when they're overloaded instead of just waiting for the query indefinitely. Return a 503 error instead of waiting for a super long time or a 504 after a long time.
This is relatively easy to implement in the proposed code since there is a single queue.
Looking at the queue size is a basic way to start returning errors before your 
webserver fails under load.
Blocking the main thread
Currently PyTorch is not async aware, and computation will block the main
thread while running. That means it would be better if PyTorch was forced to run
on its own thread/process. This wasn't done here because the code is a lot more
complex (mostly because threads and async and queues don't play nice together).
But ultimately it does the same thing.
This would be important if the inference of single items were long (> 1s) because 
in this case, it means every query during inference would have to wait for 1s before
even receiving an error.
Dynamic batching
In general, batching is not necessarily an improvement over passing 1 item at 
a time (see batching details for more information). But it can be very effective
when used in the correct setting. In the API, there is no dynamic
batching by default (too much opportunity for a slowdown). But for BLOOM inference -
which is a very large model - dynamic batching is essential to provide a decent experience for everyone."
"
Glossary
This glossary defines general machine learning and 🤗 Transformers terms to help you better understand the
documentation.
A
attention mask
The attention mask is an optional argument used when batching sequences together.

This argument indicates to the model which tokens should be attended to, and which should not.
For example, consider these two sequences:
thon

from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained(""bert-base-cased"")
sequence_a = ""This is a short sequence.""
sequence_b = ""This is a rather long sequence. It is at least longer than the sequence A.""
encoded_sequence_a = tokenizer(sequence_a)[""input_ids""]
encoded_sequence_b = tokenizer(sequence_b)[""input_ids""]

The encoded versions have different lengths:
thon

len(encoded_sequence_a), len(encoded_sequence_b)
(8, 19)

Therefore, we can't put them together in the same tensor as-is. The first sequence needs to be padded up to the length
of the second one, or the second one needs to be truncated down to the length of the first one.
In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
it to pad like this:
thon

padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)

We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
thon

padded_sequences[""input_ids""]
[[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]

This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating the
position of the padded indices so that the model does not attend to them. For the [BertTokenizer], 1 indicates a
value that should be attended to, while 0 indicates a padded value. This attention mask is in the dictionary returned
by the tokenizer under the key ""attention_mask"":
thon

padded_sequences[""attention_mask""]
[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]

autoencoding models
See encoder models and masked language modeling
autoregressive models
See causal language modeling and decoder models
B
backbone
The backbone is the network (embeddings and layers) that outputs the raw hidden states or features. It is usually connected to a head which accepts the features as its input to make a prediction. For example, [ViTModel] is a backbone without a specific head on top. Other models can also use [VitModel] as a backbone such as DPT.
C
causal language modeling
A pretraining task where the model reads the texts in order and has to predict the next word. It's usually done by
reading the whole sentence but using a mask inside the model to hide the future tokens at a certain timestep.
channel
Color images are made up of some combination of values in three channels - red, green, and blue (RGB) - and grayscale images only have one channel. In 🤗 Transformers, the channel can be the first or last dimension of an image's tensor: [n_channels, height, width] or [height, width, n_channels].
connectionist temporal classification (CTC)
An algorithm which allows a model to learn without knowing exactly how the input and output are aligned; CTC calculates the distribution of all possible outputs for a given input and chooses the most likely output from it. CTC is commonly used in speech recognition tasks because speech doesn't always cleanly align with the transcript for a variety of reasons such as a speaker's different speech rates.
convolution
A type of layer in a neural network where the input matrix is multiplied element-wise by a smaller matrix (kernel or filter) and the values are summed up in a new matrix. This is known as a convolutional operation which is repeated over the entire input matrix. Each operation is applied to a different segment of the input matrix. Convolutional neural networks (CNNs) are commonly used in computer vision.
D
decoder input IDs
This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder. These
inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually built in a
way specific to each model.
Most encoder-decoder models (BART, T5) create their decoder_input_ids on their own from the labels. In such models,
passing the labels is the preferred way to handle training.
Please check each model's docs to see how they handle these input IDs for sequence to sequence training.
decoder models
Also referred to as autoregressive models, decoder models involve a pretraining task (called causal language modeling) where the model reads the texts in order and has to predict the next word. It's usually done by
reading the whole sentence with a mask to hide future tokens at a certain timestep.

deep learning (DL)
Machine learning algorithms which uses neural networks with several layers.
E
encoder models
Also known as autoencoding models, encoder models take an input (such as text or images) and transform them into a condensed numerical representation called an embedding. Oftentimes, encoder models are pretrained using techniques like masked language modeling, which masks parts of the input sequence and forces the model to create more meaningful representations.

F
feature extraction
The process of selecting and transforming raw data into a set of features that are more informative and useful for machine learning algorithms. Some examples of feature extraction include transforming raw text into word embeddings and extracting important features such as edges or shapes from image/video data.
feed forward chunking
In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g., for
bert-base-uncased).
For an input of size [batch_size, sequence_length], the memory required to store the intermediate feed forward
embeddings [batch_size, sequence_length, config.intermediate_size] can account for a large fraction of the memory
use. The authors of Reformer: The Efficient Transformer noticed that since the
computation is independent of the sequence_length dimension, it is mathematically equivalent to compute the output
embeddings of both feed forward layers [batch_size, config.hidden_size]_0, , [batch_size, config.hidden_size]_n
individually and concat them afterward to [batch_size, sequence_length, config.hidden_size] with n =
sequence_length, which trades increased computation time against reduced memory use, but yields a mathematically
equivalent result.
For models employing the function [apply_chunking_to_forward], the chunk_size defines the number of output
embeddings that are computed in parallel and thus defines the trade-off between memory and time complexity. If
chunk_size is set to 0, no feed forward chunking is done.
finetuned models
Finetuning is a form of transfer learning which involves taking a pretrained model, freezing its weights, and replacing the output layer with a newly added model head. The model head is trained on your target dataset.
See the Fine-tune a pretrained model tutorial for more details, and learn how to fine-tune models with 🤗 Transformers.
H
head
The model head refers to the last layer of a neural network that accepts the raw hidden states and projects them onto a different dimension. There is a different model head for each task. For example:

[GPT2ForSequenceClassification] is a sequence classification head - a linear layer - on top of the base [GPT2Model].
[ViTForImageClassification] is an image classification head - a linear layer on top of the final hidden state of the CLS token - on top of the base [ViTModel].
[Wav2Vec2ForCTC] ia a language modeling head with CTC on top of the base [Wav2Vec2Model].

I
image patch
Vision-based Transformers models split an image into smaller patches which are linearly embedded, and then passed as a sequence to the model. You can find the patch_size - or resolution - of the model in it's configuration.
inference
Inference is the process of evaluating a model on new data after training is complete. See the Pipeline for inference tutorial to learn how to perform inference with 🤗 Transformers.
input IDs
The input ids are often the only required parameters to be passed to the model as input. They are token indices,
numerical representations of tokens building the sequences that will be used as input by the model.

Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
tokenizer, which is a WordPiece tokenizer:
thon

from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained(""bert-base-cased"")
sequence = ""A Titan RTX has 24GB of VRAM""

The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
thon

tokenized_sequence = tokenizer.tokenize(sequence)

The tokens are either words or subwords. Here for instance, ""VRAM"" wasn't in the model vocabulary, so it's been split
in ""V"", ""RA"" and ""M"". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix
is added for ""RA"" and ""M"":
thon

print(tokenized_sequence)
['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']

These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding
the sentence to the tokenizer, which leverages the Rust implementation of 🤗
Tokenizers for peak performance.
thon

inputs = tokenizer(sequence)

The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
token indices are under the key input_ids:
thon

encoded_sequence = inputs[""input_ids""]
print(encoded_sequence)
[101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]

Note that the tokenizer automatically adds ""special tokens"" (if the associated model relies on them) which are special
IDs the model sometimes uses.
If we decode the previous sequence of ids,
thon

decoded_sequence = tokenizer.decode(encoded_sequence)

we will see
thon

print(decoded_sequence)
[CLS] A Titan RTX has 24GB of VRAM [SEP]

because this is the way a [BertModel] is going to expect its inputs.
L
labels
The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels
should be the expected prediction of the model: it will use the standard loss in order to compute the loss between its
predictions and the expected value (the label).
These labels are different according to the model head, for example:

For sequence classification models, ([BertForSequenceClassification]), the model expects a tensor of dimension
  (batch_size) with each value of the batch corresponding to the expected label of the entire sequence.
For token classification models, ([BertForTokenClassification]), the model expects a tensor of dimension
  (batch_size, seq_length) with each value corresponding to the expected label of each individual token.
For masked language modeling, ([BertForMaskedLM]), the model expects a tensor of dimension (batch_size,
  seq_length) with each value corresponding to the expected label of each individual token: the labels being the token
  ID for the masked token, and values to be ignored for the rest (usually -100).
For sequence to sequence tasks, ([BartForConditionalGeneration], [MBartForConditionalGeneration]), the model
  expects a tensor of dimension (batch_size, tgt_seq_length) with each value corresponding to the target sequences
  associated with each input sequence. During training, both BART and T5 will make the appropriate
  decoder_input_ids and decoder attention masks internally. They usually do not need to be supplied. This does not
  apply to models leveraging the Encoder-Decoder framework.
For image classification models, ([ViTForImageClassification]), the model expects a tensor of dimension
  (batch_size) with each value of the batch corresponding to the expected label of each individual image.
For semantic segmentation models, ([SegformerForSemanticSegmentation]), the model expects a tensor of dimension
  (batch_size, height, width) with each value of the batch corresponding to the expected label of each individual pixel.
For object detection models, ([DetrForObjectDetection]), the model expects a list of dictionaries with a
  class_labels and boxes key where each value of the batch corresponds to the expected label and number of bounding boxes of each individual image.
For automatic speech recognition models, ([Wav2Vec2ForCTC]), the model expects a tensor of dimension (batch_size,
  target_length) with each value corresponding to the expected label of each individual token.

Each model's labels may be different, so be sure to always check the documentation of each model for more information
about their specific labels!

The base models ([BertModel]) do not accept labels, as these are the base transformer models, simply outputting
features.
large language models (LLM)
A generic term that refers to transformer language models (GPT-3, BLOOM, OPT) that were trained on a large quantity of data. These models also tend to have a large number of learnable parameters (e.g. 175 billion for GPT-3).
M
masked language modeling (MLM)
A pretraining task where the model sees a corrupted version of the texts, usually done by
masking some tokens randomly, and has to predict the original text.
multimodal
A task that combines texts with another kind of inputs (for instance images).
N
Natural language generation (NLG)
All tasks related to generating text (for instance, Write With Transformers, translation).
Natural language processing (NLP)
A generic way to say ""deal with texts"".
Natural language understanding (NLU)
All tasks related to understanding what is in a text (for instance classifying the
whole text, individual words).
P
pipeline
A pipeline in 🤗 Transformers is an abstraction referring to a series of steps that are executed in a specific order to preprocess and transform data and return a prediction from a model. Some example stages found in a pipeline might be data preprocessing, feature extraction, and normalization.
For more details, see Pipelines for inference.
pixel values
A tensor of the numerical representations of an image that is passed to a model. The pixel values have a shape of [batch_size, num_channels, height, width], and are generated from an image processor.
pooling
An operation that reduces a matrix into a smaller matrix, either by taking the maximum or average of the pooled dimension(s). Pooling layers are commonly found between convolutional layers to downsample the feature representation.
position IDs
Contrary to RNNs that have the position of each token embedded within them, transformers are unaware of the position of
each token. Therefore, the position IDs (position_ids) are used by the model to identify each token's position in the
list of tokens.
They are an optional parameter. If no position_ids are passed to the model, the IDs are automatically created as
absolute positional embeddings.
Absolute positional embeddings are selected in the range [0, config.max_position_embeddings - 1]. Some models use
other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
preprocessing
The task of preparing raw data into a format that can be easily consumed by machine learning models. For example, text is typically preprocessed by tokenization. To gain a better idea of what preprocessing looks like for other input types, check out the Preprocess tutorial.
pretrained model
A model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods involve a
self-supervised objective, which can be reading the text and trying to predict the next word (see causal language
modeling) or masking some words and trying to predict them (see masked language
modeling). 
Speech and vision models have their own pretraining objectives. For example, Wav2Vec2 is a speech model pretrained on a contrastive task which requires the model to identify the ""true"" speech representation from a set of ""false"" speech representations. On the other hand, BEiT is a vision model pretrained on a masked image modeling task which masks some of the image patches and requires the model to predict the masked patches (similar to the masked language modeling objective).
R
recurrent neural network (RNN)
A type of model that uses a loop over a layer to process texts.
representation learning
A subfield of machine learning which focuses on learning meaningful representations of raw data. Some examples of representation learning techniques include word embeddings, autoencoders, and Generative Adversarial Networks (GANs).
S
sampling rate
A measurement in hertz of the number of samples (the audio signal) taken per second. The sampling rate is a result of discretizing a continuous signal such as speech.
self-attention
Each element of the input finds out which other elements of the input they should attend to.
self-supervised learning
A category of machine learning techniques in which a model creates its own learning objective from unlabeled data. It differs from unsupervised learning and supervised learning in that the learning process is supervised, but not explicitly from the user. 
One example of self-supervised learning is masked language modeling, where a model is passed sentences with a proportion of its tokens removed and learns to predict the missing tokens.
semi-supervised learning
A broad category of machine learning training techniques that leverages a small amount of labeled data with a larger quantity of unlabeled data to improve the accuracy of a model, unlike supervised learning and unsupervised learning.
An example of a semi-supervised learning approach is ""self-training"", in which a model is trained on labeled data, and then used to make predictions on the unlabeled data. The portion of the unlabeled data that the model predicts with the most confidence gets added to the labeled dataset and used to retrain the model.
sequence-to-sequence (seq2seq)
Models that generate a new sequence from an input, like translation models, or summarization models (such as
Bart or T5).
stride
In convolution or pooling, the stride refers to the distance the kernel is moved over a matrix. A stride of 1 means the kernel is moved one pixel over at a time, and a stride of 2 means the kernel is moved two pixels over at a time.
supervised learning
A form of model training that directly uses labeled data to correct and instruct model performance. Data is fed into the model being trained, and its predictions are compared to the known labels. The model updates its weights based on how incorrect its predictions were, and the process is repeated to optimize model performance.
T
token
A part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords) or a
punctuation symbol.
token Type IDs
Some models' purpose is to do classification on pairs of sentences or question answering.

These require two different sequences to be joined in a single ""input_ids"" entry, which usually is performed with the
help of special tokens, such as the classifier ([CLS]) and separator ([SEP]) tokens. For example, the BERT model
builds its two sequence input as such:
thon

[CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]

We can use our tokenizer to automatically generate such a sentence by passing the two sequences to tokenizer as two
arguments (and not a list, like before) like this:
thon

from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained(""bert-base-cased"")
sequence_a = ""HuggingFace is based in NYC""
sequence_b = ""Where is HuggingFace based?""
encoded_dict = tokenizer(sequence_a, sequence_b)
decoded = tokenizer.decode(encoded_dict[""input_ids""])

which will return:
thon

print(decoded)
[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]

This is enough for some models to understand where one sequence ends and where another begins. However, other models,
such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary mask identifying
the two types of sequence in the model.
The tokenizer returns this mask as the ""token_type_ids"" entry:
thon

encoded_dict[""token_type_ids""]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]

The first sequence, the ""context"" used for the question, has all its tokens represented by a 0, whereas the second
sequence, corresponding to the ""question"", has all its tokens represented by a 1.
Some models, like [XLNetModel] use an additional token represented by a 2.
transfer learning
A technique that involves taking a pretrained model and adapting it to a dataset specific to your task. Instead of training a model from scratch, you can leverage knowledge obtained from an existing model as a starting point. This speeds up the learning process and reduces the amount of training data needed.
transformer
Self-attention based deep learning model architecture.
U
unsupervised learning
A form of model training in which data provided to the model is not labeled. Unsupervised learning techniques leverage statistical information of the data distribution to find patterns useful for the task at hand."
"
Utilities for Trainer
This page lists all the utility functions used by [Trainer].
Most of those are only useful if you are studying the code of the Trainer in the library.
Utilities
[[autodoc]] EvalPrediction
[[autodoc]] IntervalStrategy
[[autodoc]] enable_full_determinism
[[autodoc]] set_seed
[[autodoc]] torch_distributed_zero_first
Callbacks internals
[[autodoc]] trainer_callback.CallbackHandler
Distributed Evaluation
[[autodoc]] trainer_pt_utils.DistributedTensorGatherer
Distributed Evaluation
[[autodoc]] HfArgumentParser
Debug Utilities
[[autodoc]] debug_utils.DebugUnderflowOverflow"
"
Efficient Training on Multiple GPUs
When training on a single GPU is too slow or the model weights don't fit in a single GPUs memory we use a multi-GPU setup. Switching from a single GPU to multiple requires some form of parallelism as the work needs to be distributed. There are several techniques to achieve parallism such as data, tensor, or pipeline parallism. However, there is no one solution to fit them all and which settings works best depends on the hardware you are running on. While the main concepts most likely will apply to any other framework, this article is focused on PyTorch-based implementations.

Note: Most of the strategies introduced in the single GPU section (such as mixed precision training or gradient accumulation) are generic and apply to training models in general so make sure to have a look at it before diving into the following sections such as multi-GPU or CPU training.

We will first discuss in depth various 1D parallelism techniques and their pros and cons and then look at how they can be combined into 2D and 3D parallelism to enable an even faster training and to support even bigger models. Various other powerful alternative approaches will be presented.
Concepts
The following is the brief description of the main concepts that will be described later in depth in this document.

DataParallel (DP) - the same setup is replicated multiple times, and each being fed a slice of the data. The processing is done in parallel and all setups are synchronized at the end of each training step.
TensorParallel (TP) - each tensor is split up into multiple chunks, so instead of having the whole tensor reside on a single gpu, each shard of the tensor resides on its designated gpu. During processing each shard gets processed separately and in parallel on different GPUs and the results are synced at the end of the step. This is what one may call horizontal parallelism, as the splitting happens on horizontal level.
PipelineParallel (PP) - the model is split up vertically (layer-level) across multiple GPUs, so that only one or several layers of the model are places on a single gpu. Each gpu processes in parallel different stages of the pipeline and working on a small chunk of the batch.
Zero Redundancy Optimizer (ZeRO) - Also performs sharding of the tensors somewhat similar to TP, except the whole tensor gets reconstructed in time for a forward or backward computation, therefore the model doesn't need to be modified. It also supports various offloading techniques to compensate for limited GPU memory.
Sharded DDP - is another name for the foundational ZeRO concept as used by various other implementations of ZeRO.

Before diving deeper into the specifics of each concept we first have a look at the rough decision process when training large models on a large infrastructure.
Scalability Strategy
⇨ Single Node / Multi-GPU
* Model fits onto a single GPU:
1. DDP - Distributed DP
2. ZeRO - may or may not be faster depending on the situation and configuration used

Model doesn't fit onto a single GPU:

PP
ZeRO
TP

With very fast intra-node connectivity of NVLINK or NVSwitch all three should be mostly on par, without these PP will be faster than TP or ZeRO. The degree of TP may also make a difference. Best to experiment to find the winner on your particular setup.
TP is almost always used within a single node. That is TP size <= gpus per node.

Largest Layer not fitting into a single GPU:

If not using ZeRO - must use TP, as PP alone won't be able to fit.
With ZeRO see the same entry for ""Single GPU"" above

⇨ Multi-Node / Multi-GPU

When you have fast inter-node connectivity:

ZeRO - as it requires close to no modifications to the model
PP+TP+DP - less communications, but requires massive changes to the model

when you have slow inter-node connectivity and still low on GPU memory:

DP+PP+TP+ZeRO-1

Data Parallelism
Most users with just 2 GPUs already enjoy the increased training speed up thanks to DataParallel (DP) and DistributedDataParallel (DDP) that are almost trivial to use. This is a built-in feature of Pytorch. Note that in general it is advised to use DDP as it is better maintained and works for all models while DP might fail for some models. PyTorch documentation itself recommends the use of DDP.
DP vs DDP
DistributedDataParallel (DDP) is typically faster than DataParallel (DP), but it is not always the case:
* while DP is python threads-based, DDP is multiprocess-based - and as such it has no python threads limitations, such as GIL
* on the other hand a slow inter-connectivity between the GPU cards could lead to an actual slower outcome with DDP
Here are the main differences in the inter-GPU communication overhead between the two modes:
DDP:

At the start time the main process replicates the model once from gpu 0 to the rest of gpus
Then for each batch:
each gpu consumes each own mini-batch of data directly
during backward, once the local gradients are ready, they are then averaged across all processes

DP:
For each batch:
   1. gpu 0 reads the batch of data and then sends a mini-batch to each gpu
   2. replicates the up-to-date model from gpu 0 to each gpu
   3. runs forward and sends output from each gpu to gpu 0, computes loss
   4. scatters loss from gpu 0 to all gpus, runs backward
   5. sends gradients from each gpu to gpu 0 and averages those
The only communication DDP performs per batch is sending gradients, whereas DP does 5 different data exchanges per batch.
DP copies data within the process via python threads, whereas DDP copies data via torch.distributed.
Under DP gpu 0 performs a lot more work than the rest of the gpus, thus resulting in under-utilization of gpus.
You can use DDP across multiple machines, but this is not the case with DP.
There are other differences between DP and DDP but they aren't relevant to this discussion.
If you want to go really deep into understanding these 2 modes, this article is highly recommended, as it has great diagrams, includes multiple benchmarks and profiler outputs on various hardware, explains all the nuances that you may need to know.
Let's look at an actual benchmark:
| Type   | NVlink | Time |
| :----- | -----  | ---: |
| 2:DP   | Y      | 110s |
| 2:DDP  | Y      | 101s |
| 2:DDP  | N      | 131s |
Analysis:
Here DP is ~10% slower than DDP w/ NVlink, but ~15% faster than DDP w/o NVlink
The real difference will depend on how much data each GPU needs to sync with the others - the more there is to sync, the more a slow link will slow down the total runtime.
Here is the full benchmark code and outputs:
NCCL_P2P_DISABLE=1 was used to disable the NVLink feature on the corresponding benchmark.

DP
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
python examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 110.5948, 'train_samples_per_second': 1.808, 'epoch': 0.69}
DDP w/ NVlink
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 101.9003, 'train_samples_per_second': 1.963, 'epoch': 0.69}
DDP w/o NVlink
rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \
python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 131.4367, 'train_samples_per_second': 1.522, 'epoch': 0.69}

Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m)
Software: pytorch-1.8-to-be + cuda-11.0 / transformers==4.3.0.dev0
ZeRO Data Parallelism
ZeRO-powered data parallelism (ZeRO-DP) is described on the following diagram from this blog post

It can be difficult to wrap one's head around it, but in reality the concept is quite simple. This is just the usual DataParallel (DP), except, instead of replicating the full model params, gradients and optimizer states, each GPU stores only a slice of it.  And then at run-time when the full layer params are needed just for the given layer, all GPUs synchronize to give each other parts that they miss - this is it.
Consider this simple model with 3 layers, where each layer has 3 params:
La | Lb | Lc
---|----|---
a0 | b0 | c0
a1 | b1 | c1
a2 | b2 | c2
Layer La has weights a0, a1 and a2.
If we have 3 GPUs, the Sharded DDP (= Zero-DP) splits the model onto 3 GPUs like so:

GPU0:
La | Lb | Lc
---|----|---
a0 | b0 | c0
GPU1:
La | Lb | Lc
---|----|---
a1 | b1 | c1
GPU2:
La | Lb | Lc
---|----|---
a2 | b2 | c2

In a way this is the same horizontal slicing, as tensor parallelism, if you imagine the typical DNN diagram. Vertical slicing is where one puts whole layer-groups on different GPUs. But it's just the starting point.
Now each of these GPUs will get the usual mini-batch as it works in DP:
x0 => GPU0
x1 => GPU1
x2 => GPU2
The inputs are unmodified - they think they are going to be processed by the normal model.
First, the inputs hit the layer La.
Let's focus just on GPU0: x0 needs a0, a1, a2 params to do its forward path, but GPU0 has only a0 - it gets sent a1 from GPU1 and a2 from GPU2, bringing all pieces of the model together.
In parallel, GPU1 gets mini-batch x1 and it only has a1, but needs a0 and a2 params, so it gets those from GPU0 and GPU2.
Same happens to GPU2 that gets input x2. It gets a0 and a1 from GPU0 and GPU1, and with its a2 it reconstructs the full tensor.
All 3 GPUs get the full tensors reconstructed and a forward happens.
As soon as the calculation is done, the data that is no longer needed gets dropped - it's only used during the calculation. The reconstruction is done efficiently via a pre-fetch.
And the whole process is repeated for layer Lb, then Lc forward-wise, and then backward Lc -> Lb -> La.
To me this sounds like an efficient group backpacking weight distribution strategy:

person A carries the tent
person B carries the stove
person C carries the axe

Now each night they all share what they have with others and get from others what they don't have, and in the morning they pack up their allocated type of gear and continue on their way. This is Sharded DDP / Zero DP.
Compare this strategy to the simple one where each person has to carry their own tent, stove and axe, which would be far more inefficient. This is DataParallel (DP and DDP) in Pytorch.
While reading the literature on this topic you may encounter the following synonyms: Sharded, Partitioned.
If you pay close attention the way ZeRO partitions the model's weights - it looks very similar to tensor parallelism which will be discussed later. This is because it partitions/shards each layer's weights, unlike vertical model parallelism which is discussed next.
Implementations:

DeepSpeed ZeRO-DP stages 1+2+3
transformers integration

Naive Model Parallelism (Vertical) and Pipeline Parallelism
Naive Model Parallelism (MP) is where one spreads groups of model layers across multiple GPUs. The mechanism is relatively simple - switch the desired layers .to() the desired devices and now whenever the data goes in and out those layers switch the data to the same device as the layer and leave the rest unmodified.
We refer to it as Vertical MP, because if you remember how most models are drawn, we slice the layers vertically. For example, if the following diagram shows an 8-layer model:
===================  ===================
|  0 | 1 | 2 | 3  |  |  4 | 5 | 6 | 7  |
===================  ===================
        gpu0                 gpu1
we just sliced it in 2 vertically, placing layers 0-3 onto GPU0 and 4-7 to GPU1.
Now while data travels from layer 0 to 1, 1 to 2 and 2 to 3 this is just the normal model. But when data needs to pass from layer 3 to layer 4 it needs to travel from GPU0 to GPU1 which introduces a communication overhead. If the participating GPUs are on the same compute node (e.g. same physical machine) this copying is pretty fast, but if the GPUs are located on different compute nodes (e.g. multiple machines) the communication overhead could be significantly larger.
Then layers 4 to 5 to 6 to 7 are as a normal model would have and when the 7th layer completes we often need to send the data back to layer 0 where the labels are (or alternatively send the labels to the last layer). Now the loss can be computed and the optimizer can do its work.
Problems:
- the main deficiency and why this one is called ""naive"" MP, is that all but one GPU is idle at any given moment. So if 4 GPUs are used, it's almost identical to quadrupling the amount of memory of a single GPU, and ignoring the rest of the hardware. Plus there is the overhead of copying the data between devices. So 4x 6GB cards will be able to accommodate the same size as 1x 24GB card using naive MP, except the latter will complete the training faster, since it doesn't have the data copying overhead. But, say, if you have 40GB cards and need to fit a 45GB model you can with 4x 40GB cards (but barely because of the gradient and optimizer states)
- shared embeddings may need to get copied back and forth between GPUs.
Pipeline Parallelism (PP) is almost identical to a naive MP, but it solves the GPU idling problem, by chunking the incoming batch into micro-batches and artificially creating a pipeline, which allows different GPUs to concurrently participate in the computation process.
The following illustration from the GPipe paper shows the naive MP on the top, and PP on the bottom:

It's easy to see from the bottom diagram how PP has less dead zones, where GPUs are idle. The idle parts are referred to as the ""bubble"".
Both parts of the diagram show a parallelism that is of degree 4. That is 4 GPUs are participating in the pipeline. So there is the forward path of 4 pipe stages F0, F1, F2 and F3 and then the return reverse order backward path of B3, B2, B1 and B0.
PP introduces a new hyper-parameter to tune and it's chunks which defines how many chunks of data are sent in a sequence through the same pipe stage. For example, in the bottom diagram you can see that chunks=4. GPU0 performs the same forward path on chunk 0, 1, 2 and 3 (F0,0, F0,1, F0,2, F0,3) and then it waits for other GPUs to do their work and only when their work is starting to be complete, GPU0 starts to work again doing the backward path for chunks 3, 2, 1 and 0 (B0,3, B0,2, B0,1, B0,0).
Note that conceptually this is the same concept as gradient accumulation steps (GAS). Pytorch uses chunks, whereas DeepSpeed refers to the same hyper-parameter as GAS.
Because of the chunks, PP introduces the concept of micro-batches (MBS). DP splits the global data batch size into mini-batches, so if you have a DP degree of 4, a global batch size of 1024 gets split up into 4 mini-batches of 256 each (1024/4). And if the number of chunks (or GAS) is 32 we end up with a micro-batch size of 8 (256/32). Each Pipeline stage works with a single micro-batch at a time.
To calculate the global batch size of the DP + PP setup we then do: mbs*chunks*dp_degree (8*32*4=1024).
Let's go back to the diagram.
With chunks=1 you end up with the naive MP, which is very inefficient. With a very large chunks value you end up with tiny micro-batch sizes which could be not every efficient either. So one has to experiment to find the value that leads to the highest efficient utilization of the gpus.
While the diagram shows that there is a bubble of ""dead"" time that can't be parallelized because the last forward stage has to wait for backward to complete the pipeline, the purpose of finding the best value for chunks is to enable a high concurrent GPU utilization across all participating GPUs which translates to minimizing the size of the bubble.
There are 2 groups of solutions - the traditional Pipeline API and the more modern solutions that make things much easier for the end user.
Traditional Pipeline API solutions:
- PyTorch
- DeepSpeed
- Megatron-LM
Modern solutions:
- Varuna
- Sagemaker
Problems with traditional Pipeline API solutions:
- have to modify the model quite heavily, because Pipeline requires one to rewrite the normal flow of modules into a nn.Sequential sequence of the same, which may require changes to the design of the model.
- currently the Pipeline API is very restricted. If you had a bunch of python variables being passed in the very first stage of the Pipeline, you will have to find a way around it. Currently, the pipeline interface requires either a single Tensor or a tuple of Tensors as the only input and output. These tensors must have a batch size as the very first dimension, since pipeline is going to chunk the mini batch into micro-batches. Possible improvements are being discussed here https://github.com/pytorch/pytorch/pull/50693
- conditional control flow at the level of pipe stages is not possible - e.g., Encoder-Decoder models like T5 require special workarounds to handle a conditional encoder stage.
- have to arrange each layer so that the output of one model becomes an input to the other model.
We are yet to experiment with Varuna and SageMaker but their papers report that they have overcome the list of problems mentioned above and that they require much smaller changes to the user's model.
Implementations:
- Pytorch (initial support in pytorch-1.8, and progressively getting improved in 1.9 and more so in 1.10). Some examples
- DeepSpeed
- Megatron-LM has an internal implementation - no API.
- Varuna
- SageMaker - this is a proprietary solution that can only be used on AWS.
- OSLO - this is implemented based on the Hugging Face Transformers.
🤗 Transformers status: as of this writing none of the models supports full-PP. GPT2 and T5 models have naive MP support. The main obstacle is being unable to convert the models to nn.Sequential and have all the inputs to be Tensors. This is because currently the models include many features that make the conversion very complicated, and will need to be removed to accomplish that.
Other approaches:
DeepSpeed, Varuna and SageMaker use the concept of an Interleaved Pipeline

Here the bubble (idle time) is further minimized by prioritizing backward passes.
Varuna further tries to improve the schedule by using simulations to discover the most efficient scheduling.
OSLO has pipeline parallelism implementation based on the Transformers without nn.Sequential converting.
Tensor Parallelism
In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing.
In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large-Scale Language Model Training on GPU Clusters.
The main building block of any transformer is a fully connected nn.Linear followed by a nonlinear activation GeLU.
Following the Megatron's paper notation, we can write the dot-product part of it as Y = GeLU(XA), where X and Y are the input and output vectors, and A is the weight matrix.
If we look at the computation in matrix form, it's easy to see how the matrix multiplication can be split between multiple GPUs:

If we split the weight matrix A column-wise across N GPUs and perform matrix multiplications XA_1 through XA_n in parallel, then we will end up with N output vectors Y_1, Y_2, , Y_n which can be fed into GeLU independently:

Using this principle, we can update an MLP of arbitrary depth, without the need for any synchronization between GPUs until the very end, where we need to reconstruct the output vector from shards. The Megatron-LM paper authors provide a helpful illustration for that:

Parallelizing the multi-headed attention layers is even simpler, since they are already inherently parallel, due to having multiple independent heads!

Special considerations: TP requires very fast network, and therefore it's not advisable to do TP across more than one node. Practically, if a node has 4 GPUs, the highest TP degree is therefore 4. If you need a TP degree of 8, you need to use nodes that have at least 8 GPUs.
This section is based on the original much more detailed TP overview.
by @anton-l.
SageMaker combines TP with DP for a more efficient processing.
Alternative names:
- DeepSpeed calls it tensor slicing
Implementations:
- Megatron-LM has an internal implementation, as it's very model-specific
- parallelformers (only inference at the moment)
- SageMaker - this is a proprietary solution that can only be used on AWS.
- OSLO has the tensor parallelism implementation based on the Transformers.
🤗 Transformers status:
- core: not yet implemented in the core
- but if you want inference parallelformers provides this support for most of our models. So until this is implemented in the core you can use theirs. And hopefully training mode will be supported too.
- Deepspeed-Inference also supports our BERT, GPT-2, and GPT-Neo models in their super-fast CUDA-kernel-based inference mode, see more here
DP+PP
The following diagram from the DeepSpeed pipeline tutorial demonstrates how one combines DP with PP.

Here it's important to see how DP rank 0 doesn't see GPU2 and DP rank 1 doesn't see GPU3. To DP there is just GPUs 0 and 1 where it feeds data as if there were just 2 GPUs. GPU0 ""secretly"" offloads some of its load to GPU2 using PP. And GPU1 does the same by enlisting GPU3 to its aid.
Since each dimension requires at least 2 GPUs, here you'd need at least 4 GPUs.
Implementations:
- DeepSpeed
- Megatron-LM
- Varuna
- SageMaker
- OSLO
🤗 Transformers status: not yet implemented
DP+PP+TP
To get an even more efficient training a 3D parallelism is used where PP is combined with TP and DP. This can be seen in the following diagram.

This diagram is from a blog post 3D parallelism: Scaling to trillion-parameter models, which is a good read as well.
Since each dimension requires at least 2 GPUs, here you'd need at least 8 GPUs.
Implementations:
- DeepSpeed - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP.
- Megatron-LM
- Varuna
- SageMaker
- OSLO
🤗 Transformers status: not yet implemented, since we have no PP and TP.
ZeRO DP+PP+TP
One of the main features of DeepSpeed is ZeRO, which is a super-scalable extension of DP. It has already been discussed in ZeRO Data Parallelism. Normally it's a standalone feature that doesn't require PP or TP. But it can be combined with PP and TP.
When ZeRO-DP is combined with PP (and optionally TP) it typically enables only ZeRO stage 1 (optimizer sharding).
While it's theoretically possible to use ZeRO stage 2 (gradient sharding) with Pipeline Parallelism, it will have bad performance impacts. There would need to be an additional reduce-scatter collective for every micro-batch to aggregate the gradients before sharding, which adds a potentially significant communication overhead. By nature of Pipeline Parallelism, small micro-batches are used and instead the focus is on trying to balance arithmetic intensity (micro-batch size) with minimizing the Pipeline bubble (number of micro-batches). Therefore those communication costs are going to hurt.
In addition, There are already fewer layers than normal due to PP and so the memory savings won't be huge. PP already reduces gradient size by 1/PP, and so gradient sharding savings on top of that are less significant than pure DP.
ZeRO stage 3 is not a good choice either for the same reason - more inter-node communications required.
And since we have ZeRO, the other benefit is ZeRO-Offload. Since this is stage 1 optimizer states can be offloaded to CPU.
Implementations:
- Megatron-DeepSpeed and Megatron-Deepspeed from BigScience, which is the fork of the former repo.
- OSLO
Important papers:

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

🤗 Transformers status: not yet implemented, since we have no PP and TP.
FlexFlow
FlexFlow also solves the parallelization problem in a slightly different approach.
Paper: ""Beyond Data and Model Parallelism for Deep Neural Networks"" by Zhihao Jia, Matei Zaharia, Alex Aiken
It performs a sort of 4D Parallelism over Sample-Operator-Attribute-Parameter.

Sample = Data Parallelism (sample-wise parallel)
Operator = Parallelize a single operation into several sub-operations
Attribute = Data Parallelism (length-wise parallel)
Parameter = Model Parallelism (regardless of dimension - horizontal or vertical)

Examples:
* Sample
Let's take 10 batches of sequence length 512. If we parallelize them by sample dimension into 2 devices, we get 10 x 512 which becomes be 5 x 2 x 512.

Operator

If we perform layer normalization, we compute std first and mean second, and then we can normalize data. Operator parallelism allows computing std and mean in parallel. So if we parallelize them by operator dimension into 2 devices (cuda:0, cuda:1), first we copy input data into both devices, and cuda:0 computes std, cuda:1 computes mean at the same time.

Attribute

We have 10 batches of 512 length. If we parallelize them by attribute dimension into 2 devices, 10 x 512 will be 10 x 2 x 256.

Parameter

It is similar with tensor model parallelism or naive layer-wise model parallelism.

The significance of this framework is that it takes resources like (1) GPU/TPU/CPU vs. (2) RAM/DRAM vs. (3) fast-intra-connect/slow-inter-connect and it automatically optimizes all these  algorithmically deciding which parallelisation to use where.
One very important aspect is that FlexFlow is designed for optimizing DNN parallelizations for models with static and fixed workloads, since models with dynamic behavior may prefer different parallelization strategies across iterations.
So the promise is very attractive - it runs a 30min simulation on the cluster of choice and it comes up with the best strategy to utilise this specific environment. If you add/remove/replace any parts it'll run and re-optimize the plan for that. And then you can train. A different setup will have its own custom optimization.
🤗 Transformers status: not yet integrated. We already have our models FX-trace-able via transformers.utils.fx, which is a prerequisite for FlexFlow, so someone needs to figure out what needs to be done to make FlexFlow work with our models.
Which Strategy To Use When
Here is a very rough outline at which parallelism strategy to use when. The first on each list is typically faster.
⇨ Single GPU

Model fits onto a single GPU:

Normal use

Model doesn't fit onto a single GPU:

ZeRO + Offload CPU and optionally NVMe
as above plus Memory Centric Tiling (see below for details) if the largest layer can't fit into a single GPU

Largest Layer not fitting into a single GPU:

ZeRO - Enable Memory Centric Tiling (MCT). It allows you to run arbitrarily large layers by automatically splitting them and executing them sequentially. MCT reduces the number of parameters that are live on a GPU, but it does not affect the activation memory. As this need is very rare as of this writing a manual override of torch.nn.Linear needs to be done by the user.

⇨ Single Node / Multi-GPU

Model fits onto a single GPU:

DDP - Distributed DP
ZeRO - may or may not be faster depending on the situation and configuration used

Model doesn't fit onto a single GPU:

PP
ZeRO
TP

With very fast intra-node connectivity of NVLINK or NVSwitch all three should be mostly on par, without these PP will be faster than TP or ZeRO. The degree of TP may also make a difference. Best to experiment to find the winner on your particular setup.
TP is almost always used within a single node. That is TP size <= gpus per node.

Largest Layer not fitting into a single GPU:

If not using ZeRO - must use TP, as PP alone won't be able to fit.
With ZeRO see the same entry for ""Single GPU"" above

⇨ Multi-Node / Multi-GPU

When you have fast inter-node connectivity:

ZeRO - as it requires close to no modifications to the model
PP+TP+DP - less communications, but requires massive changes to the model

when you have slow inter-node connectivity and still low on GPU memory:

DP+PP+TP+ZeRO-1

"
"
Processors
Processors can mean two different things in the Transformers library:
- the objects that pre-process inputs for multi-modal models such as Wav2Vec2 (speech and text)
  or CLIP (text and vision)
- deprecated objects that were used in older versions of the library to preprocess data for GLUE or SQUAD.
Multi-modal processors
Any multi-modal model will require an object to encode or decode the data that groups several modalities (among text,
vision and audio). This is handled by objects called processors, which group together two or more processing objects
such as tokenizers (for the text modality), image processors (for vision) and feature extractors (for audio).
Those processors inherit from the following base class that implements the saving and loading functionality:
[[autodoc]] ProcessorMixin
Deprecated processors
All processors follow the same architecture which is that of the
[~data.processors.utils.DataProcessor]. The processor returns a list of
[~data.processors.utils.InputExample]. These
[~data.processors.utils.InputExample] can be converted to
[~data.processors.utils.InputFeatures] in order to be fed to the model.
[[autodoc]] data.processors.utils.DataProcessor
[[autodoc]] data.processors.utils.InputExample
[[autodoc]] data.processors.utils.InputFeatures
GLUE
General Language Understanding Evaluation (GLUE) is a benchmark that evaluates the
performance of models across a diverse set of existing NLU tasks. It was released together with the paper GLUE: A
multi-task benchmark and analysis platform for natural language understanding
This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB,
QQP, QNLI, RTE and WNLI.
Those processors are:

[~data.processors.utils.MrpcProcessor]
[~data.processors.utils.MnliProcessor]
[~data.processors.utils.MnliMismatchedProcessor]
[~data.processors.utils.Sst2Processor]
[~data.processors.utils.StsbProcessor]
[~data.processors.utils.QqpProcessor]
[~data.processors.utils.QnliProcessor]
[~data.processors.utils.RteProcessor]
[~data.processors.utils.WnliProcessor]

Additionally, the following method can be used to load values from a data file and convert them to a list of
[~data.processors.utils.InputExample].
[[autodoc]] data.processors.glue.glue_convert_examples_to_features
XNLI
The Cross-Lingual NLI Corpus (XNLI) is a benchmark that evaluates the
quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on MultiNLI: pairs of text are labeled with textual entailment annotations for 15
different languages (including both high-resource language such as English and low-resource languages such as Swahili).
It was released together with the paper XNLI: Evaluating Cross-lingual Sentence Representations
This library hosts the processor to load the XNLI data:

[~data.processors.utils.XnliProcessor]

Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
An example using these processors is given in the run_xnli.py script.
SQuAD
The Stanford Question Answering Dataset (SQuAD) is a benchmark that
evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
(v1.1) was released together with the paper SQuAD: 100,000+ Questions for Machine Comprehension of Text. The second version (v2.0) was released alongside the paper Know What You Don't
Know: Unanswerable Questions for SQuAD.
This library hosts a processor for each of the two versions:
Processors
Those processors are:

[~data.processors.utils.SquadV1Processor]
[~data.processors.utils.SquadV2Processor]

They both inherit from the abstract class [~data.processors.utils.SquadProcessor]
[[autodoc]] data.processors.squad.SquadProcessor
    - all
Additionally, the following method can be used to convert SQuAD examples into
[~data.processors.utils.SquadFeatures] that can be used as model inputs.
[[autodoc]] data.processors.squad.squad_convert_examples_to_features
These processors as well as the aforementioned method can be used with files containing the data as well as with the
tensorflow_datasets package. Examples are given below.
Example usage
Here is an example using the processors as well as the conversion method using data files:
thon
Loading a V2 processor
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
Loading a V1 processor
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)
features = squad_convert_examples_to_features(
    examples=examples,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    doc_stride=args.doc_stride,
    max_query_length=max_query_length,
    is_training=not evaluate,
)

Using tensorflow_datasets is as easy as using a data file:
thon
tensorflow_datasets only handle Squad V1.
tfds_examples = tfds.load(""squad"")
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
features = squad_convert_examples_to_features(
    examples=examples,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    doc_stride=args.doc_stride,
    max_query_length=max_query_length,
    is_training=not evaluate,
)

Another example using these processors is given in the run_squad.py script."
"
How to add a model to 🤗 Transformers?
The 🤗 Transformers library is often able to offer new models thanks to community contributors. But this can be a challenging project and requires an in-depth knowledge of the 🤗 Transformers library and the model to implement. At Hugging Face, we're trying to empower more of the community to actively add models and we've put together this guide to walk you through the process of adding a PyTorch model (make sure you have PyTorch installed).

If you're interested in implementing a TensorFlow model, take a look at the How to convert a 🤗 Transformers model to TensorFlow guide!

Along the way, you'll:

get insights into open-source best practices
understand the design principles behind one of the most popular deep learning libraries
learn how to efficiently test large models
learn how to integrate Python utilities like black, ruff, and make fix-copies to ensure clean and readable code

A Hugging Face team member will be available to help you along the way so you'll never be alone. 🤗 ❤️
To get started, open a New model addition issue for the model you want to see in 🤗 Transformers. If you're not especially picky about contributing a specific model, you can filter by the New model label to see if there are any unclaimed model requests and work on it.
Once you've opened a new model request, the first step is to get familiar with 🤗 Transformers if you aren't already!
General overview of 🤗 Transformers
First, you should get a general overview of 🤗 Transformers. 🤗 Transformers is a very opinionated library, so there is a
chance that you don't agree with some of the library's philosophies or design choices. From our experience, however, we
found that the fundamental design choices and philosophies of the library are crucial to efficiently scale 🤗
Transformers while keeping maintenance costs at a reasonable level.
A good first starting point to better understand the library is to read the documentation of our philosophy. As a result of our way of working, there are some choices that we try to apply to all models:

Composition is generally favored over-abstraction
Duplicating code is not always bad if it strongly improves the readability or accessibility of a model
Model files are as self-contained as possible so that when you read the code of a specific model, you ideally only
  have to look into the respective modeling_.py file.

In our opinion, the library's code is not just a means to provide a product, e.g. the ability to use BERT for
inference, but also as the very product that we want to improve. Hence, when adding a model, the user is not only the
person that will use your model, but also everybody that will read, try to understand, and possibly tweak your code.
With this in mind, let's go a bit deeper into the general library design.
Overview of models
To successfully add a model, it is important to understand the interaction between your model and its config,
[PreTrainedModel], and [PretrainedConfig]. For exemplary purposes, we will
call the model to be added to 🤗 Transformers BrandNewBert.
Let's take a look:

As you can see, we do make use of inheritance in 🤗 Transformers, but we keep the level of abstraction to an absolute
minimum. There are never more than two levels of abstraction for any model in the library. BrandNewBertModel
inherits from BrandNewBertPreTrainedModel which in turn inherits from [PreTrainedModel] and
that's it. As a general rule, we want to make sure that a new model only depends on
[PreTrainedModel]. The important functionalities that are automatically provided to every new
model are [~PreTrainedModel.from_pretrained] and
[~PreTrainedModel.save_pretrained], which are used for serialization and deserialization. All of the
other important functionalities, such as BrandNewBertModel.forward should be completely defined in the new
modeling_brand_new_bert.py script. Next, we want to make sure that a model with a specific head layer, such as
BrandNewBertForMaskedLM does not inherit from BrandNewBertModel, but rather uses BrandNewBertModel
as a component that can be called in its forward pass to keep the level of abstraction low. Every new model requires a
configuration class, called BrandNewBertConfig. This configuration is always stored as an attribute in
[PreTrainedModel], and thus can be accessed via the config attribute for all classes
inheriting from BrandNewBertPreTrainedModel:
python
model = BrandNewBertModel.from_pretrained(""brandy/brand_new_bert"")
model.config  # model has access to its config
Similar to the model, the configuration inherits basic serialization and deserialization functionalities from
[PretrainedConfig]. Note that the configuration and the model are always serialized into two
different formats - the model to a pytorch_model.bin file and the configuration to a config.json file. Calling
[~PreTrainedModel.save_pretrained] will automatically call
[~PretrainedConfig.save_pretrained], so that both model and configuration are saved.
Code style
When coding your new model, keep in mind that Transformers is an opinionated library and we have a few quirks of our
own regarding how code should be written :-)

The forward pass of your model should be fully written in the modeling file while being fully independent of other
   models in the library. If you want to reuse a block from another model, copy the code and paste it with a
   # Copied from comment on top (see here
   for a good example).
The code should be fully understandable, even by a non-native English speaker. This means you should pick
   descriptive variable names and avoid abbreviations. As an example, activation is preferred to act.
   One-letter variable names are strongly discouraged unless it's an index in a for loop.
More generally we prefer longer explicit code to short magical one.
Avoid subclassing nn.Sequential in PyTorch but subclass nn.Module and write the forward pass, so that anyone
   using your code can quickly debug it by adding print statements or breaking points.
Your function signature should be type-annotated. For the rest, good variable names are way more readable and
   understandable than type annotations.

Overview of tokenizers
Not quite ready yet :-( This section will be added soon!
Step-by-step recipe to add a model to 🤗 Transformers
Everyone has different preferences of how to port a model so it can be very helpful for you to take a look at summaries
of how other contributors ported models to Hugging Face. Here is a list of community blog posts on how to port a model:

Porting GPT2 Model by Thomas
Porting WMT19 MT Model by Stas

From experience, we can tell you that the most important things to keep in mind when adding a model are:

Don't reinvent the wheel! Most parts of the code you will add for the new 🤗 Transformers model already exist
  somewhere in 🤗 Transformers. Take some time to find similar, already existing models and tokenizers you can copy
  from. grep and rg are your
  friends. Note that it might very well happen that your model's tokenizer is based on one model implementation, and
  your model's modeling code on another one. E.g. FSMT's modeling code is based on BART, while FSMT's tokenizer code
  is based on XLM.
It's more of an engineering challenge than a scientific challenge. You should spend more time on creating an
  efficient debugging environment than trying to understand all theoretical aspects of the model in the paper.
Ask for help, when you're stuck! Models are the core component of 🤗 Transformers so that we at Hugging Face are more
  than happy to help you at every step to add your model. Don't hesitate to ask if you notice you are not making
  progress.

In the following, we try to give you a general recipe that we found most useful when porting a model to 🤗 Transformers.
The following list is a summary of everything that has to be done to add a model and can be used by you as a To-Do
List:
☐ (Optional) Understood the model's theoretical aspects
☐ Prepared 🤗 Transformers dev environment
☐ Set up debugging environment of the original repository
☐ Created script that successfully runs the forward() pass using the original repository and checkpoint
☐ Successfully added the model skeleton to 🤗 Transformers
☐ Successfully converted original checkpoint to 🤗 Transformers checkpoint
☐ Successfully ran forward() pass in 🤗 Transformers that gives identical output to original checkpoint
☐ Finished model tests in 🤗 Transformers
☐ Successfully added tokenizer in 🤗 Transformers
☐ Run end-to-end integration tests
☐ Finished docs
☐ Uploaded model weights to the Hub
☐ Submitted the pull request
☐ (Optional) Added a demo notebook
To begin with, we usually recommend to start by getting a good theoretical understanding of BrandNewBert. However,
if you prefer to understand the theoretical aspects of the model on-the-job, then it is totally fine to directly dive
into the BrandNewBert's code-base. This option might suit you better, if your engineering skills are better than
your theoretical skill, if you have trouble understanding BrandNewBert's paper, or if you just enjoy programming
much more than reading scientific papers.
1. (Optional) Theoretical aspects of BrandNewBert
You should take some time to read BrandNewBert's paper, if such descriptive work exists. There might be large
sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is
not to get a deep theoretical understanding of the paper, but to extract the necessary information required to
effectively re-implement the model in 🤗 Transformers. That being said, you don't have to spend too much time on the
theoretical aspects, but rather focus on the practical ones, namely:

What type of model is brand_new_bert? BERT-like encoder-only model? GPT2-like decoder-only model? BART-like
  encoder-decoder model? Look at the model_summary if you're not familiar with the differences between those.
What are the applications of brand_new_bert? Text classification? Text generation? Seq2Seq tasks, e.g.,
  summarization?
What is the novel feature of the model making it different from BERT/GPT-2/BART?
Which of the already existing 🤗 Transformers models is most
  similar to brand_new_bert?
What type of tokenizer is used? A sentencepiece tokenizer? Word piece tokenizer? Is it the same tokenizer as used
  for BERT or BART?

After you feel like you have gotten a good overview of the architecture of the model, you might want to write to the
Hugging Face team with any questions you might have. This might include questions regarding the model's architecture,
its attention layer, etc. We will be more than happy to help you.
2. Next prepare your environment

Fork the repository by clicking on the ‘Fork' button on the
   repository's page. This creates a copy of the code under your GitHub user account.

Clone your transformers fork to your local disk, and add the base repository as a remote:

git clone https://github.com/[your Github handle]/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git

Set up a development environment, for instance by running the following command:

python -m venv .env
source .env/bin/activate
pip install -e "".[dev]""
Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
failure with this command. If that's the case make sure to install the Deep Learning framework you are working with
(PyTorch, TensorFlow and/or Flax) then do:

pip install -e "".[quality]""
which should be enough for most use cases. You can then return to the parent directory

cd ..

We recommend adding the PyTorch version of brand_new_bert to Transformers. To install PyTorch, please follow the
   instructions on https://pytorch.org/get-started/locally/.

Note: You don't need to have CUDA installed. Making the new model work on CPU is sufficient.

To port brand_new_bert, you will also need access to its original repository:

git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git
cd brand_new_bert
pip install -e .
Now you have set up a development environment to port brand_new_bert to 🤗 Transformers.
3.-4. Run a pretrained checkpoint using the original repository
At first, you will work on the original brand_new_bert repository. Often, the original implementation is very
“researchy”. Meaning that documentation might be lacking and the code can be difficult to understand. But this should
be exactly your motivation to reimplement brand_new_bert. At Hugging Face, one of our main goals is to make people
stand on the shoulders of giants which translates here very well into taking a working model and rewriting it to make
it as accessible, user-friendly, and beautiful as possible. This is the number-one motivation to re-implement
models into 🤗 Transformers - trying to make complex new NLP technology accessible to everybody.
You should start thereby by diving into the original repository.
Successfully running the official pretrained model in the original repository is often the most difficult step.
From our experience, it is very important to spend some time getting familiar with the original code-base. You need to
figure out the following:

Where to find the pretrained weights?
How to load the pretrained weights into the corresponding model?
How to run the tokenizer independently from the model?
Trace one forward pass so that you know which classes and functions are required for a simple forward pass. Usually,
  you only have to reimplement those functions.
Be able to locate the important components of the model: Where is the model's class? Are there model sub-classes,
  e.g. EncoderModel, DecoderModel? Where is the self-attention layer? Are there multiple different attention layers,
  e.g. self-attention, cross-attention?
How can you debug the model in the original environment of the repo? Do you have to add print statements, can you
  work with an interactive debugger like ipdb, or should you use an efficient IDE to debug the model, like PyCharm?

It is very important that before you start the porting process, that you can efficiently debug code in the original
repository! Also, remember that you are working with an open-source library, so do not hesitate to open an issue, or
even a pull request in the original repository. The maintainers of this repository are most likely very happy about
someone looking into their code!
At this point, it is really up to you which debugging environment and strategy you prefer to use to debug the original
model. We strongly advise against setting up a costly GPU environment, but simply work on a CPU both when starting to
dive into the original repository and also when starting to write the 🤗 Transformers implementation of the model. Only
at the very end, when the model has already been successfully ported to 🤗 Transformers, one should verify that the
model also works as expected on GPU.
In general, there are two possible debugging environments for running the original model

Jupyter notebooks / google colab
Local python scripts.

Jupyter notebooks have the advantage that they allow for cell-by-cell execution which can be helpful to better split
logical components from one another and to have faster debugging cycles as intermediate results can be stored. Also,
notebooks are often easier to share with other contributors, which might be very helpful if you want to ask the Hugging
Face team for help. If you are familiar with Jupyter notebooks, we strongly recommend you to work with them.
The obvious disadvantage of Jupyter notebooks is that if you are not used to working with them you will have to spend
some time adjusting to the new programming environment and that you might not be able to use your known debugging tools
anymore, like ipdb.
For each code-base, a good first step is always to load a small pretrained checkpoint and to be able to reproduce a
single forward pass using a dummy integer vector of input IDs as an input. Such a script could look like this (in
pseudocode):
python
model = BrandNewBertModel.load_pretrained_checkpoint(""/path/to/checkpoint/"")
input_ids = [0, 4, 5, 2, 3, 7, 9]  # vector of input ids
original_output = model.predict(input_ids)
Next, regarding the debugging strategy, there are generally a few from which to choose from:

Decompose the original model into many small testable components and run a forward pass on each of those for
  verification
Decompose the original model only into the original tokenizer and the original model, run a forward pass on
  those, and use intermediate print statements or breakpoints for verification

Again, it is up to you which strategy to choose. Often, one or the other is advantageous depending on the original code
base.
If the original code-base allows you to decompose the model into smaller sub-components, e.g. if the original
code-base can easily be run in eager mode, it is usually worth the effort to do so. There are some important advantages
to taking the more difficult road in the beginning:

at a later stage when comparing the original model to the Hugging Face implementation, you can verify automatically
  for each component individually that the corresponding component of the 🤗 Transformers implementation matches instead
  of relying on visual comparison via print statements
it can give you some rope to decompose the big problem of porting a model into smaller problems of just porting
  individual components and thus structure your work better
separating the model into logical meaningful components will help you to get a better overview of the model's design
  and thus to better understand the model
at a later stage those component-by-component tests help you to ensure that no regression occurs as you continue
  changing your code

Lysandre's integration checks for ELECTRA
gives a nice example of how this can be done.
However, if the original code-base is very complex or only allows intermediate components to be run in a compiled mode,
it might be too time-consuming or even impossible to separate the model into smaller testable sub-components. A good
example is T5's MeshTensorFlow library which is
very complex and does not offer a simple way to decompose the model into its sub-components. For such libraries, one
often relies on verifying print statements.
No matter which strategy you choose, the recommended procedure is often the same in that you should start to debug the
starting layers first and the ending layers last.
It is recommended that you retrieve the output, either by print statements or sub-component functions, of the following
layers in the following order:

Retrieve the input IDs passed to the model
Retrieve the word embeddings
Retrieve the input of the first Transformer layer
Retrieve the output of the first Transformer layer
Retrieve the output of the following n - 1 Transformer layers
Retrieve the output of the whole BrandNewBert Model

Input IDs should thereby consists of an array of integers, e.g. input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
The outputs of the following layers often consist of multi-dimensional float arrays and can look like this:
[[
 [-0.1465, -0.6501,  0.1993,  ,  0.1451,  0.3430,  0.6024],
 [-0.4417, -0.5920,  0.3450,  , -0.3062,  0.6182,  0.7132],
 [-0.5009, -0.7122,  0.4548,  , -0.3662,  0.6091,  0.7648],
 ,
 [-0.5613, -0.6332,  0.4324,  , -0.3792,  0.7372,  0.9288],
 [-0.5416, -0.6345,  0.4180,  , -0.3564,  0.6992,  0.9191],
 [-0.5334, -0.6403,  0.4271,  , -0.3339,  0.6533,  0.8694]]],
We expect that every model added to 🤗 Transformers passes a couple of integration tests, meaning that the original
model and the reimplemented version in 🤗 Transformers have to give the exact same output up to a precision of 0.001!
Since it is normal that the exact same model written in different libraries can give a slightly different output
depending on the library framework, we accept an error tolerance of 1e-3 (0.001). It is not enough if the model gives
nearly the same output, they have to be the almost identical. Therefore, you will certainly compare the intermediate
outputs of the 🤗 Transformers version multiple times against the intermediate outputs of the original implementation of
brand_new_bert in which case an efficient debugging environment of the original repository is absolutely
important. Here is some advice is to make your debugging environment as efficient as possible.

Find the best way of debugging intermediate results. Is the original repository written in PyTorch? Then you should
  probably take the time to write a longer script that decomposes the original model into smaller sub-components to
  retrieve intermediate values. Is the original repository written in Tensorflow 1? Then you might have to rely on
  TensorFlow print operations like tf.print to output
  intermediate values. Is the original repository written in Jax? Then make sure that the model is not jitted when
  running the forward pass, e.g. check-out this link.
Use the smallest pretrained checkpoint you can find. The smaller the checkpoint, the faster your debug cycle
  becomes. It is not efficient if your pretrained model is so big that your forward pass takes more than 10 seconds.
  In case only very large checkpoints are available, it might make more sense to create a dummy model in the new
  environment with randomly initialized weights and save those weights for comparison with the 🤗 Transformers version
  of your model
Make sure you are using the easiest way of calling a forward pass in the original repository. Ideally, you want to
  find the function in the original repository that only calls a single forward pass, i.e. that is often called
  predict, evaluate, forward or __call__. You don't want to debug a function that calls forward
  multiple times, e.g. to generate text, like autoregressive_sample, generate.
Try to separate the tokenization from the model's forward pass. If the original repository shows examples where
  you have to input a string, then try to find out where in the forward call the string input is changed to input ids
  and start from this point. This might mean that you have to possibly write a small script yourself or change the
  original code so that you can directly input the ids instead of an input string.
Make sure that the model in your debugging setup is not in training mode, which often causes the model to yield
  random outputs due to multiple dropout layers in the model. Make sure that the forward pass in your debugging
  environment is deterministic so that the dropout layers are not used. Or use transformers.utils.set_seed
  if the old and new implementations are in the same framework.

The following section gives you more specific details/tips on how you can do this for brand_new_bert.
5.-14. Port BrandNewBert to 🤗 Transformers
Next, you can finally start adding new code to 🤗 Transformers. Go into the clone of your 🤗 Transformers' fork:

cd transformers
In the special case that you are adding a model whose architecture exactly matches the model architecture of an
existing model you only have to add a conversion script as described in this section.
In this case, you can just re-use the whole model architecture of the already existing model.
Otherwise, let's start generating a new model. You have two choices here:

transformers-cli add-new-model-like to add a new model like an existing one
transformers-cli add-new-model to add a new model from our template (will look like BERT or Bart depending on the type of model you select)

In both cases, you will be prompted with a questionnaire to fill the basic information of your model. The second command requires to install cookiecutter, you can find more information on it here.
Open a Pull Request on the main huggingface/transformers repo
Before starting to adapt the automatically generated code, now is the time to open a “Work in progress (WIP)” pull
request, e.g. “[WIP] Add brand_new_bert”, in 🤗 Transformers so that you and the Hugging Face team can work
side-by-side on integrating the model into 🤗 Transformers.
You should do the following:

Create a branch with a descriptive name from your main branch

git checkout -b add_brand_new_bert

Commit the automatically generated code:

git add .
git commit

Fetch and rebase to current main

git fetch upstream
git rebase upstream/main

Push the changes to your account using:

git push -u origin a-descriptive-name-for-my-changes

Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the
   GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for
   future changes.

Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page.

In the following, whenever you have done some progress, don't forget to commit your work and push it to your account so
that it shows in the pull request. Additionally, you should make sure to update your work with the current main from
time to time by doing:

git fetch upstream
git merge upstream/main
In general, all questions you might have regarding the model or your implementation should be asked in your PR and
discussed/solved in the PR. This way, the Hugging Face team will always be notified when you are committing new code or
if you have a question. It is often very helpful to point the Hugging Face team to your added code so that the Hugging
Face team can efficiently understand your problem or question.
To do so, you can go to the “Files changed” tab where you see all of your changes, go to a line regarding which you
want to ask a question, and click on the “+” symbol to add a comment. Whenever a question or problem has been solved,
you can click on the “Resolve” button of the created comment.
In the same way, the Hugging Face team will open comments when reviewing your code. We recommend asking most questions
on GitHub on your PR. For some very general questions that are not very useful for the public, feel free to ping the
Hugging Face team by Slack or email.
5. Adapt the generated models code for brand_new_bert
At first, we will focus only on the model itself and not care about the tokenizer. All the relevant code should be
found in the generated files src/transformers/models/brand_new_bert/modeling_brand_new_bert.py and
src/transformers/models/brand_new_bert/configuration_brand_new_bert.py.
Now you can finally start coding :). The generated code in
src/transformers/models/brand_new_bert/modeling_brand_new_bert.py will either have the same architecture as BERT if
it's an encoder-only model or BART if it's an encoder-decoder model. At this point, you should remind yourself what
you've learned in the beginning about the theoretical aspects of the model: How is the model different from BERT or
BART?"". Implement those changes which often means to change the self-attention layer, the order of the normalization
layer, etc… Again, it is often useful to look at the similar architecture of already existing models in Transformers to
get a better feeling of how your model should be implemented.
Note that at this point, you don't have to be very sure that your code is fully correct or clean. Rather, it is
advised to add a first unclean, copy-pasted version of the original code to
src/transformers/models/brand_new_bert/modeling_brand_new_bert.py until you feel like all the necessary code is
added. From our experience, it is much more efficient to quickly add a first version of the required code and
improve/correct the code iteratively with the conversion script as described in the next section. The only thing that
has to work at this point is that you can instantiate the 🤗 Transformers implementation of brand_new_bert, i.e. the
following command should work:
thon
from transformers import BrandNewBertModel, BrandNewBertConfig
model = BrandNewBertModel(BrandNewBertConfig())

The above command will create a model according to the default parameters as defined in BrandNewBertConfig() with
random weights, thus making sure that the init() methods of all components works.
Note that all random initialization should happen in the _init_weights method of your BrandnewBertPreTrainedModel
class. It should initialize all leaf modules depending on the variables of the config. Here is an example with the
BERT _init_weights method:
py
def _init_weights(self, module):
    """"""Initialize the weights""""""
    if isinstance(module, nn.Linear):
        module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        if module.bias is not None:
            module.bias.data.zero_()
    elif isinstance(module, nn.Embedding):
        module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        if module.padding_idx is not None:
            module.weight.data[module.padding_idx].zero_()
    elif isinstance(module, nn.LayerNorm):
        module.bias.data.zero_()
        module.weight.data.fill_(1.0)
You can have some more custom schemes if you need a special initialization for some modules. For instance, in
Wav2Vec2ForPreTraining, the last two linear layers need to have the initialization of the regular PyTorch nn.Linear
but all the other ones should use an initialization as above. This is coded like this:
py
def _init_weights(self, module):
    """"""Initialize the weights""""""
    if isinstnace(module, Wav2Vec2ForPreTraining):
        module.project_hid.reset_parameters()
        module.project_q.reset_parameters()
        module.project_hid._is_hf_initialized = True
        module.project_q._is_hf_initialized = True
    elif isinstance(module, nn.Linear):
        module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        if module.bias is not None:
            module.bias.data.zero_()
The _is_hf_initialized flag is internally used to make sure we only initialize a submodule once. By setting it to
True for module.project_q and module.project_hid, we make sure the custom initialization we did is not overridden later on,
the _init_weights function won't be applied to them.
6. Write a conversion script
Next, you should write a conversion script that lets you convert the checkpoint you used to debug brand_new_bert in
the original repository to a checkpoint compatible with your just created 🤗 Transformers implementation of
brand_new_bert. It is not advised to write the conversion script from scratch, but rather to look through already
existing conversion scripts in 🤗 Transformers for one that has been used to convert a similar model that was written in
the same framework as brand_new_bert. Usually, it is enough to copy an already existing conversion script and
slightly adapt it for your use case. Don't hesitate to ask the Hugging Face team to point you to a similar already
existing conversion script for your model.

If you are porting a model from TensorFlow to PyTorch, a good starting point might be BERT's conversion script here
If you are porting a model from PyTorch to PyTorch, a good starting point might be BART's conversion script here

In the following, we'll quickly explain how PyTorch models store layer weights and define layer names. In PyTorch, the
name of a layer is defined by the name of the class attribute you give the layer. Let's define a dummy model in
PyTorch, called SimpleModel as follows:
thon
from torch import nn
class SimpleModel(nn.Module):
    def init(self):
        super().init()
        self.dense = nn.Linear(10, 10)
        self.intermediate = nn.Linear(10, 10)
        self.layer_norm = nn.LayerNorm(10)

Now we can create an instance of this model definition which will fill all weights: dense, intermediate,
layer_norm with random weights. We can print the model to see its architecture
thon
model = SimpleModel()
print(model)

This will print out the following:
SimpleModel(
  (dense): Linear(in_features=10, out_features=10, bias=True)
  (intermediate): Linear(in_features=10, out_features=10, bias=True)
  (layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True)
)
We can see that the layer names are defined by the name of the class attribute in PyTorch. You can print out the weight
values of a specific layer:
python
print(model.dense.weight.data)
to see that the weights were randomly initialized
tensor([[-0.0818,  0.2207, -0.0749, -0.0030,  0.0045, -0.1569, -0.1598,  0.0212,
         -0.2077,  0.2157],
        [ 0.1044,  0.0201,  0.0990,  0.2482,  0.3116,  0.2509,  0.2866, -0.2190,
          0.2166, -0.0212],
        [-0.2000,  0.1107, -0.1999, -0.3119,  0.1559,  0.0993,  0.1776, -0.1950,
         -0.1023, -0.0447],
        [-0.0888, -0.1092,  0.2281,  0.0336,  0.1817, -0.0115,  0.2096,  0.1415,
         -0.1876, -0.2467],
        [ 0.2208, -0.2352, -0.1426, -0.2636, -0.2889, -0.2061, -0.2849, -0.0465,
          0.2577,  0.0402],
        [ 0.1502,  0.2465,  0.2566,  0.0693,  0.2352, -0.0530,  0.1859, -0.0604,
          0.2132,  0.1680],
        [ 0.1733, -0.2407, -0.1721,  0.1484,  0.0358, -0.0633, -0.0721, -0.0090,
          0.2707, -0.2509],
        [-0.1173,  0.1561,  0.2945,  0.0595, -0.1996,  0.2988, -0.0802,  0.0407,
          0.1829, -0.1568],
        [-0.1164, -0.2228, -0.0403,  0.0428,  0.1339,  0.0047,  0.1967,  0.2923,
          0.0333, -0.0536],
        [-0.1492, -0.1616,  0.1057,  0.1950, -0.2807, -0.2710, -0.1586,  0.0739,
          0.2220,  0.2358]]).
In the conversion script, you should fill those randomly initialized weights with the exact weights of the
corresponding layer in the checkpoint. E.g.
thon
retrieve matching layer weights, e.g. by
recursive algorithm
layer_name = ""dense""
pretrained_weight = array_of_dense_layer
model_pointer = getattr(model, ""dense"")
model_pointer.weight.data = torch.from_numpy(pretrained_weight)

While doing so, you must verify that each randomly initialized weight of your PyTorch model and its corresponding
pretrained checkpoint weight exactly match in both shape and name. To do so, it is necessary to add assert
statements for the shape and print out the names of the checkpoints weights. E.g. you should add statements like:
python
assert (
    model_pointer.weight.shape == pretrained_weight.shape
), f""Pointer shape of random weight {model_pointer.shape} and array shape of checkpoint weight {pretrained_weight.shape} mismatched""
Besides, you should also print out the names of both weights to make sure they match, e.g.
python
logger.info(f""Initialize PyTorch weight {layer_name} from {pretrained_weight.name}"")
If either the shape or the name doesn't match, you probably assigned the wrong checkpoint weight to a randomly
initialized layer of the 🤗 Transformers implementation.
An incorrect shape is most likely due to an incorrect setting of the config parameters in BrandNewBertConfig() that
do not exactly match those that were used for the checkpoint you want to convert. However, it could also be that
PyTorch's implementation of a layer requires the weight to be transposed beforehand.
Finally, you should also check that all required weights are initialized and print out all checkpoint weights that
were not used for initialization to make sure the model is correctly converted. It is completely normal, that the
conversion trials fail with either a wrong shape statement or wrong name assignment. This is most likely because either
you used incorrect parameters in BrandNewBertConfig(), have a wrong architecture in the 🤗 Transformers
implementation, you have a bug in the init() functions of one of the components of the 🤗 Transformers
implementation or you need to transpose one of the checkpoint weights.
This step should be iterated with the previous step until all weights of the checkpoint are correctly loaded in the
Transformers model. Having correctly loaded the checkpoint into the 🤗 Transformers implementation, you can then save
the model under a folder of your choice /path/to/converted/checkpoint/folder that should then contain both a
pytorch_model.bin file and a config.json file:
python
model.save_pretrained(""/path/to/converted/checkpoint/folder"")
7. Implement the forward pass
Having managed to correctly load the pretrained weights into the 🤗 Transformers implementation, you should now make
sure that the forward pass is correctly implemented. In Get familiar with the original repository, you have already created a script that runs a forward
pass of the model using the original repository. Now you should write an analogous script using the 🤗 Transformers
implementation instead of the original one. It should look as follows:
python
model = BrandNewBertModel.from_pretrained(""/path/to/converted/checkpoint/folder"")
input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
output = model(input_ids).last_hidden_states
It is very likely that the 🤗 Transformers implementation and the original model implementation don't give the exact
same output the very first time or that the forward pass throws an error. Don't be disappointed - it's expected! First,
you should make sure that the forward pass doesn't throw any errors. It often happens that the wrong dimensions are
used leading to a Dimensionality mismatch error or that the wrong data type object is used, e.g. torch.long
instead of torch.float32. Don't hesitate to ask the Hugging Face team for help, if you don't manage to solve
certain errors.
The final part to make sure the 🤗 Transformers implementation works correctly is to ensure that the outputs are
equivalent to a precision of 1e-3. First, you should ensure that the output shapes are identical, i.e.
outputs.shape should yield the same value for the script of the 🤗 Transformers implementation and the original
implementation. Next, you should make sure that the output values are identical as well. This one of the most difficult
parts of adding a new model. Common mistakes why the outputs are not identical are:

Some layers were not added, i.e. an activation layer was not added, or the residual connection was forgotten
The word embedding matrix was not tied
The wrong positional embeddings are used because the original implementation uses on offset
Dropout is applied during the forward pass. To fix this make sure model.training is False and that no dropout
  layer is falsely activated during the forward pass, i.e. pass self.training to PyTorch's functional dropout

The best way to fix the problem is usually to look at the forward pass of the original implementation and the 🤗
Transformers implementation side-by-side and check if there are any differences. Ideally, you should debug/print out
intermediate outputs of both implementations of the forward pass to find the exact position in the network where the 🤗
Transformers implementation shows a different output than the original implementation. First, make sure that the
hard-coded input_ids in both scripts are identical. Next, verify that the outputs of the first transformation of
the input_ids (usually the word embeddings) are identical. And then work your way up to the very last layer of the
network. At some point, you will notice a difference between the two implementations, which should point you to the bug
in the 🤗 Transformers implementation. From our experience, a simple and efficient way is to add many print statements
in both the original implementation and 🤗 Transformers implementation, at the same positions in the network
respectively, and to successively remove print statements showing the same values for intermediate presentations.
When you're confident that both implementations yield the same output, verifying the outputs with
torch.allclose(original_output, output, atol=1e-3), you're done with the most difficult part! Congratulations - the
work left to be done should be a cakewalk 😊.
8. Adding all necessary model tests
At this point, you have successfully added a new model. However, it is very much possible that the model does not yet
fully comply with the required design. To make sure, the implementation is fully compatible with 🤗 Transformers, all
common tests should pass. The Cookiecutter should have automatically added a test file for your model, probably under
the same tests/models/brand_new_bert/test_modeling_brand_new_bert.py. Run this test file to verify that all common
tests pass:

pytest tests/models/brand_new_bert/test_modeling_brand_new_bert.py
Having fixed all common tests, it is now crucial to ensure that all the nice work you have done is well tested, so that

a) The community can easily understand your work by looking at specific tests of brand_new_bert
b) Future changes to your model will not break any important feature of the model.

At first, integration tests should be added. Those integration tests essentially do the same as the debugging scripts
you used earlier to implement the model to 🤗 Transformers. A template of those model tests is already added by the
Cookiecutter, called BrandNewBertModelIntegrationTests and only has to be filled out by you. To ensure that those
tests are passing, run

RUN_SLOW=1 pytest -sv tests/models/brand_new_bert/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests

In case you are using Windows, you should replace RUN_SLOW=1 with SET RUN_SLOW=1

Second, all features that are special to brand_new_bert should be tested additionally in a separate test under
BrandNewBertModelTester/`BrandNewBertModelTest. This part is often forgotten but is extremely useful in two
ways:

It helps to transfer the knowledge you have acquired during the model addition to the community by showing how the
  special features of brand_new_bert should work.
Future contributors can quickly test changes to the model by running those special tests.

9. Implement the tokenizer
Next, we should add the tokenizer of brand_new_bert. Usually, the tokenizer is equivalent or very similar to an
already existing tokenizer of 🤗 Transformers.
It is very important to find/extract the original tokenizer file and to manage to load this file into the 🤗
Transformers' implementation of the tokenizer.
To ensure that the tokenizer works correctly, it is recommended to first create a script in the original repository
that inputs a string and returns the `input_ids``. It could look similar to this (in pseudo-code):
python
input_str = ""This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words.""
model = BrandNewBertModel.load_pretrained_checkpoint(""/path/to/checkpoint/"")
input_ids = model.tokenize(input_str)
You might have to take a deeper look again into the original repository to find the correct tokenizer function or you
might even have to do changes to your clone of the original repository to only output the input_ids. Having written
a functional tokenization script that uses the original repository, an analogous script for 🤗 Transformers should be
created. It should look similar to this:
thon
from transformers import BrandNewBertTokenizer
input_str = ""This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words.""
tokenizer = BrandNewBertTokenizer.from_pretrained(""/path/to/tokenizer/folder/"")
input_ids = tokenizer(input_str).input_ids

When both input_ids yield the same values, as a final step a tokenizer test file should also be added.
Analogous to the modeling test files of brand_new_bert, the tokenization test files of brand_new_bert should
contain a couple of hard-coded integration tests.
10. Run End-to-end integration tests
Having added the tokenizer, you should also add a couple of end-to-end integration tests using both the model and the
tokenizer to tests/models/brand_new_bert/test_modeling_brand_new_bert.py in 🤗 Transformers.
Such a test should show on a meaningful
text-to-text sample that the 🤗 Transformers implementation works as expected. A meaningful text-to-text sample can
include e.g. a source-to-target-translation pair, an article-to-summary pair, a question-to-answer pair, etc… If none
of the ported checkpoints has been fine-tuned on a downstream task it is enough to simply rely on the model tests. In a
final step to ensure that the model is fully functional, it is advised that you also run all tests on GPU. It can
happen that you forgot to add some .to(self.device) statements to internal tensors of the model, which in such a
test would show in an error. In case you have no access to a GPU, the Hugging Face team can take care of running those
tests for you.
11. Add Docstring
Now, all the necessary functionality for brand_new_bert is added - you're almost done! The only thing left to add is
a nice docstring and a doc page. The Cookiecutter should have added a template file called
docs/source/model_doc/brand_new_bert.md that you should fill out. Users of your model will usually first look at
this page before using your model. Hence, the documentation must be understandable and concise. It is very useful for
the community to add some Tips to show how the model should be used. Don't hesitate to ping the Hugging Face team
regarding the docstrings.
Next, make sure that the docstring added to src/transformers/models/brand_new_bert/modeling_brand_new_bert.py is
correct and included all necessary inputs and outputs. We have a detailed guide about writing documentation and our docstring format here. It is always to good to remind oneself that documentation should
be treated at least as carefully as the code in 🤗 Transformers since the documentation is usually the first contact
point of the community with the model.
Code refactor
Great, now you have added all the necessary code for brand_new_bert. At this point, you should correct some potential
incorrect code style by running:

make style
and verify that your coding style passes the quality check:

make quality
There are a couple of other very strict design tests in 🤗 Transformers that might still be failing, which shows up in
the tests of your pull request. This is often because of some missing information in the docstring or some incorrect
naming. The Hugging Face team will surely help you if you're stuck here.
Lastly, it is always a good idea to refactor one's code after having ensured that the code works correctly. With all
tests passing, now it's a good time to go over the added code again and do some refactoring.
You have now finished the coding part, congratulation! 🎉 You are Awesome! 😎
12. Upload the models to the model hub
In this final part, you should convert and upload all checkpoints to the model hub and add a model card for each
uploaded model checkpoint. You can get familiar with the hub functionalities by reading our Model sharing and uploading Page. You should work alongside the Hugging Face team here to decide on a fitting name for each
checkpoint and to get the required access rights to be able to upload the model under the author's organization of
brand_new_bert. The push_to_hub method, present in all models in transformers, is a quick and efficient way to push your checkpoint to the hub. A little snippet is pasted below:
thon
brand_new_bert.push_to_hub(""brand_new_bert"")
Uncomment the following line to push to an organization.
brand_new_bert.push_to_hub(""/brand_new_bert"")

It is worth spending some time to create fitting model cards for each checkpoint. The model cards should highlight the
specific characteristics of this particular checkpoint, e.g. On which dataset was the checkpoint
pretrained/fine-tuned on? On what down-stream task should the model be used? And also include some code on how to
correctly use the model.
13. (Optional) Add notebook
It is very helpful to add a notebook that showcases in-detail how brand_new_bert can be used for inference and/or
fine-tuned on a downstream task. This is not mandatory to merge your PR, but very useful for the community.
14. Submit your finished PR
You're done programming now and can move to the last step, which is getting your PR merged into main. Usually, the
Hugging Face team should have helped you already at this point, but it is worth taking some time to give your finished
PR a nice description and eventually add comments to your code, if you want to point out certain design choices to your
reviewer.
Share your work!!
Now, it's time to get some credit from the community for your work! Having completed a model addition is a major
contribution to Transformers and the whole NLP community. Your code and the ported pre-trained models will certainly be
used by hundreds and possibly even thousands of developers and researchers. You should be proud of your work and share
your achievement with the community.
You have made another model that is super easy to access for everyone in the community! 🤯"
"
Fine-tune a pretrained model
[[open-in-colab]]
There are significant benefits to using a pretrained model. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch. 🤗 Transformers provides access to thousands of pretrained models for a wide range of tasks. When you use a pretrained model, you train it on a dataset specific to your task. This is known as fine-tuning, an incredibly powerful training technique. In this tutorial, you will fine-tune a pretrained model with a deep learning framework of your choice:

Fine-tune a pretrained model with 🤗 Transformers [Trainer].
Fine-tune a pretrained model in TensorFlow with Keras.
Fine-tune a pretrained model in native PyTorch.

Prepare a dataset

Before you can fine-tune a pretrained model, download a dataset and prepare it for training. The previous tutorial showed you how to process data for training, and now you get an opportunity to put those skills to the test!
Begin by loading the Yelp Reviews dataset:

from datasets import load_dataset
dataset = load_dataset(""yelp_review_full"")
dataset[""train""][100]
{'label': 0,
 'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularlythat takes something special!\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \""serving off their orders\"" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}

As you now know, you need a tokenizer to process the text and include a padding and truncation strategy to handle any variable sequence lengths. To process your dataset in one step, use 🤗 Datasets map method to apply a preprocessing function over the entire dataset:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""bert-base-cased"")
def tokenize_function(examples):
     return tokenizer(examples[""text""], padding=""max_length"", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)

If you like, you can create a smaller subset of the full dataset to fine-tune on to reduce the time it takes:

small_train_dataset = tokenized_datasets[""train""].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets[""test""].shuffle(seed=42).select(range(1000))

Train
At this point, you should follow the section corresponding to the framework you want to use. You can use the links
in the right sidebar to jump to the one you want - and if you want to hide all of the content for a given framework,
just use the button at the top-right of that framework's block!

Train with PyTorch Trainer
🤗 Transformers provides a [Trainer] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [Trainer] API supports a wide range of training options and features such as logging, gradient accumulation, and mixed precision.
Start by loading your model and specify the number of expected labels. From the Yelp Review dataset card, you know there are five labels:

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(""bert-base-cased"", num_labels=5)

You will see a warning about some of the pretrained weights not being used and some weights being randomly
initialized. Don't worry, this is completely normal! The pretrained head of the BERT model is discarded, and replaced with a randomly initialized classification head. You will fine-tune this new model head on your sequence classification task, transferring the knowledge of the pretrained model to it.

Training hyperparameters
Next, create a [TrainingArguments] class which contains all the hyperparameters you can tune as well as flags for activating different training options. For this tutorial you can start with the default training hyperparameters, but feel free to experiment with these to find your optimal settings.
Specify where to save the checkpoints from your training:

from transformers import TrainingArguments
training_args = TrainingArguments(output_dir=""test_trainer"")

Evaluate
[Trainer] does not automatically evaluate model performance during training. You'll need to pass [Trainer] a function to compute and report metrics. The 🤗 Evaluate library provides a simple accuracy function you can load with the [evaluate.load] (see this quicktour for more information) function:

import numpy as np
import evaluate
metric = evaluate.load(""accuracy"")

Call [~evaluate.compute] on metric to calculate the accuracy of your predictions. Before passing your predictions to compute, you need to convert the predictions to logits (remember all 🤗 Transformers models return logits):

def compute_metrics(eval_pred):
     logits, labels = eval_pred
     predictions = np.argmax(logits, axis=-1)
     return metric.compute(predictions=predictions, references=labels)

If you'd like to monitor your evaluation metrics during fine-tuning, specify the evaluation_strategy parameter in your training arguments to report the evaluation metric at the end of each epoch:

from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(output_dir=""test_trainer"", evaluation_strategy=""epoch"")

Trainer
Create a [Trainer] object with your model, training arguments, training and test datasets, and evaluation function:

trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=small_train_dataset,
     eval_dataset=small_eval_dataset,
     compute_metrics=compute_metrics,
 )

Then fine-tune your model by calling [~transformers.Trainer.train]:

trainer.train()

Train a TensorFlow model with Keras
You can also train 🤗 Transformers models in TensorFlow with the Keras API!
Loading data for Keras
When you want to train a 🤗 Transformers model with the Keras API, you need to convert your dataset to a format that
Keras understands. If your dataset is small, you can just convert the whole thing to NumPy arrays and pass it to Keras.
Let's try that first before we do anything more complicated.
First, load a dataset. We'll use the CoLA dataset from the GLUE benchmark,
since it's a simple binary text classification task, and just take the training split for now.

from datasets import load_dataset
dataset = load_dataset(""glue"", ""cola"")
dataset = dataset[""train""]  # Just take the training split for now

Next, load a tokenizer and tokenize the data as NumPy arrays. Note that the labels are already a list of 0 and 1s,
so we can just convert that directly to a NumPy array without tokenization!

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""bert-base-cased"")
tokenized_data = tokenizer(dataset[""sentence""], return_tensors=""np"", padding=True)
Tokenizer returns a BatchEncoding, but we convert that to a dict for Keras
tokenized_data = dict(tokenized_data)
labels = np.array(dataset[""label""])  # Label is already an array of 0 and 1

Finally, load, compile, and fit the model. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

from transformers import TFAutoModelForSequenceClassification
from tensorflow.keras.optimizers import Adam
Load and compile our model
model = TFAutoModelForSequenceClassification.from_pretrained(""bert-base-cased"")
Lower learning rates are often better for fine-tuning transformers
model.compile(optimizer=Adam(3e-5))  # No loss argument!
model.fit(tokenized_data, labels)

You don't have to pass a loss argument to your models when you compile() them! Hugging Face models automatically
choose a loss that is appropriate for their task and model architecture if this argument is left blank. You can always
override this by specifying a loss yourself if you want to!

This approach works great for smaller datasets, but for larger datasets, you might find it starts to become a problem. Why?
Because the tokenized array and labels would have to be fully loaded into memory, and because NumPy doesn’t handle
“jagged” arrays, so every tokenized sample would have to be padded to the length of the longest sample in the whole
dataset. That’s going to make your array even bigger, and all those padding tokens will slow down training too!
Loading data as a tf.data.Dataset
If you want to avoid slowing down training, you can load your data as a tf.data.Dataset instead. Although you can write your own
tf.data pipeline if you want, we have two convenience methods for doing this:

[~TFPreTrainedModel.prepare_tf_dataset]: This is the method we recommend in most cases. Because it is a method
on your model, it can inspect the model to automatically figure out which columns are usable as model inputs, and
discard the others to make a simpler, more performant dataset.
[~datasets.Dataset.to_tf_dataset]: This method is more low-level, and is useful when you want to exactly control how
your dataset is created, by specifying exactly which columns and label_cols to include.

Before you can use [~TFPreTrainedModel.prepare_tf_dataset], you will need to add the tokenizer outputs to your dataset as columns, as shown in
the following code sample:

def tokenize_dataset(data):
    # Keys of the returned dictionary will be added to the dataset as columns
    return tokenizer(data[""text""])
dataset = dataset.map(tokenize_dataset)

Remember that Hugging Face datasets are stored on disk by default, so this will not inflate your memory usage! Once the
columns have been added, you can stream batches from the dataset and add padding to each batch, which greatly
reduces the number of padding tokens compared to padding the entire dataset.

tf_dataset = model.prepare_tf_dataset(dataset[""train""], batch_size=16, shuffle=True, tokenizer=tokenizer)

Note that in the code sample above, you need to pass the tokenizer to prepare_tf_dataset so it can correctly pad batches as they're loaded.
If all the samples in your dataset are the same length and no padding is necessary, you can skip this argument.
If you need to do something more complex than just padding samples (e.g. corrupting tokens for masked language
modelling), you can use the collate_fn argument instead to pass a function that will be called to transform the
list of samples into a batch and apply any preprocessing you want. See our
examples or
notebooks to see this approach in action.
Once you've created a tf.data.Dataset, you can compile and fit the model as before:

model.compile(optimizer=Adam(3e-5))  # No loss argument!
model.fit(tf_dataset)

Train in native PyTorch

[Trainer] takes care of the training loop and allows you to fine-tune a model in a single line of code. For users who prefer to write their own training loop, you can also fine-tune a 🤗 Transformers model in native PyTorch.
At this point, you may need to restart your notebook or execute the following code to free some memory:
py
del model
del trainer
torch.cuda.empty_cache()
Next, manually postprocess tokenized_dataset to prepare it for training.

Remove the text column because the model does not accept raw text as an input:

tokenized_datasets = tokenized_datasets.remove_columns([""text""])

Rename the label column to labels because the model expects the argument to be named labels:

tokenized_datasets = tokenized_datasets.rename_column(""label"", ""labels"")

Set the format of the dataset to return PyTorch tensors instead of lists:

tokenized_datasets.set_format(""torch"")

Then create a smaller subset of the dataset as previously shown to speed up the fine-tuning:

small_train_dataset = tokenized_datasets[""train""].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets[""test""].shuffle(seed=42).select(range(1000))

DataLoader
Create a DataLoader for your training and test datasets so you can iterate over batches of data:

from torch.utils.data import DataLoader
train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)

Load your model with the number of expected labels:

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(""bert-base-cased"", num_labels=5)

Optimizer and learning rate scheduler
Create an optimizer and learning rate scheduler to fine-tune the model. Let's use the AdamW optimizer from PyTorch:

from torch.optim import AdamW
optimizer = AdamW(model.parameters(), lr=5e-5)

Create the default learning rate scheduler from [Trainer]:

from transformers import get_scheduler
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
     name=""linear"", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps
 )

Lastly, specify device to use a GPU if you have access to one. Otherwise, training on a CPU may take several hours instead of a couple of minutes.

import torch
device = torch.device(""cuda"") if torch.cuda.is_available() else torch.device(""cpu"")
model.to(device)

Get free access to a cloud GPU if you don't have one with a hosted notebook like Colaboratory or SageMaker StudioLab.

Great, now you are ready to train! 🥳 
Training loop
To keep track of your training progress, use the tqdm library to add a progress bar over the number of training steps:

from tqdm.auto import tqdm
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
     for batch in train_dataloader:
         batch = {k: v.to(device) for k, v in batch.items()}
         outputs = model(**batch)
         loss = outputs.loss
         loss.backward()

         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()
         progress_bar.update(1)

Evaluate
Just like how you added an evaluation function to [Trainer], you need to do the same when you write your own training loop. But instead of calculating and reporting the metric at the end of each epoch, this time you'll accumulate all the batches with [~evaluate.add_batch] and calculate the metric at the very end.

import evaluate
metric = evaluate.load(""accuracy"")
model.eval()
for batch in eval_dataloader:
     batch = {k: v.to(device) for k, v in batch.items()}
     with torch.no_grad():
         outputs = model(**batch)

     logits = outputs.logits
     predictions = torch.argmax(logits, dim=-1)
     metric.add_batch(predictions=predictions, references=batch[""labels""])

metric.compute()

Additional resources
For more fine-tuning examples, refer to:

🤗 Transformers Examples includes scripts
  to train common NLP tasks in PyTorch and TensorFlow.

🤗 Transformers Notebooks contains various notebooks on how to fine-tune a model for specific tasks in PyTorch and TensorFlow.

"
"
Pipelines for inference
The [pipeline] makes it simple to use any model from the Hub for inference on any language, computer vision, speech, and multimodal tasks. Even if you don't have experience with a specific modality or aren't familiar with the underlying code behind the models, you can still use them for inference with the [pipeline]! This tutorial will teach you to:

Use a [pipeline] for inference.
Use a specific tokenizer or model.
Use a [pipeline] for audio, vision, and multimodal tasks.

Take a look at the [pipeline] documentation for a complete list of supported tasks and available parameters.

Pipeline usage
While each task has an associated [pipeline], it is simpler to use the general [pipeline] abstraction which contains all the task-specific pipelines. The [pipeline] automatically loads a default model and a preprocessing class capable of inference for your task.

Start by creating a [pipeline] and specify an inference task:

from transformers import pipeline
generator = pipeline(task=""automatic-speech-recognition"")

Pass your input text to the [pipeline]:

generator(""https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac"")
{'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP LIVE UP THE TRUE MEANING OF ITS TREES'}

Not the result you had in mind? Check out some of the most downloaded automatic speech recognition models on the Hub to see if you can get a better transcription.
Let's try openai/whisper-large:

generator = pipeline(model=""openai/whisper-large"")
generator(""https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac"")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}

Now this result looks more accurate!
We really encourage you to check out the Hub for models in different languages, models specialized in your field, and more.
You can check out and compare model results directly from your browser on the Hub to see if it fits or 
handles corner cases better than other ones.
And if you don't find a model for your use case, you can always start training your own!
If you have several inputs, you can pass your input as a list:
py
generator(
    [
        ""https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac"",
        ""https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac"",
    ]
)
If you want to iterate over a whole dataset, or want to use it for inference in a webserver, check out dedicated parts
Using pipelines on a dataset
Using pipelines for a webserver
Parameters
[pipeline] supports many parameters; some are task specific, and some are general to all pipelines.
In general you can specify parameters anywhere you want:
py
generator = pipeline(model=""openai/whisper-large"", my_parameter=1)
out = generator()  # This will use `my_parameter=1`.
out = generator(, my_parameter=2)  # This will override and use `my_parameter=2`.
out = generator()  # This will go back to using `my_parameter=1`.
Let's check out 3 important ones:
Device
If you use device=n, the pipeline automatically puts the model on the specified device.
This will work regardless of whether you are using PyTorch or Tensorflow.
py
generator = pipeline(model=""openai/whisper-large"", device=0)
If the model is too large for a single GPU, you can set device_map=""auto"" to allow 🤗 Accelerate to automatically determine how to load and store the model weights.

!pip install accelerate
generator = pipeline(model=""openai/whisper-large"", device_map=""auto"")

Note that if  device_map=""auto"" is passed, there is no need to add the argument device=device when instantiating your pipeline as you may encounter some unexpected behavior!
Batch size
By default, pipelines will not batch inference for reasons explained in detail here. The reason is that batching is not necessarily faster, and can actually be quite slower in some cases.
But if it works in your use case, you can use:
py
generator = pipeline(model=""openai/whisper-large"", device=0, batch_size=2)
audio_filenames = [f""audio_{i}.flac"" for i in range(10)]
texts = generator(audio_filenames)
This runs the pipeline on the 10 provided audio files, but it will pass them in batches of 2
to the model (which is on a GPU, where batching is more likely to help) without requiring any further code from you. 
The output should always match what you would have received without batching. It is only meant as a way to help you get more speed out of a pipeline.
Pipelines can also alleviate some of the complexities of batching because, for some pipelines, a single item (like a long audio file) needs to be chunked into multiple parts to be processed by a model. The pipeline performs this chunk batching for you.
Task specific parameters
All tasks provide task specific parameters which allow for additional flexibility and options to help you get your job done.
For instance, the [transformers.AutomaticSpeechRecognitionPipeline.__call__] method has a return_timestamps parameter which sounds promising for subtitling videos:

Not using whisper, as it cannot provide timestamps.
generator = pipeline(model=""facebook/wav2vec2-large-960h-lv60-self"", return_timestamps=""word"")
generator(""https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac"")
{'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP AND LIVE OUT THE TRUE MEANING OF ITS CREED', 'chunks': [{'text': 'I', 'timestamp': (1.22, 1.24)}, {'text': 'HAVE', 'timestamp': (1.42, 1.58)}, {'text': 'A', 'timestamp': (1.66, 1.68)}, {'text': 'DREAM', 'timestamp': (1.76, 2.14)}, {'text': 'BUT', 'timestamp': (3.68, 3.8)}, {'text': 'ONE', 'timestamp': (3.94, 4.06)}, {'text': 'DAY', 'timestamp': (4.16, 4.3)}, {'text': 'THIS', 'timestamp': (6.36, 6.54)}, {'text': 'NATION', 'timestamp': (6.68, 7.1)}, {'text': 'WILL', 'timestamp': (7.32, 7.56)}, {'text': 'RISE', 'timestamp': (7.8, 8.26)}, {'text': 'UP', 'timestamp': (8.38, 8.48)}, {'text': 'AND', 'timestamp': (10.08, 10.18)}, {'text': 'LIVE', 'timestamp': (10.26, 10.48)}, {'text': 'OUT', 'timestamp': (10.58, 10.7)}, {'text': 'THE', 'timestamp': (10.82, 10.9)}, {'text': 'TRUE', 'timestamp': (10.98, 11.18)}, {'text': 'MEANING', 'timestamp': (11.26, 11.58)}, {'text': 'OF', 'timestamp': (11.66, 11.7)}, {'text': 'ITS', 'timestamp': (11.76, 11.88)}, {'text': 'CREED', 'timestamp': (12.0, 12.38)}]}

As you can see, the model inferred the text and also outputted when the various words were pronounced
in the sentence.
There are many parameters available for each task, so check out each task's API reference to see what you can tinker with!
For instance, the [~transformers.AutomaticSpeechRecognitionPipeline] has a chunk_length_s parameter which is helpful for working on really long audio files (for example, subtitling entire movies or hour-long videos) that a model typically cannot handle on its own.
If you can't find a parameter that would really help you out, feel free to request it!
Using pipelines on a dataset
The pipeline can also run inference on a large dataset. The easiest way we recommend doing this is by using an iterator:

def data():
    for i in range(1000):
        yield f""My example {i}""
pipe = pipeline(model=""gpt2"", device=0)
generated_characters = 0
for out in pipe(data()):
    generated_characters += len(out[0][""generated_text""])

The iterator data() yields each result, and the pipeline automatically
recognizes the input is iterable and will start fetching the data while
it continues to process it on the GPU (this uses DataLoader under the hood).
This is important because you don't have to allocate memory for the whole dataset
and you can feed the GPU as fast as possible.
Since batching could speed things up, it may be useful to try tuning the batch_size parameter here.
The simplest way to iterate over a dataset is to just load one from 🤗 Datasets:

KeyDataset is a util that will just output the item we're interested in.
from transformers.pipelines.pt_utils import KeyDataset
from datasets import load_dataset
pipe = pipeline(model=""hf-internal-testing/tiny-random-wav2vec2"", device=0)
dataset = load_dataset(""hf-internal-testing/librispeech_asr_dummy"", ""clean"", split=""validation[:10]"")
for out in pipe(KeyDataset(dataset, ""audio"")):
    print(out)

Using pipelines for a webserver

Creating an inference engine is a complex topic which deserves it's own
page.

Link
Vision pipeline
Using a [pipeline] for vision tasks is practically identical.
Specify your task and pass your image to the classifier. The image can be a link or a local path to the image. For example, what species of cat is shown below?

from transformers import pipeline
vision_classifier = pipeline(model=""google/vit-base-patch16-224"")
preds = vision_classifier(
     images=""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg""
 )
preds = [{""score"": round(pred[""score""], 4), ""label"": pred[""label""]} for pred in preds]
preds
[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]

Text pipeline
Using a [pipeline] for NLP tasks is practically identical.

from transformers import pipeline
This model is a zero-shot-classification model.
It will classify text, except you are free to choose any label you might imagine
classifier = pipeline(model=""facebook/bart-large-mnli"")
classifier(
     ""I have a problem with my iphone that needs to be resolved asap!!"",
     candidate_labels=[""urgent"", ""not urgent"", ""phone"", ""tablet"", ""computer""],
 )
{'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]}

Multimodal pipeline
The [pipeline] supports more than one modality. For example, a visual question answering (VQA) task combines text and image. Feel free to use any image link you like and a question you want to ask about the image. The image can be a URL or a local path to the image.
For example, if you use this invoice image:

from transformers import pipeline
vqa = pipeline(model=""impira/layoutlm-document-qa"")
vqa(
     image=""https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"",
     question=""What is the invoice number?"",
 )
[{'score': 0.42515, 'answer': 'us-001', 'start': 16, 'end': 16}]

To run the example above you need to have pytesseract installed in addition to 🤗 Transformers:

sudo apt install -y tesseract-ocr
pip install pytesseract

Using pipeline on large models with 🤗 accelerate:
You can easily run pipeline on large models using 🤗 accelerate! First make sure you have installed accelerate with pip install accelerate. 
First load your model using device_map=""auto""! We will use facebook/opt-1.3b for our example.

pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline(model=""facebook/opt-1.3b"", torch_dtype=torch.bfloat16, device_map=""auto"")
output = pipe(""This is a cool example!"", do_sample=True, top_p=0.95)

You can also pass 8-bit loaded models if you install bitsandbytes and add the argument load_in_8bit=True

pip install accelerate bitsandbytes
import torch
from transformers import pipeline
pipe = pipeline(model=""facebook/opt-1.3b"", device_map=""auto"", model_kwargs={""load_in_8bit"": True})
output = pipe(""This is a cool example!"", do_sample=True, top_p=0.95)

Note that you can replace the checkpoint with any of the Hugging Face model that supports large model loading such as BLOOM!"
"
Zero-shot image classification
[[open-in-colab]]
Zero-shot image classification is a task that involves classifying images into different categories using a model that was
not explicitly trained on data containing labeled examples from those specific categories.
Traditionally, image classification requires training a model on a specific set of labeled images, and this model learns to
""map"" certain image features to labels. When there's a need to use such model for a classification task that introduces a
new set of labels, fine-tuning is required to ""recalibrate"" the model.
In contrast, zero-shot or open vocabulary image classification models are typically multi-modal models that have been trained on a large
dataset of images and associated descriptions. These models learn aligned vision-language representations that can be used for many downstream tasks including zero-shot image classification.
This is a more flexible approach to image classification that allows models to generalize to new and unseen categories
without the need for additional training data and enables users to query images with free-form text descriptions of their target objects .
In this guide you'll learn how to:

create a zero-shot image classification pipeline
run zero-shot image classification inference by hand

Before you begin, make sure you have all the necessary libraries installed:

pip install -q transformers
Zero-shot image classification pipeline
The simplest way to try out inference with a model supporting zero-shot image classification is to use the corresponding [pipeline].
Instantiate a pipeline from a checkpoint on the Hugging Face Hub:
thon

from transformers import pipeline
checkpoint = ""openai/clip-vit-large-patch14""
detector = pipeline(model=checkpoint, task=""zero-shot-image-classification"")

Next, choose an image you'd like to classify.

from PIL import Image
import requests
url = ""https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640""
image = Image.open(requests.get(url, stream=True).raw)
image

Pass the image and the candidate object labels to the pipeline. Here we pass the image directly; other suitable options
include a local path to an image or an image url.
The candidate labels can be simple words like in this example, or more descriptive.

predictions = classifier(image, candidate_labels=[""fox"", ""bear"", ""seagull"", ""owl""])
predictions
[{'score': 0.9996670484542847, 'label': 'owl'},
 {'score': 0.000199399160919711, 'label': 'seagull'},
 {'score': 7.392891711788252e-05, 'label': 'fox'},
 {'score': 5.96074532950297e-05, 'label': 'bear'}]

Zero-shot image classification by hand
Now that you've seen how to use the zero-shot image classification pipeline, let's take a look how you can run zero-shot
image classification manually.
Start by loading the model and associated processor from a checkpoint on the Hugging Face Hub.
Here we'll use the same checkpoint as before:

from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
model = AutoModelForZeroShotImageClassification.from_pretrained(checkpoint)
processor = AutoProcessor.from_pretrained(checkpoint)

Let's take a different image to switch things up.

from PIL import Image
import requests
url = ""https://unsplash.com/photos/xBRQfR2bqNI/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjc4Mzg4ODEx&force=true&w=640""
image = Image.open(requests.get(url, stream=True).raw)
image

Use the processor to prepare the inputs for the model. The processor combines an image processor that prepares the
image for the model by resizing and normalizing it, and a tokenizer that takes care of the text inputs.

candidate_labels = [""tree"", ""car"", ""bike"", ""cat""]
inputs = processor(images=image, text=candidate_labels, return_tensors=""pt"", padding=True)

Pass the inputs through the model, and post-process the results:

import torch
with torch.no_grad():
     outputs = model(**inputs)
logits = outputs.logits_per_image[0]
probs = logits.softmax(dim=-1).numpy()
scores = probs.tolist()
result = [
     {""score"": score, ""label"": candidate_label}
     for score, candidate_label in sorted(zip(probs, candidate_labels), key=lambda x: -x[0])
 ]
result
[{'score': 0.998572, 'label': 'car'},
 {'score': 0.0010570387, 'label': 'bike'},
 {'score': 0.0003393686, 'label': 'tree'},
 {'score': 3.1572064e-05, 'label': 'cat'}]

"
"
Utilities for FeatureExtractors
This page lists all the utility functions that can be used by the audio [FeatureExtractor] in order to compute special features from a raw audio using common algorithms such as Short Time Fourier Transform or log mel spectrogram.
Most of those are only useful if you are studying the code of the audio processors in the library.
Audio Transformations
[[autodoc]] audio_utils.hertz_to_mel
[[autodoc]] audio_utils.mel_to_hertz
[[autodoc]] audio_utils.mel_filter_bank
[[autodoc]] audio_utils.optimal_fft_length
[[autodoc]] audio_utils.window_function
[[autodoc]] audio_utils.spectrogram
[[autodoc]] audio_utils.power_to_db
[[autodoc]] audio_utils.amplitude_to_db"
"
Transformers Agent

Transformers Agent is an experimental API which is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.

Transformers version v4.29.0, building on the concept of tools and agents. You can play with in
this colab.
In short, it provides a natural language API on top of transformers: we define a set of curated tools and design an 
agent to interpret natural language and to use these tools. It is extensible by design; we curated some relevant tools, 
but we'll show you how the system can be extended easily to use any tool developed by the community.
Let's start with a few examples of what can be achieved with this new API. It is particularly powerful when it comes 
to multimodal tasks, so let's take it for a spin to generate images and read text out loud.
py
agent.run(""Caption the following image"", image=image)
| Input                                                                                                                   | Output                        |
|-----------------------------------------------------------------------------------------------------------------------------|-----------------------------------|
|  | A beaver is swimming in the water |

py
agent.run(""Read the following text out loud"", text=text)
| Input                                                                                                               | Output                                   |
|-------------------------------------------------------------------------------------------------------------------------|----------------------------------------------|
| A beaver is swimming in the water |  your browser does not support the audio element. 

py
agent.run(
    ""In the following `document`, where will the TRRF Scientific Advisory Council Meeting take place?"",
    document=document,
)
| Input                                                                                                                   | Output     |
|-----------------------------------------------------------------------------------------------------------------------------|----------------|
|  | ballroom foyer |
Quickstart
Before being able to use agent.run, you will need to instantiate an agent, which is a large language model (LLM). 
We provide support for openAI models as well as opensource alternatives from BigCode and OpenAssistant. The openAI
models perform better (but require you to have an openAI API key, so cannot be used for free); Hugging Face is
providing free access to endpoints for BigCode and OpenAssistant models.
To start with, please install the agents extras in order to install all default dependencies.

pip install transformers[agents]
To use openAI models, you instantiate an [OpenAiAgent] after installing the openai dependency:

pip install openai

from transformers import OpenAiAgent
agent = OpenAiAgent(model=""text-davinci-003"", api_key="""")

To use BigCode or OpenAssistant, start by logging in to have access to the Inference API:

from huggingface_hub import login
login("""")

Then, instantiate the agent

from transformers import HfAgent
Starcoder
agent = HfAgent(""https://api-inference.huggingface.co/models/bigcode/starcoder"")
StarcoderBase
agent = HfAgent(""https://api-inference.huggingface.co/models/bigcode/starcoderbase"")
OpenAssistant
agent = HfAgent(url_endpoint=""https://api-inference.huggingface.co/models/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"")

This is using the inference API that Hugging Face provides for free at the moment. If you have your own inference
endpoint for this model (or another one) you can replace the URL above with your URL endpoint.

StarCoder and OpenAssistant are free to use and perform admirably well on simple tasks. However, the checkpoints
don't hold up when handling more complex prompts. If you're facing such an issue, we recommend trying out the OpenAI
model which, while sadly not open-source, performs better at this given time.

You're now good to go! Let's dive into the two APIs that you now have at your disposal.
Single execution (run)
The single execution method is when using the [~Agent.run] method of the agent:
py
agent.run(""Draw me a picture of rivers and lakes."")

It automatically selects the tool (or tools) appropriate for the task you want to perform and runs them appropriately. It
can perform one or several tasks in the same instruction (though the more complex your instruction, the more likely
the agent is to fail).
py
agent.run(""Draw me a picture of the sea then transform the picture to add an island"")

Every [~Agent.run] operation is independent, so you can run it several times in a row with different tasks.
Note that your agent is just a large-language model, so small variations in your prompt might yield completely
different results. It's important to explain as clearly as possible the task you want to perform. We go more in-depth
on how to write good prompts here.
If you'd like to keep a state across executions or to pass non-text objects to the agent, you can do so by specifying
variables that you would like the agent to use. For example, you could generate the first image of rivers and lakes, 
and ask the model to update that picture to add an island by doing the following:
python
picture = agent.run(""Generate a picture of rivers and lakes."")
updated_picture = agent.run(""Transform the image in `picture` to add an island to it."", picture=picture)

This can be helpful when the model is unable to understand your request and mixes tools. An example would be:
py
agent.run(""Draw me the picture of a capybara swimming in the sea"")
Here, the model could interpret in two ways:
- Have the text-to-image generate a capybara swimming in the sea
- Or, have the text-to-image generate capybara, then use the image-transformation tool to have it swim in the sea
In case you would like to force the first scenario, you could do so by passing it the prompt as an argument:
py
agent.run(""Draw me a picture of the `prompt`"", prompt=""a capybara swimming in the sea"")

Chat-based execution (chat)
The agent also has a chat-based approach, using the [~Agent.chat] method:
py
agent.chat(""Generate a picture of rivers and lakes"")
 
py
agent.chat(""Transform the picture so that there is a rock in there"")

This is an interesting approach when you want to keep the state across instructions. It's better for experimentation, 
but will tend to be much better at single instructions rather than complex instructions (which the [~Agent.run]
method is better at handling).
This method can also take arguments if you would like to pass non-text types or specific prompts.
⚠️ Remote execution
For demonstration purposes and so that this can be used with all setups, we have created remote executors for several 
of the default tools the agent has access. These are created using 
inference endpoints. To see how to set up remote executors tools yourself,
we recommend reading the custom tool guide.
In order to run with remote tools, specifying remote=True to either [~Agent.run] or [~Agent.chat] is sufficient.
For example, the following command could be run on any device efficiently, without needing significant RAM or GPU:
py
agent.run(""Draw me a picture of rivers and lakes"", remote=True)
The same can be said for [~Agent.chat]:
py
agent.chat(""Draw me a picture of rivers and lakes"", remote=True)
What's happening here? What are tools, and what are agents?

Agents
The ""agent"" here is a large language model, and we're prompting it so that it has access to a specific set of tools.
LLMs are pretty good at generating small samples of code, so this API takes advantage of that by prompting the 
LLM gives a small sample of code performing a task with a set of tools. This prompt is then completed by the 
task you give your agent and the description of the tools you give it. This way it gets access to the doc of the 
tools you are using, especially their expected inputs and outputs, and can generate the relevant code.
Tools
Tools are very simple: they're a single function, with a name, and a description. We then use these tools' descriptions 
to prompt the agent. Through the prompt, we show the agent how it would leverage tools to perform what was 
requested in the query.
This is using brand-new tools and not pipelines, because the agent writes better code with very atomic tools. 
Pipelines are more refactored and often combine several tasks in one. Tools are meant to be focused on
one very simple task only.
Code-execution?!
This code is then executed with our small Python interpreter on the set of inputs passed along with your tools. 
We hear you screaming ""Arbitrary code execution!"" in the back, but let us explain why that is not the case.
The only functions that can be called are the tools you provided and the print function, so you're already 
limited in what can be executed. You should be safe if it's limited to Hugging Face tools. 
Then, we don't allow any attribute lookup or imports (which shouldn't be needed anyway for passing along 
inputs/outputs to a small set of functions) so all the most obvious attacks (and you'd need to prompt the LLM 
to output them anyway) shouldn't be an issue. If you want to be on the super safe side, you can execute the 
run() method with the additional argument return_code=True, in which case the agent will just return the code 
to execute and you can decide whether to do it or not.
The execution will stop at any line trying to perform an illegal operation or if there is a regular Python error 
with the code generated by the agent.
A curated set of tools
We identify a set of tools that can empower such agents. Here is an updated list of the tools we have integrated 
in transformers:

Document question answering: given a document (such as a PDF) in image format, answer a question on this document (Donut)
Text question answering: given a long text and a question, answer the question in the text (Flan-T5)
Unconditional image captioning: Caption the image! (BLIP)
Image question answering: given an image, answer a question on this image (VILT)
Image segmentation: given an image and a prompt, output the segmentation mask of that prompt (CLIPSeg)
Speech to text: given an audio recording of a person talking, transcribe the speech into text (Whisper)
Text to speech: convert text to speech (SpeechT5)
Zero-shot text classification: given a text and a list of labels, identify to which label the text corresponds the most (BART)
Text summarization: summarize a long text in one or a few sentences (BART)
Translation: translate the text into a given language (NLLB)

These tools have an integration in transformers, and can be used manually as well, for example:

from transformers import load_tool
tool = load_tool(""text-to-speech"")
audio = tool(""This is a text to speech tool"")

Custom tools
While we identify a curated set of tools, we strongly believe that the main value provided by this implementation is 
the ability to quickly create and share custom tools.
By pushing the code of a tool to a Hugging Face Space or a model repository, you're then able to leverage the tool 
directly with the agent. We've added a few 
transformers-agnostic tools to the huggingface-tools organization:

Text downloader: to download a text from a web URL
Text to image: generate an image according to a prompt, leveraging stable diffusion
Image transformation: modify an image given an initial image and a prompt, leveraging instruct pix2pix stable diffusion
Text to video: generate a small video according to a prompt, leveraging damo-vilab

The text-to-image tool we have been using since the beginning is a remote tool that lives in 
huggingface-tools/text-to-image! We will
continue releasing such tools on this and other organizations, to further supercharge this implementation.
The agents have by default access to tools that reside on huggingface-tools.
We explain how to you can write and share your tools as well as leverage any custom tool that resides on the Hub in following guide.
Code generation
So far we have shown how to use the agents to perform actions for you. However, the agent is only generating code
that we then execute using a very restricted Python interpreter. In case you would like to use the code generated in 
a different setting, the agent can be prompted to return the code, along with tool definition and accurate imports.
For example, the following instruction
python
agent.run(""Draw me a picture of rivers and lakes"", return_code=True)
returns the following code
thon
from transformers import load_tool
image_generator = load_tool(""huggingface-tools/text-to-image"")
image = image_generator(prompt=""rivers and lakes"")

that you can then modify and execute yourself."
"
Use tokenizers from 🤗 Tokenizers
The [PreTrainedTokenizerFast] depends on the 🤗 Tokenizers library. The tokenizers obtained from the 🤗 Tokenizers library can be
loaded very simply into 🤗 Transformers.
Before getting in the specifics, let's first start by creating a dummy tokenizer in a few lines:
thon

from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from tokenizers.pre_tokenizers import Whitespace
tokenizer = Tokenizer(BPE(unk_token=""[UNK]""))
trainer = BpeTrainer(special_tokens=[""[UNK]"", ""[CLS]"", ""[SEP]"", ""[PAD]"", ""[MASK]""])
tokenizer.pre_tokenizer = Whitespace()
files = []
tokenizer.train(files, trainer)

We now have a tokenizer trained on the files we defined. We can either continue using it in that runtime, or save it to
a JSON file for future re-use.
Loading directly from the tokenizer object
Let's see how to leverage this tokenizer object in the 🤗 Transformers library. The
[PreTrainedTokenizerFast] class allows for easy instantiation, by accepting the instantiated
tokenizer object as an argument:
thon

from transformers import PreTrainedTokenizerFast
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)

This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to the tokenizer
page for more information.
Loading from a JSON file
In order to load a tokenizer from a JSON file, let's first start by saving our tokenizer:
thon

tokenizer.save(""tokenizer.json"")

The path to which we saved this file can be passed to the [PreTrainedTokenizerFast] initialization
method using the tokenizer_file parameter:
thon

from transformers import PreTrainedTokenizerFast
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file=""tokenizer.json"")

This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to the tokenizer
page for more information."
"
Perplexity of fixed-length models
[[open-in-colab]]
Perplexity (PPL) is one of the most common metrics for evaluating language models. Before diving in, we should note
that the metric applies specifically to classical language models (sometimes called autoregressive or causal language
models) and is not well defined for masked language models like BERT (see summary of the models).
Perplexity is defined as the exponentiated average negative log-likelihood of a sequence. If we have a tokenized
sequence \(X = (x_0, x_1, \dots, x_t)\), then the perplexity of \(X\) is,
$$\text{PPL}(X) = \exp \left{ {-\frac{1}{t}\sum_i^t \log p_\theta (x_i|x_{<i}) } \right}$$
where \(\log p_\theta (x_i|x_{<i})\) is the log-likelihood of the ith token conditioned on the preceding tokens \(x_{<i}\) according to our model. Intuitively, it can be thought of as an evaluation of the model's ability to predict uniformly among the set of specified tokens in a corpus. Importantly, this means that the tokenization procedure has a direct impact on a model's perplexity which should always be taken into consideration when comparing different models.
This is also equivalent to the exponentiation of the cross-entropy between the data and model predictions. For more
intuition about perplexity and its relationship to Bits Per Character (BPC) and data compression, check out this
fantastic blog post on The Gradient.
Calculating PPL with fixed-length models
If we weren't limited by a model's context size, we would evaluate the model's perplexity by autoregressively
factorizing a sequence and conditioning on the entire preceding subsequence at each step, as shown below.

When working with approximate models, however, we typically have a constraint on the number of tokens the model can
process. The largest version of GPT-2, for example, has a fixed length of 1024 tokens, so we
cannot calculate \(p_\theta(x_t|x_{<t})\) directly when \(t\) is greater than 1024.
Instead, the sequence is typically broken into subsequences equal to the model's maximum input size. If a model's max
input size is \(k\), we then approximate the likelihood of a token \(x_t\) by conditioning only on the
\(k-1\) tokens that precede it rather than the entire context. When evaluating the model's perplexity of a
sequence, a tempting but suboptimal approach is to break the sequence into disjoint chunks and add up the decomposed
log-likelihoods of each segment independently.

This is quick to compute since the perplexity of each segment can be computed in one forward pass, but serves as a poor
approximation of the fully-factorized perplexity and will typically yield a higher (worse) PPL because the model will
have less context at most of the prediction steps.
Instead, the PPL of fixed-length models should be evaluated with a sliding-window strategy. This involves repeatedly
sliding the context window so that the model has more context when making each prediction.

This is a closer approximation to the true decomposition of the sequence probability and will typically yield a more
favorable score. The downside is that it requires a separate forward pass for each token in the corpus. A good
practical compromise is to employ a strided sliding window, moving the context by larger strides rather than sliding by
1 token a time. This allows computation to proceed much faster while still giving the model a large context to make
predictions at each step.
Example: Calculating perplexity with GPT-2 in 🤗 Transformers
Let's demonstrate this process with GPT-2.
thon
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
device = ""cuda""
model_id = ""gpt2-large""
model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
tokenizer = GPT2TokenizerFast.from_pretrained(model_id)

We'll load in the WikiText-2 dataset and evaluate the perplexity using a few different sliding-window strategies. Since
this dataset is small and we're just doing one forward pass over the set, we can just load and encode the entire
dataset in memory.
thon
from datasets import load_dataset
test = load_dataset(""wikitext"", ""wikitext-2-raw-v1"", split=""test"")
encodings = tokenizer(""\n\n"".join(test[""text""]), return_tensors=""pt"")

With 🤗 Transformers, we can simply pass the input_ids as the labels to our model, and the average negative
log-likelihood for each token is returned as the loss. With our sliding window approach, however, there is overlap in
the tokens we pass to the model at each iteration. We don't want the log-likelihood for the tokens we're just treating
as context to be included in our loss, so we can set these targets to -100 so that they are ignored. The following
is an example of how we could do this with a stride of 512. This means that the model will have at least 512 tokens
for context when calculating the conditional likelihood of any one token (provided there are 512 preceding tokens
available to condition on).
thon
import torch
from tqdm import tqdm
max_length = model.config.n_positions
stride = 512
seq_len = encodings.input_ids.size(1)
nlls = []
prev_end_loc = 0
for begin_loc in tqdm(range(0, seq_len, stride)):
    end_loc = min(begin_loc + max_length, seq_len)
    trg_len = end_loc - prev_end_loc  # may be different from stride on last loop
    input_ids = encodings.input_ids[:, begin_loc:end_loc].to(device)
    target_ids = input_ids.clone()
    target_ids[:, :-trg_len] = -100
with torch.no_grad():
    outputs = model(input_ids, labels=target_ids)

    # loss is calculated using CrossEntropyLoss which averages over valid labels
    # N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels
    # to the left by 1.
    neg_log_likelihood = outputs.loss

nlls.append(neg_log_likelihood)

prev_end_loc = end_loc
if end_loc == seq_len:
    break

ppl = torch.exp(torch.stack(nlls).mean())

Running this with the stride length equal to the max input length is equivalent to the suboptimal, non-sliding-window
strategy we discussed above. The smaller the stride, the more context the model will have in making each prediction,
and the better the reported perplexity will typically be.
When we run the above with stride = 1024, i.e. no overlap, the resulting PPL is 19.44, which is about the same
as the 19.93 reported in the GPT-2 paper. By using stride = 512 and thereby employing our striding window
strategy, this jumps down to 16.45. This is not only a more favorable score, but is calculated in a way that is
closer to the true autoregressive decomposition of a sequence likelihood."
"

Before you begin, make sure you have all the necessary libraries installed:

pip install -q datasets transformers evaluate
We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:

from huggingface_hub import notebook_login
notebook_login()

Load SceneParse150 dataset
Start by loading a smaller subset of the SceneParse150 dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.

from datasets import load_dataset
ds = load_dataset(""scene_parse_150"", split=""train[:50]"")

Split the dataset's train split into a train and test set with the [~datasets.Dataset.train_test_split] method:

ds = ds.train_test_split(test_size=0.2)
train_ds = ds[""train""]
test_ds = ds[""test""]

Then take a look at an example:

train_ds[0]
{'image': ,
 'annotation': ,
 'scene_category': 368}

image: a PIL image of the scene.
annotation: a PIL image of the segmentation map, which is also the model's target.
scene_category: a category id that describes the image scene like ""kitchen"" or ""office"". In this guide, you'll only need image and annotation, both of which are PIL images.

You'll also want to create a dictionary that maps a label id to a label class which will be useful when you set up the model later. Download the mappings from the Hub and create the id2label and label2id dictionaries:

import json
from huggingface_hub import cached_download, hf_hub_url
repo_id = ""huggingface/label-files""
filename = ""ade20k-id2label.json""
id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type=""dataset"")), ""r""))
id2label = {int(k): v for k, v in id2label.items()}
label2id = {v: k for k, v in id2label.items()}
num_labels = len(id2label)

Preprocess
The next step is to load a SegFormer image processor to prepare the images and annotations for the model. Some datasets, like this one, use the zero-index as the background class. However, the background class isn't actually included in the 150 classes, so you'll need to set reduce_labels=True to subtract one from all the labels. The zero-index is replaced by 255 so it's ignored by SegFormer's loss function:

from transformers import AutoImageProcessor
checkpoint = ""nvidia/mit-b0""
image_processor = AutoImageProcessor.from_pretrained(checkpoint, reduce_labels=True)

It is common to apply some data augmentations to an image dataset to make a model more robust against overfitting. In this guide, you'll use the ColorJitter function from torchvision to randomly change the color properties of an image, but you can also use any image library you like.

from torchvision.transforms import ColorJitter
jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)

Now create two preprocessing functions to prepare the images and annotations for the model. These functions convert the images into pixel_values and annotations to labels. For the training set, jitter is applied before providing the images to the image processor. For the test set, the image processor crops and normalizes the images, and only crops the labels because no data augmentation is applied during testing.

def train_transforms(example_batch):
     images = [jitter(x) for x in example_batch[""image""]]
     labels = [x for x in example_batch[""annotation""]]
     inputs = image_processor(images, labels)
     return inputs
def val_transforms(example_batch):
     images = [x for x in example_batch[""image""]]
     labels = [x for x in example_batch[""annotation""]]
     inputs = image_processor(images, labels)
     return inputs

To apply the jitter over the entire dataset, use the 🤗 Datasets [~datasets.Dataset.set_transform] function. The transform is applied on the fly which is faster and consumes less disk space:

train_ds.set_transform(train_transforms)
test_ds.set_transform(val_transforms)

It is common to apply some data augmentations to an image dataset to make a model more robust against overfitting.
In this guide, you'll use tf.image to randomly change the color properties of an image, but you can also use any image
library you like.
Define two separate transformation functions:
- training data transformations that include image augmentation
- validation data transformations that only transpose the images, since computer vision models in 🤗 Transformers expect channels-first layout

import tensorflow as tf
def aug_transforms(image):
     image = tf.keras.utils.img_to_array(image)
     image = tf.image.random_brightness(image, 0.25)
     image = tf.image.random_contrast(image, 0.5, 2.0)
     image = tf.image.random_saturation(image, 0.75, 1.25)
     image = tf.image.random_hue(image, 0.1)
     image = tf.transpose(image, (2, 0, 1))
     return image
def transforms(image):
     image = tf.keras.utils.img_to_array(image)
     image = tf.transpose(image, (2, 0, 1))
     return image

Next, create two preprocessing functions to prepare batches of images and annotations for the model. These functions apply
the image transformations and use the earlier loaded image_processor to convert the images into pixel_values and
annotations to labels. ImageProcessor also takes care of resizing and normalizing the images.

def train_transforms(example_batch):
     images = [aug_transforms(x.convert(""RGB"")) for x in example_batch[""image""]]
     labels = [x for x in example_batch[""annotation""]]
     inputs = image_processor(images, labels)
     return inputs
def val_transforms(example_batch):
     images = [transforms(x.convert(""RGB"")) for x in example_batch[""image""]]
     labels = [x for x in example_batch[""annotation""]]
     inputs = image_processor(images, labels)
     return inputs

To apply the preprocessing transformations over the entire dataset, use the 🤗 Datasets [~datasets.Dataset.set_transform] function.
The transform is applied on the fly which is faster and consumes less disk space:

train_ds.set_transform(train_transforms)
test_ds.set_transform(val_transforms)

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the mean Intersection over Union (IoU) metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
metric = evaluate.load(""mean_iou"")

Then create a function to [~evaluate.EvaluationModule.compute] the metrics. Your predictions need to be converted to
logits first, and then reshaped to match the size of the labels before you can call [~evaluate.EvaluationModule.compute]:

def compute_metrics(eval_pred):
     with torch.no_grad():
         logits, labels = eval_pred
         logits_tensor = torch.from_numpy(logits)
         logits_tensor = nn.functional.interpolate(
             logits_tensor,
             size=labels.shape[-2:],
             mode=""bilinear"",
             align_corners=False,
         ).argmax(dim=1)

         pred_labels = logits_tensor.detach().cpu().numpy()
         metrics = metric.compute(
             predictions=pred_labels,
             references=labels,
             num_labels=num_labels,
             ignore_index=255,
             reduce_labels=False,
         )
         for key, value in metrics.items():
             if type(value) is np.ndarray:
                 metrics[key] = value.tolist()
         return metrics

def compute_metrics(eval_pred):
     logits, labels = eval_pred
     logits = tf.transpose(logits, perm=[0, 2, 3, 1])
     logits_resized = tf.image.resize(
         logits,
         size=tf.shape(labels)[1:],
         method=""bilinear"",
     )

     pred_labels = tf.argmax(logits_resized, axis=-1)
     metrics = metric.compute(
         predictions=pred_labels,
         references=labels,
         num_labels=num_labels,
         ignore_index=-1,
         reduce_labels=image_processor.do_reduce_labels,
     )
     per_category_accuracy = metrics.pop(""per_category_accuracy"").tolist()
     per_category_iou = metrics.pop(""per_category_iou"").tolist()
     metrics.update({f""accuracy_{id2label[i]}"": v for i, v in enumerate(per_category_accuracy)})
     metrics.update({f""iou_{id2label[i]}"": v for i, v in enumerate(per_category_iou)})
     return {""val_"" + k: v for k, v in metrics.items()}

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load SegFormer with [AutoModelForSemanticSegmentation], and pass the model the mapping between label ids and label classes:

from transformers import AutoModelForSemanticSegmentation, TrainingArguments, Trainer
model = AutoModelForSemanticSegmentation.from_pretrained(checkpoint, id2label=id2label, label2id=label2id)

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. It is important you don't remove unused columns because this'll drop the image column. Without the image column, you can't create pixel_values. Set remove_unused_columns=False to prevent this behavior! The only other required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the IoU metric and save the training checkpoint.
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""segformer-b0-scene-parse-150"",
     learning_rate=6e-5,
     num_train_epochs=50,
     per_device_train_batch_size=2,
     per_device_eval_batch_size=2,
     save_total_limit=3,
     evaluation_strategy=""steps"",
     save_strategy=""steps"",
     save_steps=20,
     eval_steps=20,
     logging_steps=1,
     eval_accumulation_steps=5,
     remove_unused_columns=False,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=train_ds,
     eval_dataset=test_ds,
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you are unfamiliar with fine-tuning a model with Keras, check out the basic tutorial first!

To fine-tune a model in TensorFlow, follow these steps:
1. Define the training hyperparameters, and set up an optimizer and a learning rate schedule.
2. Instantiate a pretrained model.
3. Convert a 🤗 Dataset to a tf.data.Dataset.
4. Compile your model.
5. Add callbacks to calculate metrics and upload your model to 🤗 Hub
6. Use the fit() method to run the training.
Start by defining the hyperparameters, optimizer and learning rate schedule:

from transformers import create_optimizer
batch_size = 2
num_epochs = 50
num_train_steps = len(train_ds) * num_epochs
learning_rate = 6e-5
weight_decay_rate = 0.01
optimizer, lr_schedule = create_optimizer(
     init_lr=learning_rate,
     num_train_steps=num_train_steps,
     weight_decay_rate=weight_decay_rate,
     num_warmup_steps=0,
 )

Then, load SegFormer with [TFAutoModelForSemanticSegmentation] along with the label mappings, and compile it with the
optimizer. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

from transformers import TFAutoModelForSemanticSegmentation
model = TFAutoModelForSemanticSegmentation.from_pretrained(
     checkpoint,
     id2label=id2label,
     label2id=label2id,
 )
model.compile(optimizer=optimizer)  # No loss argument!

Convert your datasets to the tf.data.Dataset format using the [~datasets.Dataset.to_tf_dataset] and the [DefaultDataCollator]:

from transformers import DefaultDataCollator
data_collator = DefaultDataCollator(return_tensors=""tf"")
tf_train_dataset = train_ds.to_tf_dataset(
     columns=[""pixel_values"", ""label""],
     shuffle=True,
     batch_size=batch_size,
     collate_fn=data_collator,
 )
tf_eval_dataset = test_ds.to_tf_dataset(
     columns=[""pixel_values"", ""label""],
     shuffle=True,
     batch_size=batch_size,
     collate_fn=data_collator,
 )

To compute the accuracy from the predictions and push your model to the 🤗 Hub, use Keras callbacks.
Pass your compute_metrics function to [KerasMetricCallback],
and use the [PushToHubCallback] to upload the model:

from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback
metric_callback = KerasMetricCallback(
     metric_fn=compute_metrics, eval_dataset=tf_eval_dataset, batch_size=batch_size, label_cols=[""labels""]
 )
push_to_hub_callback = PushToHubCallback(output_dir=""scene_segmentation"", tokenizer=image_processor)
callbacks = [metric_callback, push_to_hub_callback]

Finally, you are ready to train your model! Call fit() with your training and validation datasets, the number of epochs,
and your callbacks to fine-tune the model:

model.fit(
     tf_train_dataset,
     validation_data=tf_eval_dataset,
     callbacks=callbacks,
     epochs=num_epochs,
 )

Congratulations! You have fine-tuned your model and shared it on the 🤗 Hub. You can now use it for inference!

Inference
Great, now that you've finetuned a model, you can use it for inference!
Load an image for inference:

image = ds[0][""image""]
image

The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for image segmentation with your model, and pass your image to it:

from transformers import pipeline
segmenter = pipeline(""image-segmentation"", model=""my_awesome_seg_model"")
segmenter(image)
[{'score': None,
  'label': 'wall',
  'mask': },
 {'score': None,
  'label': 'sky',
  'mask': },
 {'score': None,
  'label': 'floor',
  'mask': },
 {'score': None,
  'label': 'ceiling',
  'mask': },
 {'score': None,
  'label': 'bed ',
  'mask': },
 {'score': None,
  'label': 'windowpane',
  'mask': },
 {'score': None,
  'label': 'cabinet',
  'mask': },
 {'score': None,
  'label': 'chair',
  'mask': },
 {'score': None,
  'label': 'armchair',
  'mask': }]

You can also manually replicate the results of the pipeline if you'd like. Process the image with an image processor and place the pixel_values on a GPU:

device = torch.device(""cuda"" if torch.cuda.is_available() else ""cpu"")  # use GPU if available, otherwise use a CPU
encoding = image_processor(image, return_tensors=""pt"")
pixel_values = encoding.pixel_values.to(device)

Pass your input to the model and return the logits:

outputs = model(pixel_values=pixel_values)
logits = outputs.logits.cpu()

Next, rescale the logits to the original image size:

upsampled_logits = nn.functional.interpolate(
     logits,
     size=image.size[::-1],
     mode=""bilinear"",
     align_corners=False,
 )
pred_seg = upsampled_logits.argmax(dim=1)[0]

Load an image processor to preprocess the image and return the input as TensorFlow tensors:

from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained(""MariaK/scene_segmentation"")
inputs = image_processor(image, return_tensors=""tf"")

Pass your input to the model and return the logits:

from transformers import TFAutoModelForSemanticSegmentation
model = TFAutoModelForSemanticSegmentation.from_pretrained(""MariaK/scene_segmentation"")
logits = model(**inputs).logits

Next, rescale the logits to the original image size and apply argmax on the class dimension:

logits = tf.transpose(logits, [0, 2, 3, 1])
upsampled_logits = tf.image.resize(
     logits,
     # We reverse the shape of image because image.size returns width and height.
     image.size[::-1],
 )
pred_seg = tf.math.argmax(upsampled_logits, axis=-1)[0]

To visualize the results, load the dataset color palette as ade_palette() that maps each class to their RGB values. Then you can combine and plot your image and the predicted segmentation map:

import matplotlib.pyplot as plt
import numpy as np
color_seg = np.zeros((pred_seg.shape[0], pred_seg.shape[1], 3), dtype=np.uint8)
palette = np.array(ade_palette())
for label, color in enumerate(palette):
     color_seg[pred_seg == label, :] = color
color_seg = color_seg[, ::-1]  # convert to BGR
img = np.array(image) * 0.5 + color_seg * 0.5  # plot the image with the segmentation map
img = img.astype(np.uint8)
plt.figure(figsize=(15, 10))
plt.imshow(img)
plt.show()

"
"

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

from huggingface_hub import notebook_login
notebook_login()

Load SWAG dataset
Start by loading the regular configuration of the SWAG dataset from the 🤗 Datasets library:

from datasets import load_dataset
swag = load_dataset(""swag"", ""regular"")

Then take a look at an example:

swag[""train""][0]
{'ending0': 'passes by walking down the street playing their instruments.',
 'ending1': 'has heard approaching them.',
 'ending2': ""arrives and they're outside dancing and asleep."",
 'ending3': 'turns the lead singer watches the performance.',
 'fold-ind': '3416',
 'gold-source': 'gold',
 'label': 0,
 'sent1': 'Members of the procession walk down the street holding small horn brass instruments.',
 'sent2': 'A drum line',
 'startphrase': 'Members of the procession walk down the street holding small horn brass instruments. A drum line',
 'video-id': 'anetv_jkn6uvmqwh4'}

While it looks like there are a lot of fields here, it is actually pretty straightforward:

sent1 and sent2: these fields show how a sentence starts, and if you put the two together, you get the startphrase field.
ending: suggests a possible ending for how a sentence can end, but only one of them is correct.
label: identifies the correct sentence ending.

Preprocess
The next step is to load a BERT tokenizer to process the sentence starts and the four possible endings:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""bert-base-uncased"")

The preprocessing function you want to create needs to:

Make four copies of the sent1 field and combine each of them with sent2 to recreate how a sentence starts.
Combine sent2 with each of the four possible sentence endings.
Flatten these two lists so you can tokenize them, and then unflatten them afterward so each example has a corresponding input_ids, attention_mask, and labels field.

ending_names = [""ending0"", ""ending1"", ""ending2"", ""ending3""]
def preprocess_function(examples):
     first_sentences = [[context] * 4 for context in examples[""sent1""]]
     question_headers = examples[""sent2""]
     second_sentences = [
         [f""{header} {examples[end][i]}"" for end in ending_names] for i, header in enumerate(question_headers)
     ]

     first_sentences = sum(first_sentences, [])
     second_sentences = sum(second_sentences, [])
     tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True)
     return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once:
py
tokenized_swag = swag.map(preprocess_function, batched=True)
🤗 Transformers doesn't have a data collator for multiple choice, so you'll need to adapt the [DataCollatorWithPadding] to create a batch of examples. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.
DataCollatorForMultipleChoice flattens all the model inputs, applies padding, and then unflattens the results:

from dataclasses import dataclass
from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
from typing import Optional, Union
import torch
@dataclass
 class DataCollatorForMultipleChoice:
     """"""
     Data collator that will dynamically pad the inputs for multiple choice received.
     """"""

     tokenizer: PreTrainedTokenizerBase
     padding: Union[bool, str, PaddingStrategy] = True
     max_length: Optional[int] = None
     pad_to_multiple_of: Optional[int] = None
     def call(self, features):
         label_name = ""label"" if ""label"" in features[0].keys() else ""labels""
         labels = [feature.pop(label_name) for feature in features]
         batch_size = len(features)
         num_choices = len(features[0][""input_ids""])
         flattened_features = [
             [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
         ]
         flattened_features = sum(flattened_features, [])
         batch = self.tokenizer.pad(
             flattened_features,
             padding=self.padding,
             max_length=self.max_length,
             pad_to_multiple_of=self.pad_to_multiple_of,
             return_tensors=""pt"",
         )
         batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
         batch[""labels""] = torch.tensor(labels, dtype=torch.int64)
         return batch
</pt>
<tf>py

from dataclasses import dataclass
from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
from typing import Optional, Union
import tensorflow as tf
@dataclass
 class DataCollatorForMultipleChoice:
     """"""
     Data collator that will dynamically pad the inputs for multiple choice received.
     """"""

     tokenizer: PreTrainedTokenizerBase
     padding: Union[bool, str, PaddingStrategy] = True
     max_length: Optional[int] = None
     pad_to_multiple_of: Optional[int] = None
     def call(self, features):
         label_name = ""label"" if ""label"" in features[0].keys() else ""labels""
         labels = [feature.pop(label_name) for feature in features]
         batch_size = len(features)
         num_choices = len(features[0][""input_ids""])
         flattened_features = [
             [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
         ]
         flattened_features = sum(flattened_features, [])
         batch = self.tokenizer.pad(
             flattened_features,
             padding=self.padding,
             max_length=self.max_length,
             pad_to_multiple_of=self.pad_to_multiple_of,
             return_tensors=""tf"",
         )
         batch = {k: tf.reshape(v, (batch_size, num_choices, -1)) for k, v in batch.items()}
         batch[""labels""] = tf.convert_to_tensor(labels, dtype=tf.int64)
         return batch

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
accuracy = evaluate.load(""accuracy"")

Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the accuracy:

import numpy as np
def compute_metrics(eval_pred):
     predictions, labels = eval_pred
     predictions = np.argmax(predictions, axis=1)
     return accuracy.compute(predictions=predictions, references=labels)

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load BERT with [AutoModelForMultipleChoice]:

from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
model = AutoModelForMultipleChoice.from_pretrained(""bert-base-uncased"")

At this point, only three steps remain:

Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the accuracy and save the training checkpoint.
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir=""my_awesome_swag_model"",
     evaluation_strategy=""epoch"",
     save_strategy=""epoch"",
     load_best_model_at_end=True,
     learning_rate=5e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
     num_train_epochs=3,
     weight_decay=0.01,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_swag[""train""],
     eval_dataset=tokenized_swag[""validation""],
     tokenizer=tokenizer,
     data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer
batch_size = 16
num_train_epochs = 2
total_train_steps = (len(tokenized_swag[""train""]) // batch_size) * num_train_epochs
optimizer, schedule = create_optimizer(init_lr=5e-5, num_warmup_steps=0, num_train_steps=total_train_steps)

Then you can load BERT with [TFAutoModelForMultipleChoice]:

from transformers import TFAutoModelForMultipleChoice
model = TFAutoModelForMultipleChoice.from_pretrained(""bert-base-uncased"")

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
tf_train_set = model.prepare_tf_dataset(
     tokenized_swag[""train""],
     shuffle=True,
     batch_size=batch_size,
     collate_fn=data_collator,
 )
tf_validation_set = model.prepare_tf_dataset(
     tokenized_swag[""validation""],
     shuffle=False,
     batch_size=batch_size,
     collate_fn=data_collator,
 )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

model.compile(optimizer=optimizer)  # No loss argument!

The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks.
Pass your compute_metrics function to [~transformers.KerasMetricCallback]:

from transformers.keras_callbacks import KerasMetricCallback
metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)

Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
     output_dir=""my_awesome_model"",
     tokenizer=tokenizer,
 )

Then bundle your callbacks together:

callbacks = [metric_callback, push_to_hub_callback]

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2, callbacks=callbacks)

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for multiple choice, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text and two candidate answers:

prompt = ""France has a bread law, Le Décret Pain, with strict rules on what is allowed in a traditional baguette.""
candidate1 = ""The law does not apply to croissants and brioche.""
candidate2 = ""The law applies to baguettes.""

Tokenize each prompt and candidate answer pair and return PyTorch tensors. You should also create some labels:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""my_awesome_swag_model"")
inputs = tokenizer([[prompt, candidate1], [prompt, candidate2]], return_tensors=""pt"", padding=True)
labels = torch.tensor(0).unsqueeze(0)

Pass your inputs and labels to the model and return the logits:

from transformers import AutoModelForMultipleChoice
model = AutoModelForMultipleChoice.from_pretrained(""my_awesome_swag_model"")
outputs = model(**{k: v.unsqueeze(0) for k, v in inputs.items()}, labels=labels)
logits = outputs.logits

Get the class with the highest probability:

predicted_class = logits.argmax().item()
predicted_class
'0'

Tokenize each prompt and candidate answer pair and return TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""my_awesome_swag_model"")
inputs = tokenizer([[prompt, candidate1], [prompt, candidate2]], return_tensors=""tf"", padding=True)

Pass your inputs to the model and return the logits:

from transformers import TFAutoModelForMultipleChoice
model = TFAutoModelForMultipleChoice.from_pretrained(""my_awesome_swag_model"")
inputs = {k: tf.expand_dims(v, 0) for k, v in inputs.items()}
outputs = model(inputs)
logits = outputs.logits

Get the class with the highest probability:

predicted_class = int(tf.math.argmax(logits, axis=-1)[0])
predicted_class
'0'

"
"
Utilities for pipelines
This page lists all the utility functions the library provides for pipelines.
Most of those are only useful if you are studying the code of the models in the library.
Argument handling
[[autodoc]] pipelines.ArgumentHandler
[[autodoc]] pipelines.ZeroShotClassificationArgumentHandler
[[autodoc]] pipelines.QuestionAnsweringArgumentHandler
Data format
[[autodoc]] pipelines.PipelineDataFormat
[[autodoc]] pipelines.CsvPipelineDataFormat
[[autodoc]] pipelines.JsonPipelineDataFormat
[[autodoc]] pipelines.PipedPipelineDataFormat
Utilities
[[autodoc]] pipelines.PipelineException"
"
Train with a script
Along with the 🤗 Transformers notebooks, there are also example scripts demonstrating how to train a model for a task with PyTorch, TensorFlow, or JAX/Flax.
You will also find scripts we've used in our research projects and legacy examples which are mostly community contributed. These scripts are not actively maintained and require a specific version of 🤗 Transformers that will most likely be incompatible with the latest version of the library.
The example scripts are not expected to work out-of-the-box on every problem, and you may need to adapt the script to the problem you're trying to solve. To help you with this, most of the scripts fully expose how data is preprocessed, allowing you to edit it as necessary for your use case.
For any feature you'd like to implement in an example script, please discuss it on the forum or in an issue before submitting a Pull Request. While we welcome bug fixes, it is unlikely we will merge a Pull Request that adds more functionality at the cost of readability.
This guide will show you how to run an example summarization training script in PyTorch and TensorFlow. All examples are expected to work with both frameworks unless otherwise specified.
Setup
To successfully run the latest version of the example scripts, you have to install 🤗 Transformers from source in a new virtual environment:

git clone https://github.com/huggingface/transformers
cd transformers
pip install .
For older versions of the example scripts, click on the toggle below:

Examples for older versions of 🤗 Transformers

v4.5.1
v4.4.2
v4.3.3
v4.2.2
v4.1.1
v4.0.1
v3.5.1
v3.4.0
v3.3.1
v3.2.0
v3.1.0
v3.0.2
v2.11.0
v2.10.0
v2.9.1
v2.8.0
v2.7.0
v2.6.0
v2.5.1
v2.4.0
v2.3.0
v2.2.0
v2.1.1
v2.0.0
v1.2.0
v1.1.0
v1.0.0

Then switch your current clone of 🤗 Transformers to a specific version, like v3.5.1 for example:

git checkout tags/v3.5.1
After you've setup the correct library version, navigate to the example folder of your choice and install the example specific requirements:

pip install -r requirements.txt
Run a script

The example script downloads and preprocesses a dataset from the 🤗 Datasets library. Then the script fine-tunes a dataset with the Trainer on an architecture that supports summarization. The following example shows how to fine-tune T5-small on the CNN/DailyMail dataset. The T5 model requires an additional source_prefix argument due to how it was trained. This prompt lets T5 know this is a summarization task.

python examples/pytorch/summarization/run_summarization.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --source_prefix ""summarize: "" \
    --output_dir /tmp/tst-summarization \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate

The example script downloads and preprocesses a dataset from the 🤗 Datasets library. Then the script fine-tunes a dataset using Keras on an architecture that supports summarization. The following example shows how to fine-tune T5-small on the CNN/DailyMail dataset. The T5 model requires an additional source_prefix argument due to how it was trained. This prompt lets T5 know this is a summarization task.

python examples/tensorflow/summarization/run_summarization.py  \
    --model_name_or_path t5-small \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --output_dir /tmp/tst-summarization  \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 16 \
    --num_train_epochs 3 \
    --do_train \
    --do_eval

Distributed training and mixed precision
The Trainer supports distributed training and mixed precision, which means you can also use it in a script. To enable both of these features:

Add the fp16 argument to enable mixed precision.
Set the number of GPUs to use with the nproc_per_node argument.

python -m torch.distributed.launch \
    --nproc_per_node 8 pytorch/summarization/run_summarization.py \
    --fp16 \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --source_prefix ""summarize: "" \
    --output_dir /tmp/tst-summarization \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
TensorFlow scripts utilize a MirroredStrategy for distributed training, and you don't need to add any additional arguments to the training script. The TensorFlow script will use multiple GPUs by default if they are available.
Run a script on a TPU

Tensor Processing Units (TPUs) are specifically designed to accelerate performance. PyTorch supports TPUs with the XLA deep learning compiler (see here for more details). To use a TPU, launch the xla_spawn.py script and use the num_cores argument to set the number of TPU cores you want to use.

python xla_spawn.py --num_cores 8 \
    summarization/run_summarization.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --source_prefix ""summarize: "" \
    --output_dir /tmp/tst-summarization \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate

Tensor Processing Units (TPUs) are specifically designed to accelerate performance. TensorFlow scripts utilize a TPUStrategy for training on TPUs. To use a TPU, pass the name of the TPU resource to the tpu argument.

python run_summarization.py  \
    --tpu name_of_tpu_resource \
    --model_name_or_path t5-small \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --output_dir /tmp/tst-summarization  \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 16 \
    --num_train_epochs 3 \
    --do_train \
    --do_eval

Run a script with 🤗 Accelerate
🤗 Accelerate is a PyTorch-only library that offers a unified method for training a model on several types of setups (CPU-only, multiple GPUs, TPUs) while maintaining complete visibility into the PyTorch training loop. Make sure you have 🤗 Accelerate installed if you don't already have it:

Note: As Accelerate is rapidly developing, the git version of accelerate must be installed to run the scripts

pip install git+https://github.com/huggingface/accelerate

Instead of the run_summarization.py script, you need to use the run_summarization_no_trainer.py script. 🤗 Accelerate supported scripts will have a task_no_trainer.py file in the folder. Begin by running the following command to create and save a configuration file:

accelerate config
Test your setup to make sure it is configured correctly:

accelerate test
Now you are ready to launch the training:

accelerate launch run_summarization_no_trainer.py \
    --model_name_or_path t5-small \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --source_prefix ""summarize: "" \
    --output_dir ~/tmp/tst-summarization
Use a custom dataset
The summarization script supports custom datasets as long as they are a CSV or JSON Line file. When you use your own dataset, you need to specify several additional arguments:

train_file and validation_file specify the path to your training and validation files.
text_column is the input text to summarize.
summary_column is the target text to output.

A summarization script using a custom dataset would look like this:

python examples/pytorch/summarization/run_summarization.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --train_file path_to_csv_or_jsonlines_file \
    --validation_file path_to_csv_or_jsonlines_file \
    --text_column text_column_name \
    --summary_column summary_column_name \
    --source_prefix ""summarize: "" \
    --output_dir /tmp/tst-summarization \
    --overwrite_output_dir \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --predict_with_generate
Test a script
It is often a good idea to run your script on a smaller number of dataset examples to ensure everything works as expected before committing to an entire dataset which may take hours to complete. Use the following arguments to truncate the dataset to a maximum number of samples:

max_train_samples
max_eval_samples
max_predict_samples

python examples/pytorch/summarization/run_summarization.py \
    --model_name_or_path t5-small \
    --max_train_samples 50 \
    --max_eval_samples 50 \
    --max_predict_samples 50 \
    --do_train \
    --do_eval \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --source_prefix ""summarize: "" \
    --output_dir /tmp/tst-summarization \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
Not all example scripts support the max_predict_samples argument. If you aren't sure whether your script supports this argument, add the -h argument to check:

examples/pytorch/summarization/run_summarization.py -h
Resume training from checkpoint
Another helpful option to enable is resuming training from a previous checkpoint. This will ensure you can pick up where you left off without starting over if your training gets interrupted. There are two methods to resume training from a checkpoint.
The first method uses the output_dir previous_output_dir argument to resume training from the latest checkpoint stored in output_dir. In this case, you should remove overwrite_output_dir:

python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --source_prefix ""summarize: "" \
    --output_dir /tmp/tst-summarization \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --output_dir previous_output_dir \
    --predict_with_generate
The second method uses the resume_from_checkpoint path_to_specific_checkpoint argument to resume training from a specific checkpoint folder.

python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --source_prefix ""summarize: "" \
    --output_dir /tmp/tst-summarization \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --resume_from_checkpoint path_to_specific_checkpoint \
    --predict_with_generate
Share your model
All scripts can upload your final model to the Model Hub. Make sure you are logged into Hugging Face before you begin:

huggingface-cli login
Then add the push_to_hub argument to the script. This argument will create a repository with your Hugging Face username and the folder name specified in output_dir.
To give your repository a specific name, use the push_to_hub_model_id argument to add it. The repository will be automatically listed under your namespace.
The following example shows how to upload a model with a specific repository name:

python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --dataset_name cnn_dailymail \
    --dataset_config ""3.0.0"" \
    --source_prefix ""summarize: "" \
    --push_to_hub \
    --push_to_hub_model_id finetuned-t5-cnn_dailymail \
    --output_dir /tmp/tst-summarization \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate"
"
Padding and truncation
Batched inputs are often different lengths, so they can't be converted to fixed-size tensors. Padding and truncation are strategies for dealing with this problem, to create rectangular tensors from batches of varying lengths. Padding adds a special padding token to ensure shorter sequences will have the same length as either the longest sequence in a batch or the maximum length accepted by the model. Truncation works in the other direction by truncating long sequences.
In most cases, padding your batch to the length of the longest sequence and truncating to the maximum length a model can accept works pretty well. However, the API supports more strategies if you need them. The three arguments you need to are: padding, truncation and max_length.
The padding argument controls padding. It can be a boolean or a string:

True or 'longest': pad to the longest sequence in the batch (no padding is applied if you only provide
    a single sequence).
'max_length': pad to a length specified by the max_length argument or the maximum length accepted
    by the model if no max_length is provided (max_length=None). Padding will still be applied if you only provide a single sequence.
False or 'do_not_pad': no padding is applied. This is the default behavior.

The truncation argument controls truncation. It can be a boolean or a string:

True or 'longest_first': truncate to a maximum length specified by the max_length argument or
    the maximum length accepted by the model if no max_length is provided (max_length=None). This will
    truncate token by token, removing a token from the longest sequence in the pair until the proper length is
    reached.
'only_second': truncate to a maximum length specified by the max_length argument or the maximum
    length accepted by the model if no max_length is provided (max_length=None). This will only truncate
    the second sentence of a pair if a pair of sequences (or a batch of pairs of sequences) is provided.
'only_first': truncate to a maximum length specified by the max_length argument or the maximum
    length accepted by the model if no max_length is provided (max_length=None). This will only truncate
    the first sentence of a pair if a pair of sequences (or a batch of pairs of sequences) is provided.
False or 'do_not_truncate': no truncation is applied. This is the default behavior.

The max_length argument controls the length of the padding and truncation. It can be an integer or None, in which case it will default to the maximum length the model can accept. If the model has no specific maximum input length, truncation or padding to max_length is deactivated.
The following table summarizes the recommended way to setup padding and truncation. If you use pairs of input sequences in any of the following examples, you can replace truncation=True by a STRATEGY selected in
['only_first', 'only_second', 'longest_first'], i.e. truncation='only_second' or truncation='longest_first' to control how both sequences in the pair are truncated as detailed before.
| Truncation                           | Padding                           | Instruction                                                                                 |
|--------------------------------------|-----------------------------------|---------------------------------------------------------------------------------------------|
| no truncation                        | no padding                        | tokenizer(batch_sentences)                                                           |
|                                      | padding to max sequence in batch  | tokenizer(batch_sentences, padding=True) or                                          |
|                                      |                                   | tokenizer(batch_sentences, padding='longest')                                        |
|                                      | padding to max model input length | tokenizer(batch_sentences, padding='max_length')                                     |
|                                      | padding to specific length        | tokenizer(batch_sentences, padding='max_length', max_length=42)                      |
|                                      | padding to a multiple of a value  | tokenizer(batch_sentences, padding=True, pad_to_multiple_of=8)                        |
| truncation to max model input length | no padding                        |tokenizer(batch_sentences, truncation=True)or                                       |
|                                      |                                   |tokenizer(batch_sentences, truncation=STRATEGY)|
|                                      | padding to max sequence in batch  |tokenizer(batch_sentences, padding=True, truncation=True)or                         |
|                                      |                                   |tokenizer(batch_sentences, padding=True, truncation=STRATEGY)|
|                                      | padding to max model input length |tokenizer(batch_sentences, padding='max_length', truncation=True)or                 |
|                                      |                                   |tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)|
|                                      | padding to specific length        | Not possible                                                                                |
| truncation to specific length        | no padding                        |tokenizer(batch_sentences, truncation=True, max_length=42)or                        |
|                                      |                                   |tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)|
|                                      | padding to max sequence in batch  |tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)or          |
|                                      |                                   |tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)|
|                                      | padding to max model input length | Not possible                                                                                |
|                                      | padding to specific length        |tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)or  |
|                                      |                                   |tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |"
"
Preprocess
[[open-in-colab]]
Before you can train a model on a dataset, it needs to be preprocessed into the expected model input format. Whether your data is text, images, or audio, they need to be converted and assembled into batches of tensors. 🤗 Transformers provides a set of preprocessing classes to help prepare your data for the model. In this tutorial, you'll learn that for:

Text, use a Tokenizer to convert text into a sequence of tokens, create a numerical representation of the tokens, and assemble them into tensors.
Speech and audio, use a Feature extractor to extract sequential features from audio waveforms and convert them into tensors.
Image inputs use a ImageProcessor to convert images into tensors.
Multimodal inputs, use a Processor to combine a tokenizer and a feature extractor or image processor.

AutoProcessor always works and automatically chooses the correct class for the model you're using, whether you're using a tokenizer, image processor, feature extractor or processor.

Before you begin, install 🤗 Datasets so you can load some datasets to experiment with:

pip install datasets
Natural Language Processing

The main tool for preprocessing textual data is a tokenizer. A tokenizer splits text into tokens according to a set of rules. The tokens are converted into numbers and then tensors, which become the model inputs. Any additional inputs required by the model are added by the tokenizer.

If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer. This ensures the text is split the same way as the pretraining corpus, and uses the same corresponding tokens-to-index (usually referred to as the vocab) during pretraining.

Get started by loading a pretrained tokenizer with the [AutoTokenizer.from_pretrained] method. This downloads the vocab a model was pretrained with:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""bert-base-cased"")

Then pass your text to the tokenizer:

encoded_input = tokenizer(""Do not meddle in the affairs of wizards, for they are subtle and quick to anger."")
print(encoded_input)
{'input_ids': [101, 2079, 2025, 19960, 10362, 1999, 1996, 3821, 1997, 16657, 1010, 2005, 2027, 2024, 11259, 1998, 4248, 2000, 4963, 1012, 102],
 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

The tokenizer returns a dictionary with three important items:

input_ids are the indices corresponding to each token in the sentence.
attention_mask indicates whether a token should be attended to or not.
token_type_ids identifies which sequence a token belongs to when there is more than one sequence.

Return your input by decoding the input_ids:

tokenizer.decode(encoded_input[""input_ids""])
'[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]'

As you can see, the tokenizer added two special tokens - CLS and SEP (classifier and separator) - to the sentence. Not all models need
special tokens, but if they do, the tokenizer automatically adds them for you.
If there are several sentences you want to preprocess, pass them as a list to the tokenizer:

batch_sentences = [
     ""But what about second breakfast?"",
     ""Don't think he knows about second breakfast, Pip."",
     ""What about elevensies?"",
 ]
encoded_inputs = tokenizer(batch_sentences)
print(encoded_inputs)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102],
               [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
               [101, 1327, 1164, 5450, 23434, 136, 102]],
 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0]],
 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 1, 1, 1, 1, 1, 1]]}

Pad
Sentences aren't always the same length which can be an issue because tensors, the model inputs, need to have a uniform shape. Padding is a strategy for ensuring tensors are rectangular by adding a special padding token to shorter sentences.
Set the padding parameter to True to pad the shorter sequences in the batch to match the longest sequence:

batch_sentences = [
     ""But what about second breakfast?"",
     ""Don't think he knows about second breakfast, Pip."",
     ""What about elevensies?"",
 ]
encoded_input = tokenizer(batch_sentences, padding=True)
print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
               [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
               [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}

The first and third sentences are now padded with 0's because they are shorter.
Truncation
On the other end of the spectrum, sometimes a sequence may be too long for a model to handle. In this case, you'll need to truncate the sequence to a shorter length.
Set the truncation parameter to True to truncate a sequence to the maximum length accepted by the model:

batch_sentences = [
     ""But what about second breakfast?"",
     ""Don't think he knows about second breakfast, Pip."",
     ""What about elevensies?"",
 ]
encoded_input = tokenizer(batch_sentences, padding=True, truncation=True)
print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
               [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
               [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}

Check out the Padding and truncation concept guide to learn more different padding and truncation arguments.

Build tensors
Finally, you want the tokenizer to return the actual tensors that get fed to the model.
Set the return_tensors parameter to either pt for PyTorch, or tf for TensorFlow:

batch_sentences = [
     ""But what about second breakfast?"",
     ""Don't think he knows about second breakfast, Pip."",
     ""What about elevensies?"",
 ]
encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors=""pt"")
print(encoded_input)
{'input_ids': tensor([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
                      [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
                      [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]]),
 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),
 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
                           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                           [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}
</pt>
<tf>py
batch_sentences = [
     ""But what about second breakfast?"",
     ""Don't think he knows about second breakfast, Pip."",
     ""What about elevensies?"",
 ]
encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors=""tf"")
print(encoded_input)
{'input_ids': ,
 'token_type_ids': ,
 'attention_mask': }

Audio
For audio tasks, you'll need a feature extractor to prepare your dataset for the model. The feature extractor is designed to extract features from raw audio data, and convert them into tensors.
Load the MInDS-14 dataset (see the 🤗 Datasets tutorial for more details on how to load a dataset) to see how you can use a feature extractor with audio datasets:

from datasets import load_dataset, Audio
dataset = load_dataset(""PolyAI/minds14"", name=""en-US"", split=""train"")

Access the first element of the audio column to take a look at the input. Calling the audio column automatically loads and resamples the audio file:

dataset[0][""audio""]
{'array': array([ 0.        ,  0.00024414, -0.00024414, , -0.00024414,
         0.        ,  0.        ], dtype=float32),
 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
 'sampling_rate': 8000}

This returns three items:

array is the speech signal loaded - and potentially resampled - as a 1D array.
path points to the location of the audio file.
sampling_rate refers to how many data points in the speech signal are measured per second.

For this tutorial, you'll use the Wav2Vec2 model. Take a look at the model card, and you'll learn Wav2Vec2 is pretrained on 16kHz sampled speech audio. It is important your audio data's sampling rate matches the sampling rate of the dataset used to pretrain the model. If your data's sampling rate isn't the same, then you need to resample your data.

Use 🤗 Datasets' [~datasets.Dataset.cast_column] method to upsample the sampling rate to 16kHz:

dataset = dataset.cast_column(""audio"", Audio(sampling_rate=16_000))

Call the audio column again to resample the audio file:

dataset[0][""audio""]
{'array': array([ 2.3443763e-05,  2.1729663e-04,  2.2145823e-04, ,
         3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
 'sampling_rate': 16000}

Next, load a feature extractor to normalize and pad the input. When padding textual data, a 0 is added for shorter sequences. The same idea applies to audio data. The feature extractor adds a 0 - interpreted as silence - to array.
Load the feature extractor with [AutoFeatureExtractor.from_pretrained]:

from transformers import AutoFeatureExtractor
feature_extractor = AutoFeatureExtractor.from_pretrained(""facebook/wav2vec2-base"")

Pass the audio array to the feature extractor. We also recommend adding the sampling_rate argument in the feature extractor in order to better debug any silent errors that may occur.

audio_input = [dataset[0][""audio""][""array""]]
feature_extractor(audio_input, sampling_rate=16000)
{'input_values': [array([ 3.8106556e-04,  2.7506407e-03,  2.8015103e-03, ,
        5.6335266e-04,  4.6588284e-06, -1.7142107e-04], dtype=float32)]}

Just like the tokenizer, you can apply padding or truncation to handle variable sequences in a batch. Take a look at the sequence length of these two audio samples:

dataset[0][""audio""][""array""].shape
(173398,)
dataset[1][""audio""][""array""].shape
(106496,)

Create a function to preprocess the dataset so the audio samples are the same lengths. Specify a maximum sample length, and the feature extractor will either pad or truncate the sequences to match it:

def preprocess_function(examples):
     audio_arrays = [x[""array""] for x in examples[""audio""]]
     inputs = feature_extractor(
         audio_arrays,
         sampling_rate=16000,
         padding=True,
         max_length=100000,
         truncation=True,
     )
     return inputs

Apply the preprocess_function to the the first few examples in the dataset:

processed_dataset = preprocess_function(dataset[:5])

The sample lengths are now the same and match the specified maximum length. You can pass your processed dataset to the model now!

processed_dataset[""input_values""][0].shape
(100000,)
processed_dataset[""input_values""][1].shape
(100000,)

Computer vision
For computer vision tasks, you'll need an image processor to prepare your dataset for the model.
Image preprocessing consists of several steps that convert images into the input expected by the model. These steps
include but are not limited to resizing, normalizing, color channel correction, and converting images to tensors.

Image preprocessing often follows some form of image augmentation. Both image preprocessing and image augmentation
transform image data, but they serve different purposes:

Image augmentation alters images in a way that can help prevent overfitting and increase the robustness of the model. You can get creative in how you augment your data - adjust brightness and colors, crop, rotate, resize, zoom, etc. However, be mindful not to change the meaning of the images with your augmentations.
Image preprocessing guarantees that the images match the model’s expected input format. When fine-tuning a computer vision model, images must be preprocessed exactly as when the model was initially trained.

You can use any library you like for image augmentation. For image preprocessing, use the ImageProcessor associated with the model.

Load the food101 dataset (see the 🤗 Datasets tutorial for more details on how to load a dataset) to see how you can use an image processor with computer vision datasets:

Use 🤗 Datasets split parameter to only load a small sample from the training split since the dataset is quite large!

from datasets import load_dataset
dataset = load_dataset(""food101"", split=""train[:100]"")

Next, take a look at the image with 🤗 Datasets Image feature:

dataset[0][""image""]

Load the image processor with [AutoImageProcessor.from_pretrained]:

from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained(""google/vit-base-patch16-224"")

First, let's add some image augmentation. You can use any library you prefer, but in this tutorial, we'll use torchvision's transforms module. If you're interested in using another data augmentation library, learn how in the Albumentations or Kornia notebooks.

Here we use Compose to chain together a couple of
transforms - RandomResizedCrop and ColorJitter.
Note that for resizing, we can get the image size requirements from the image_processor. For some models, an exact height and
width are expected, for others only the shortest_edge is defined.

from torchvision.transforms import RandomResizedCrop, ColorJitter, Compose
size = (
     image_processor.size[""shortest_edge""]
     if ""shortest_edge"" in image_processor.size
     else (image_processor.size[""height""], image_processor.size[""width""])
 )
_transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5)])

The model accepts pixel_values
as its input. ImageProcessor can take care of normalizing the images, and generating appropriate tensors.
Create a function that combines image augmentation and image preprocessing for a batch of images and generates pixel_values:

def transforms(examples):
     images = [_transforms(img.convert(""RGB"")) for img in examples[""image""]]
     examples[""pixel_values""] = image_processor(images, do_resize=False, return_tensors=""pt"")[""pixel_values""]
     return examples

In the example above we set do_resize=False because we have already resized the images in the image augmentation transformation,
and leveraged the size attribute from the appropriate image_processor. If you do not resize images during image augmentation,
leave this parameter out. By default, ImageProcessor will handle the resizing.
If you wish to normalize images as a part of the augmentation transformation, use the image_processor.image_mean,
and image_processor.image_std values.

Then use 🤗 Datasets set_transform to apply the transforms on the fly:

dataset.set_transform(transforms)

Now when you access the image, you'll notice the image processor has added pixel_values. You can pass your processed dataset to the model now!

dataset[0].keys()

Here is what the image looks like after the transforms are applied. The image has been randomly cropped and it's color properties are different.

import numpy as np
import matplotlib.pyplot as plt
img = dataset[0][""pixel_values""]
plt.imshow(img.permute(1, 2, 0))

For tasks like object detection, semantic segmentation, instance segmentation, and panoptic segmentation, ImageProcessor
offers post processing methods. These methods convert model's raw outputs into meaningful predictions such as bounding boxes,
or segmentation maps.

Pad
In some cases, for instance, when fine-tuning DETR, the model applies scale augmentation at training
time. This may cause images to be different sizes in a batch. You can use [DetrImageProcessor.pad]
from [DetrImageProcessor] and define a custom collate_fn to batch images together.

def collate_fn(batch):
     pixel_values = [item[""pixel_values""] for item in batch]
     encoding = image_processor.pad(pixel_values, return_tensors=""pt"")
     labels = [item[""labels""] for item in batch]
     batch = {}
     batch[""pixel_values""] = encoding[""pixel_values""]
     batch[""pixel_mask""] = encoding[""pixel_mask""]
     batch[""labels""] = labels
     return batch

Multimodal
For tasks involving multimodal inputs, you'll need a processor to prepare your dataset for the model. A processor couples together two processing objects such as as tokenizer and feature extractor.
Load the LJ Speech dataset (see the 🤗 Datasets tutorial for more details on how to load a dataset) to see how you can use a processor for automatic speech recognition (ASR):

from datasets import load_dataset
lj_speech = load_dataset(""lj_speech"", split=""train"")

For ASR, you're mainly focused on audio and text so you can remove the other columns:

lj_speech = lj_speech.map(remove_columns=[""file"", ""id"", ""normalized_text""])

Now take a look at the audio and text columns:

lj_speech[0][""audio""]
{'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ,
         7.3242188e-04,  2.1362305e-04,  6.1035156e-05], dtype=float32),
 'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
 'sampling_rate': 22050}
lj_speech[0][""text""]
'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'

Remember you should always resample your audio dataset's sampling rate to match the sampling rate of the dataset used to pretrain a model!

lj_speech = lj_speech.cast_column(""audio"", Audio(sampling_rate=16_000))

Load a processor with [AutoProcessor.from_pretrained]:

from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained(""facebook/wav2vec2-base-960h"")

Create a function to process the audio data contained in array to input_values, and tokenize text to labels. These are the inputs to the model:

def prepare_dataset(example):
     audio = example[""audio""]

     example.update(processor(audio=audio[""array""], text=example[""text""], sampling_rate=16000))
     return example

Apply the prepare_dataset function to a sample:

prepare_dataset(lj_speech[0])

The processor has now added input_values and labels, and the sampling rate has also been correctly downsampled to 16kHz. You can pass your processed dataset to the model now!"
"
Training on Specialized Hardware

Note: Most of the strategies introduced in the single GPU section (such as mixed precision training or gradient accumulation) and multi-GPU section are generic and apply to training models in general so make sure to have a look at it before diving into this section.

This document will be completed soon with information on how to train on specialized hardware."
"
Feature Extractor
A feature extractor is in charge of preparing input features for audio or vision models. This includes feature extraction
from sequences, e.g., pre-processing audio files to Log-Mel Spectrogram features, feature extraction from images
e.g. cropping image image files, but also padding, normalization, and conversion to Numpy, PyTorch, and TensorFlow
tensors.
FeatureExtractionMixin
[[autodoc]] feature_extraction_utils.FeatureExtractionMixin
    - from_pretrained
    - save_pretrained
SequenceFeatureExtractor
[[autodoc]] SequenceFeatureExtractor
    - pad
BatchFeature
[[autodoc]] BatchFeature
ImageFeatureExtractionMixin
[[autodoc]] image_utils.ImageFeatureExtractionMixin"
"
FocalNet
Overview
The FocalNet model was proposed in Focal Modulation Networks by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
FocalNets completely replace self-attention (used in models like ViT and Swin) by a focal modulation mechanism for modeling token interactions in vision.
The authors claim that FocalNets outperform self-attention based models with similar computational costs on the tasks of image classification, object detection, and segmentation.
The abstract from the paper is the following:
We propose focal modulation networks (FocalNets in short), where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation. Specifically, FocalNets with tiny and base size achieve 82.3% and 83.9% top-1 accuracy on ImageNet-1K. After pretrained on ImageNet-22K in 224 resolution, it attains 86.5% and 87.3% top-1 accuracy when finetuned with resolution 224 and 384, respectively. When transferred to downstream tasks, FocalNets exhibit clear superiority. For object detection with Mask R-CNN, FocalNet base trained with 1\times outperforms the Swin counterpart by 2.1 points and already surpasses Swin trained with 3\times schedule (49.0 v.s. 48.5). For semantic segmentation with UPerNet, FocalNet base at single-scale outperforms Swin by 2.4, and beats Swin at multi-scale (50.5 v.s. 49.7). Using large FocalNet and Mask2former, we achieve 58.5 mIoU for ADE20K semantic segmentation, and 57.9 PQ for COCO Panoptic Segmentation. Using huge FocalNet and DINO, we achieved 64.3 and 64.4 mAP on COCO minival and test-dev, respectively, establishing new SoTA on top of much larger attention-based models like Swinv2-G and BEIT-3.
Tips:

One can use the [AutoImageProcessor] class to prepare images for the model.

This model was contributed by nielsr.
The original code can be found here.
FocalNetConfig
[[autodoc]] FocalNetConfig
FocalNetModel
[[autodoc]] FocalNetModel
    - forward
FocalNetForMaskedImageModeling
[[autodoc]] FocalNetForMaskedImageModeling
    - forward
FocalNetForImageClassification
[[autodoc]] FocalNetForImageClassification
    - forward"
"
RoFormer
Overview
The RoFormer model was proposed in RoFormer: Enhanced Transformer with Rotary Position Embedding by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
The abstract from the paper is the following:
Position encoding in transformer architecture provides supervision for dependency modeling between elements at
different positions in the sequence. We investigate various methods to encode positional information in
transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The
proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative
position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of
being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and
capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced
transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We
release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing
experiment for English benchmark will soon be updated.
Tips:

RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown
  improved performance on classification tasks with long texts.

This model was contributed by junnyu. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

RoFormerConfig
[[autodoc]] RoFormerConfig
RoFormerTokenizer
[[autodoc]] RoFormerTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
RoFormerTokenizerFast
[[autodoc]] RoFormerTokenizerFast
    - build_inputs_with_special_tokens
RoFormerModel
[[autodoc]] RoFormerModel
    - forward
RoFormerForCausalLM
[[autodoc]] RoFormerForCausalLM
    - forward
RoFormerForMaskedLM
[[autodoc]] RoFormerForMaskedLM
    - forward
RoFormerForSequenceClassification
[[autodoc]] RoFormerForSequenceClassification
    - forward
RoFormerForMultipleChoice
[[autodoc]] RoFormerForMultipleChoice
    - forward
RoFormerForTokenClassification
[[autodoc]] RoFormerForTokenClassification
    - forward
RoFormerForQuestionAnswering
[[autodoc]] RoFormerForQuestionAnswering
    - forward
TFRoFormerModel
[[autodoc]] TFRoFormerModel
    - call
TFRoFormerForMaskedLM
[[autodoc]] TFRoFormerForMaskedLM
    - call
TFRoFormerForCausalLM
[[autodoc]] TFRoFormerForCausalLM
    - call
TFRoFormerForSequenceClassification
[[autodoc]] TFRoFormerForSequenceClassification
    - call
TFRoFormerForMultipleChoice
[[autodoc]] TFRoFormerForMultipleChoice
    - call
TFRoFormerForTokenClassification
[[autodoc]] TFRoFormerForTokenClassification
    - call
TFRoFormerForQuestionAnswering
[[autodoc]] TFRoFormerForQuestionAnswering
    - call
FlaxRoFormerModel
[[autodoc]] FlaxRoFormerModel
    - call
FlaxRoFormerForMaskedLM
[[autodoc]] FlaxRoFormerForMaskedLM
    - call
FlaxRoFormerForSequenceClassification
[[autodoc]] FlaxRoFormerForSequenceClassification
    - call
FlaxRoFormerForMultipleChoice
[[autodoc]] FlaxRoFormerForMultipleChoice
    - call
FlaxRoFormerForTokenClassification
[[autodoc]] FlaxRoFormerForTokenClassification
    - call
FlaxRoFormerForQuestionAnswering
[[autodoc]] FlaxRoFormerForQuestionAnswering
    - call"
"
RoBERTa-PreLayerNorm
Overview
The RoBERTa-PreLayerNorm model was proposed in fairseq: A Fast, Extensible Toolkit for Sequence Modeling by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli.
It is identical to using the --encoder-normalize-before flag in fairseq.
The abstract from the paper is the following:
fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs.
Tips:

The implementation is the same as Roberta except instead of using Add and Norm it does Norm and Add. Add and Norm refers to the Addition and LayerNormalization as described in Attention Is All You Need.
This is identical to using the --encoder-normalize-before flag in fairseq.

This model was contributed by andreasmaden.
The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

RobertaPreLayerNormConfig
[[autodoc]] RobertaPreLayerNormConfig
RobertaPreLayerNormModel
[[autodoc]] RobertaPreLayerNormModel
    - forward
RobertaPreLayerNormForCausalLM
[[autodoc]] RobertaPreLayerNormForCausalLM
    - forward
RobertaPreLayerNormForMaskedLM
[[autodoc]] RobertaPreLayerNormForMaskedLM
    - forward
RobertaPreLayerNormForSequenceClassification
[[autodoc]] RobertaPreLayerNormForSequenceClassification
    - forward
RobertaPreLayerNormForMultipleChoice
[[autodoc]] RobertaPreLayerNormForMultipleChoice
    - forward
RobertaPreLayerNormForTokenClassification
[[autodoc]] RobertaPreLayerNormForTokenClassification
    - forward
RobertaPreLayerNormForQuestionAnswering
[[autodoc]] RobertaPreLayerNormForQuestionAnswering
    - forward
TFRobertaPreLayerNormModel
[[autodoc]] TFRobertaPreLayerNormModel
    - call
TFRobertaPreLayerNormForCausalLM
[[autodoc]] TFRobertaPreLayerNormForCausalLM
    - call
TFRobertaPreLayerNormForMaskedLM
[[autodoc]] TFRobertaPreLayerNormForMaskedLM
    - call
TFRobertaPreLayerNormForSequenceClassification
[[autodoc]] TFRobertaPreLayerNormForSequenceClassification
    - call
TFRobertaPreLayerNormForMultipleChoice
[[autodoc]] TFRobertaPreLayerNormForMultipleChoice
    - call
TFRobertaPreLayerNormForTokenClassification
[[autodoc]] TFRobertaPreLayerNormForTokenClassification
    - call
TFRobertaPreLayerNormForQuestionAnswering
[[autodoc]] TFRobertaPreLayerNormForQuestionAnswering
    - call
FlaxRobertaPreLayerNormModel
[[autodoc]] FlaxRobertaPreLayerNormModel
    - call
FlaxRobertaPreLayerNormForCausalLM
[[autodoc]] FlaxRobertaPreLayerNormForCausalLM
    - call
FlaxRobertaPreLayerNormForMaskedLM
[[autodoc]] FlaxRobertaPreLayerNormForMaskedLM
    - call
FlaxRobertaPreLayerNormForSequenceClassification
[[autodoc]] FlaxRobertaPreLayerNormForSequenceClassification
    - call
FlaxRobertaPreLayerNormForMultipleChoice
[[autodoc]] FlaxRobertaPreLayerNormForMultipleChoice
    - call
FlaxRobertaPreLayerNormForTokenClassification
[[autodoc]] FlaxRobertaPreLayerNormForTokenClassification
    - call
FlaxRobertaPreLayerNormForQuestionAnswering
[[autodoc]] FlaxRobertaPreLayerNormForQuestionAnswering
    - call"
"
SpeechT5
Overview
The SpeechT5 model was proposed in SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
The abstract from the paper is the following:
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder. Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder. Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification.
This model was contributed by Matthijs. The original code can be found here.
SpeechT5Config
[[autodoc]] SpeechT5Config
SpeechT5HifiGanConfig
[[autodoc]] SpeechT5HifiGanConfig
SpeechT5Tokenizer
[[autodoc]] SpeechT5Tokenizer
    - call
    - save_vocabulary
    - decode
    - batch_decode
SpeechT5FeatureExtractor
[[autodoc]] SpeechT5FeatureExtractor
    - call
SpeechT5Processor
[[autodoc]] SpeechT5Processor
    - call
    - pad
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
SpeechT5Model
[[autodoc]] SpeechT5Model
    - forward
SpeechT5ForSpeechToText
[[autodoc]] SpeechT5ForSpeechToText
    - forward
SpeechT5ForTextToSpeech
[[autodoc]] SpeechT5ForTextToSpeech
    - forward
    - generate_speech
SpeechT5ForSpeechToSpeech
[[autodoc]] SpeechT5ForSpeechToSpeech
    - forward
    - generate_speech
SpeechT5HifiGan
[[autodoc]] SpeechT5HifiGan
    - forward"
"
BertGeneration
Overview
The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
[EncoderDecoderModel] as proposed in Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
The abstract from the paper is the following:
Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By
warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple
benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language
Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We
developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT,
GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both
encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation,
Text Summarization, Sentence Splitting, and Sentence Fusion.
Usage:

The model can be used in combination with the [EncoderDecoderModel] to leverage two pretrained
  BERT checkpoints for subsequent fine-tuning.

thon

leverage checkpoints for Bert2Bert model
use BERT's cls token as BOS token and sep token as EOS token
encoder = BertGenerationEncoder.from_pretrained(""bert-large-uncased"", bos_token_id=101, eos_token_id=102)
add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
decoder = BertGenerationDecoder.from_pretrained(
     ""bert-large-uncased"", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102
 )
bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
create tokenizer
tokenizer = BertTokenizer.from_pretrained(""bert-large-uncased"")
input_ids = tokenizer(
     ""This is a long article to summarize"", add_special_tokens=False, return_tensors=""pt""
 ).input_ids
labels = tokenizer(""This is a short summary"", return_tensors=""pt"").input_ids
train
loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
loss.backward()

Pretrained [EncoderDecoderModel] are also directly available in the model hub, e.g.,

thon

instantiate sentence fusion model
sentence_fuser = EncoderDecoderModel.from_pretrained(""google/roberta2roberta_L-24_discofuse"")
tokenizer = AutoTokenizer.from_pretrained(""google/roberta2roberta_L-24_discofuse"")
input_ids = tokenizer(
     ""This is the first sentence. This is the second sentence."", add_special_tokens=False, return_tensors=""pt""
 ).input_ids
outputs = sentence_fuser.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Tips:

[BertGenerationEncoder] and [BertGenerationDecoder] should be used in
  combination with [EncoderDecoder].
For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input.
  Therefore, no EOS token should be added to the end of the input.

This model was contributed by patrickvonplaten. The original code can be
found here.
BertGenerationConfig
[[autodoc]] BertGenerationConfig
BertGenerationTokenizer
[[autodoc]] BertGenerationTokenizer
    - save_vocabulary
BertGenerationEncoder
[[autodoc]] BertGenerationEncoder
    - forward
BertGenerationDecoder
[[autodoc]] BertGenerationDecoder
    - forward"
"
VisualBERT
Overview
The VisualBERT model was proposed in VisualBERT: A Simple and Performant Baseline for Vision and Language by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
VisualBERT is a neural network trained on a variety of (image, text) pairs.
The abstract from the paper is the following:
We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks.
VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an
associated input image with self-attention. We further propose two visually-grounded language model objectives for
pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2,
and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly
simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any
explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between
verbs and image regions corresponding to their arguments.
Tips:

Most of the checkpoints provided work with the [VisualBertForPreTraining] configuration. Other
   checkpoints provided are the fine-tuned checkpoints for down-stream tasks - VQA ('visualbert-vqa'), VCR
   ('visualbert-vcr'), NLVR2 ('visualbert-nlvr2'). Hence, if you are not working on these downstream tasks, it is
   recommended that you use the pretrained checkpoints.

For the VCR task, the authors use a fine-tuned detector for generating visual embeddings, for all the checkpoints.
   We do not provide the detector and its weights as a part of the package, but it will be available in the research
   projects, and the states can be loaded directly into the detector provided.

Usage
VisualBERT is a multi-modal vision and language model. It can be used for visual question answering, multiple choice,
visual reasoning and region-to-phrase correspondence tasks. VisualBERT uses a BERT-like transformer to prepare
embeddings for image-text pairs. Both the text and visual features are then projected to a latent space with identical
dimension.
To feed images to the model, each image is passed through a pre-trained object detector and the regions and the
bounding boxes are extracted. The authors use the features generated after passing these regions through a pre-trained
CNN like ResNet as visual embeddings. They also add absolute position embeddings, and feed the resulting sequence of
vectors to a standard BERT model. The text input is concatenated in the front of the visual embeddings in the embedding
layer, and is expected to be bound by [CLS] and a [SEP] tokens, as in BERT. The segment IDs must also be set
appropriately for the textual and visual parts.
The [BertTokenizer] is used to encode the text. A custom detector/image processor must be used
to get the visual embeddings. The following example notebooks show how to use VisualBERT with Detectron-like models:

VisualBERT VQA demo notebook : This notebook
  contains an example on VisualBERT VQA.

Generate Embeddings for VisualBERT (Colab Notebook) : This notebook contains
  an example on how to generate visual embeddings.

The following example shows how to get the last hidden state using [VisualBertModel]:
thon

import torch
from transformers import BertTokenizer, VisualBertModel
model = VisualBertModel.from_pretrained(""uclanlp/visualbert-vqa-coco-pre"")
tokenizer = BertTokenizer.from_pretrained(""bert-base-uncased"")
inputs = tokenizer(""What is the man eating?"", return_tensors=""pt"")
this is a custom function that returns the visual embeddings given the image path
visual_embeds = get_visual_embeddings(image_path)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
inputs.update(
     {
         ""visual_embeds"": visual_embeds,
         ""visual_token_type_ids"": visual_token_type_ids,
         ""visual_attention_mask"": visual_attention_mask,
     }
 )
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state

This model was contributed by gchhablani. The original code can be found here.
VisualBertConfig
[[autodoc]] VisualBertConfig
VisualBertModel
[[autodoc]] VisualBertModel
    - forward
VisualBertForPreTraining
[[autodoc]] VisualBertForPreTraining
    - forward
VisualBertForQuestionAnswering
[[autodoc]] VisualBertForQuestionAnswering
    - forward
VisualBertForMultipleChoice
[[autodoc]] VisualBertForMultipleChoice
    - forward
VisualBertForVisualReasoning
[[autodoc]] VisualBertForVisualReasoning
    - forward
VisualBertForRegionToPhraseAlignment
[[autodoc]] VisualBertForRegionToPhraseAlignment
    - forward"
"
VisionTextDualEncoder
Overview
The [VisionTextDualEncoderModel] can be used to initialize a vision-text dual encoder model with
any pretrained vision autoencoding model as the vision encoder (e.g. ViT, BEiT, DeiT) and any pretrained text autoencoding model as the text encoder (e.g. RoBERTa, BERT). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
training and then can be used for zero-shot vision tasks such image-classification or retrieval.
In LiT: Zero-Shot Transfer with Locked-image Text Tuning it is shown how
leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvement on
new zero-shot vision tasks such as image classification or retrieval.
VisionTextDualEncoderConfig
[[autodoc]] VisionTextDualEncoderConfig
VisionTextDualEncoderProcessor
[[autodoc]] VisionTextDualEncoderProcessor
VisionTextDualEncoderModel
[[autodoc]] VisionTextDualEncoderModel
    - forward
FlaxVisionTextDualEncoderModel
[[autodoc]] FlaxVisionTextDualEncoderModel
    - call
TFVisionTextDualEncoderModel
[[autodoc]] TFVisionTextDualEncoderModel
    - call"
"
MegatronGPT2
Overview
The MegatronGPT2 model was proposed in Megatron-LM: Training Multi-Billion Parameter Language Models Using Model
Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley,
Jared Casper and Bryan Catanzaro.
The abstract from the paper is the following:
Recent work in language modeling demonstrates that training large transformer models advances the state of the art in
Natural Language Processing applications. However, very large models can be quite difficult to train due to memory
constraints. In this work, we present our techniques for training very large transformer models and implement a simple,
efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our
approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model
parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We
illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain
15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline
that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance
the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9
billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in
BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we
achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA
accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy
of 89.4%).
Tips:
We have provided pretrained GPT2-345M checkpoints
for use to evaluate or finetuning downstream tasks.
To access these checkpoints, first sign up for and setup the NVIDIA GPU Cloud (NGC)
Registry CLI. Further documentation for downloading models can be found in the NGC documentation.
Alternatively, you can directly download the checkpoints using:

wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O
megatron_gpt2_345m_v0_0.zip
Once you have obtained the checkpoint from NVIDIA GPU Cloud (NGC), you have to convert it to a format that will easily
be loaded by Hugging Face Transformers GPT2 implementation.
The following command allows you to do the conversion. We assume that the folder models/megatron_gpt2 contains
megatron_gpt2_345m_v0_0.zip and that the command is run from that folder:

python3 $PATH_TO_TRANSFORMERS/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py megatron_gpt2_345m_v0_0.zip
This model was contributed by jdemouth. The original code can be found here. That repository contains a multi-GPU and multi-node implementation of the
Megatron Language models. In particular, it contains a hybrid model parallel approach using ""tensor parallel"" and
""pipeline parallel"" techniques."
"
LongT5
Overview
The LongT5 model was proposed in LongT5: Efficient Text-To-Text Transformer for Long Sequences
by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. It's an
encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of
T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2)
Transient-Global attention.
The abstract from the paper is the following:
Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the
performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we
explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated
attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training
(PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call {\em Transient Global}
(TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs. We are
able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on
question answering tasks.
Tips:

[LongT5ForConditionalGeneration] is an extension of [T5ForConditionalGeneration] exchanging the traditional
encoder self-attention layer with efficient either local attention or transient-global (tglobal) attention.
Unlike the T5 model, LongT5 does not use a task prefix. Furthermore, it uses a different pre-training objective
inspired by the pre-training of [PegasusForConditionalGeneration].
LongT5 model is designed to work efficiently and very well on long-range sequence-to-sequence tasks where the
input sequence exceeds commonly used 512 tokens. It is capable of handling input sequences of a length up to 16,384 tokens.
For Local Attention, the sparse sliding-window local attention operation allows a given token to attend only r
tokens to the left and right of it (with r=127 by default). Local Attention does not introduce any new parameters
to the model. The complexity of the mechanism is linear in input sequence length l: O(l*r).
Transient Global Attention is an extension of the Local Attention. It, furthermore, allows each input token to
interact with all other tokens in the layer. This is achieved via splitting an input sequence into blocks of a fixed
length k (with a default k=16). Then, a global token for such a block is obtained via summing and normalizing the embeddings of every token
in the block. Thanks to this, the attention allows each token to attend to both nearby tokens like in Local attention, and
also every global token like in the case of standard global attention (transient represents the fact the global tokens
are constructed dynamically within each attention operation).  As a consequence, TGlobal attention introduces
a few new parameters -- global relative position biases and a layer normalization for global token's embedding.
The complexity of this mechanism is O(l(r + l/k)).
An example showing how to evaluate a fine-tuned LongT5 model on the pubmed dataset is below.

thon

import evaluate
from datasets import load_dataset
from transformers import AutoTokenizer, LongT5ForConditionalGeneration
dataset = load_dataset(""scientific_papers"", ""pubmed"", split=""validation"")
model = (
     LongT5ForConditionalGeneration.from_pretrained(""Stancld/longt5-tglobal-large-16384-pubmed-3k_steps"")
     .to(""cuda"")
     .half()
 )
tokenizer = AutoTokenizer.from_pretrained(""Stancld/longt5-tglobal-large-16384-pubmed-3k_steps"")
def generate_answers(batch):
     inputs_dict = tokenizer(
         batch[""article""], max_length=16384, padding=""max_length"", truncation=True, return_tensors=""pt""
     )
     input_ids = inputs_dict.input_ids.to(""cuda"")
     attention_mask = inputs_dict.attention_mask.to(""cuda"")
     output_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=512, num_beams=2)
     batch[""predicted_abstract""] = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
     return batch
result = dataset.map(generate_answer, batched=True, batch_size=2)
rouge = evaluate.load(""rouge"")
rouge.compute(predictions=result[""predicted_abstract""], references=result[""abstract""])

This model was contributed by stancld.
The original code can be found here.
Documentation resources

Translation task guide
Summarization task guide

LongT5Config
[[autodoc]] LongT5Config
LongT5Model
[[autodoc]] LongT5Model
    - forward
LongT5ForConditionalGeneration
[[autodoc]] LongT5ForConditionalGeneration
    - forward
LongT5EncoderModel
[[autodoc]] LongT5EncoderModel
    - forward
FlaxLongT5Model
[[autodoc]] FlaxLongT5Model
    - call
    - encode
    - decode
FlaxLongT5ForConditionalGeneration
[[autodoc]] FlaxLongT5ForConditionalGeneration
    - call
    - encode
    - decode"
"
CTRL

Overview
CTRL model was proposed in CTRL: A Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong and
Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
The abstract from the paper is the following:
Large-scale language models show promising text generation capabilities, but users cannot easily control particular
aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
via model-based source attribution.
Tips:

CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
  or links to generate coherent text. Refer to the original implementation for
  more information.
CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
  token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
  observed in the run_generation.py example script.
The PyTorch models can take the past_key_values as input, which is the previously computed key/value attention pairs.
  TensorFlow models accepts past as input. Using the past_key_values value prevents the model from re-computing
  pre-computed values in the context of text generation. See the forward
  method for more information on the usage of this argument.

This model was contributed by keskarnitishr. The original code can be found
here.
Documentation resources

Text classification task guide
Causal language modeling task guide

CTRLConfig
[[autodoc]] CTRLConfig
CTRLTokenizer
[[autodoc]] CTRLTokenizer
    - save_vocabulary
CTRLModel
[[autodoc]] CTRLModel
    - forward
CTRLLMHeadModel
[[autodoc]] CTRLLMHeadModel
    - forward
CTRLForSequenceClassification
[[autodoc]] CTRLForSequenceClassification
    - forward
TFCTRLModel
[[autodoc]] TFCTRLModel
    - call
TFCTRLLMHeadModel
[[autodoc]] TFCTRLLMHeadModel
    - call
TFCTRLForSequenceClassification
[[autodoc]] TFCTRLForSequenceClassification
    - call"
"
MBart and MBart-50

DISCLAIMER: If you see something strange, file a Github Issue and assign
@patrickvonplaten
Overview of MBart
The MBart model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan
Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual
corpora in many languages using the BART objective. mBART is one of the first methods for pretraining a complete
sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only
on the encoder, decoder, or reconstructing parts of the text.
This model was contributed by valhalla. The Authors' code can be found here
Training of MBart
MBart is a multilingual encoder-decoder (sequence-to-sequence) model primarily intended for translation task. As the
model is multilingual it expects the sequences in a different format. A special language id token is added in both the
source and target text. The source text format is X [eos, src_lang_code] where X is the source text. The
target text format is [tgt_lang_code] X [eos]. bos is never used.
The regular [~MBartTokenizer.__call__] will encode source text format passed as first argument or with the text
keyword, and target text format passed with the text_label keyword argument.

Supervised training

thon

from transformers import MBartForConditionalGeneration, MBartTokenizer
tokenizer = MBartTokenizer.from_pretrained(""facebook/mbart-large-en-ro"", src_lang=""en_XX"", tgt_lang=""ro_RO"")
example_english_phrase = ""UN Chief Says There Is No Military Solution in Syria""
expected_translation_romanian = ""Şeful ONU declară că nu există o soluţie militară în Siria""
inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors=""pt"")
model = MBartForConditionalGeneration.from_pretrained(""facebook/mbart-large-en-ro"")
forward pass
model(**inputs)

Generation

While generating the target text set the decoder_start_token_id to the target language id. The following
  example shows how to translate English to Romanian using the facebook/mbart-large-en-ro model.
thon

from transformers import MBartForConditionalGeneration, MBartTokenizer
tokenizer = MBartTokenizer.from_pretrained(""facebook/mbart-large-en-ro"", src_lang=""en_XX"")
article = ""UN Chief Says There Is No Military Solution in Syria""
inputs = tokenizer(article, return_tensors=""pt"")
translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id[""ro_RO""])
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
""Şeful ONU declară că nu există o soluţie militară în Siria""

Overview of MBart-50
MBart-50 was introduced in the Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav
Chaudhary, Jiatao Gu, Angela Fan. MBart-50 is created using the original mbart-large-cc25 checkpoint by extendeding
its embedding layers with randomly initialized vectors for an extra set of 25 language tokens and then pretrained on 50
languages.
According to the abstract
Multilingual translation models can be created through multilingual finetuning. Instead of finetuning on one
direction, a pretrained model is finetuned on many directions at the same time. It demonstrates that pretrained models
can be extended to incorporate additional languages without loss of performance. Multilingual finetuning improves on
average 1 BLEU over the strongest baselines (being either multilingual from scratch or bilingual finetuning) while
improving 9.3 BLEU on average over bilingual baselines from scratch.
Training of MBart-50
The text format for MBart-50 is slightly different from mBART. For MBart-50 the language id token is used as a prefix
for both source and target text i.e the text format is [lang_code] X [eos], where lang_code is source
language id for source text and target language id for target text, with X being the source or target text
respectively.
MBart-50 has its own tokenizer [MBart50Tokenizer].

Supervised training

thon
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
model = MBartForConditionalGeneration.from_pretrained(""facebook/mbart-large-50"")
tokenizer = MBart50TokenizerFast.from_pretrained(""facebook/mbart-large-50"", src_lang=""en_XX"", tgt_lang=""ro_RO"")
src_text = "" UN Chief Says There Is No Military Solution in Syria""
tgt_text = ""Şeful ONU declară că nu există o soluţie militară în Siria""
model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors=""pt"")
model(**model_inputs)  # forward pass

Generation

To generate using the mBART-50 multilingual translation models, eos_token_id is used as the
  decoder_start_token_id and the target language id is forced as the first generated token. To force the
  target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method.
  The following example shows how to translate between Hindi to French and Arabic to English using the
  facebook/mbart-50-large-many-to-many checkpoint.
thon
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
article_hi = ""संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है""
article_ar = ""الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا.""
model = MBartForConditionalGeneration.from_pretrained(""facebook/mbart-large-50-many-to-many-mmt"")
tokenizer = MBart50TokenizerFast.from_pretrained(""facebook/mbart-large-50-many-to-many-mmt"")
translate Hindi to French
tokenizer.src_lang = ""hi_IN""
encoded_hi = tokenizer(article_hi, return_tensors=""pt"")
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id[""fr_XX""])
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
=> ""Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire en Syria.""
translate Arabic to English
tokenizer.src_lang = ""ar_AR""
encoded_ar = tokenizer(article_ar, return_tensors=""pt"")
generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id[""en_XX""])
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
=> ""The Secretary-General of the United Nations says there is no military solution in Syria.""

Documentation resources

Text classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Translation task guide
Summarization task guide

MBartConfig
[[autodoc]] MBartConfig
MBartTokenizer
[[autodoc]] MBartTokenizer
    - build_inputs_with_special_tokens
MBartTokenizerFast
[[autodoc]] MBartTokenizerFast
MBart50Tokenizer
[[autodoc]] MBart50Tokenizer
MBart50TokenizerFast
[[autodoc]] MBart50TokenizerFast
MBartModel
[[autodoc]] MBartModel
MBartForConditionalGeneration
[[autodoc]] MBartForConditionalGeneration
MBartForQuestionAnswering
[[autodoc]] MBartForQuestionAnswering
MBartForSequenceClassification
[[autodoc]] MBartForSequenceClassification
MBartForCausalLM
[[autodoc]] MBartForCausalLM
    - forward
TFMBartModel
[[autodoc]] TFMBartModel
    - call
TFMBartForConditionalGeneration
[[autodoc]] TFMBartForConditionalGeneration
    - call
FlaxMBartModel
[[autodoc]] FlaxMBartModel
    - call
    - encode
    - decode
FlaxMBartForConditionalGeneration
[[autodoc]] FlaxMBartForConditionalGeneration
    - call
    - encode
    - decode
FlaxMBartForSequenceClassification
[[autodoc]] FlaxMBartForSequenceClassification
    - call
    - encode
    - decode
FlaxMBartForQuestionAnswering
[[autodoc]] FlaxMBartForQuestionAnswering
    - call
    - encode
    - decode"
"
BLOOM
Overview
The BLOOM model has been proposed with its various versions through the BigScience Workshop. BigScience is inspired by other open science initiatives where researchers have pooled their time and resources to collectively achieve a higher impact.
The architecture of BLOOM is essentially similar to GPT3 (auto-regressive model for next token prediction), but has been trained on 46 different languages and 13 programming languages.
Several smaller versions of the models have been trained on the same dataset. BLOOM is available in the following versions:

bloom-560m
bloom-1b1
bloom-1b7
bloom-3b
bloom-7b1
bloom (176B parameters)

Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BLOOM. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

[BloomForCausalLM] is supported by this causal language modeling example script and notebook.

See also:
- Causal language modeling task guide
- Text classification task guide
- Token classification task guide
- Question answering task guide
⚡️ Inference
- A blog on Optimization story: Bloom inference.
- A blog on Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate.
⚙️ Training
- A blog on The Technology Behind BLOOM Training.
BloomConfig
[[autodoc]] BloomConfig
    - all
BloomModel
[[autodoc]] BloomModel
    - forward
BloomTokenizerFast
[[autodoc]] BloomTokenizerFast
    - all
BloomForCausalLM
[[autodoc]] BloomForCausalLM
    - forward
BloomForSequenceClassification
[[autodoc]] BloomForSequenceClassification
    - forward
BloomForTokenClassification
[[autodoc]] BloomForTokenClassification
    - forward
BloomForQuestionAnswering
[[autodoc]] BloomForQuestionAnswering
    - forward"
"
Llama2
Overview
The Llama2 model was proposed in LLaMA: Open Foundation and Fine-Tuned Chat Models by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom. It is a collection of foundation language models ranging from 7B to 70B parameters, with checkpoints finetuned for chat application!
The abstract from the paper is the following:
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.
Checkout all Llama2 models here
Tips:

Weights for the Llama2 models can be obtained by filling out this form
The architecture is very similar to the first Llama, with the addition of Groupe Query Attention (GQA) following this paper
Setting config.pretraining_tp to a value different than 1 will activate the more accurate but slower computation of the linear layers, which should better match the original logits.
The original model uses pad_id = -1 which means that there is no padding token. We can't have the same logic, make sure to add a padding token using tokenizer.add_special_tokens({""pad_token"":""<pad>""}) and resize the token embedding accordingly. You should also set the model.config.pad_token_id. The embed_tokens layer of the model is initialized with self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.config.padding_idx), which makes sure that encoding the padding token will output zeros, so passing it when initializing is recommended.
After filling out the form and gaining access to the model checkpoints, you should be able to use the already converted checkpoints. Otherwise, if you are converting your own model, feel free to use the conversion script. The script can be called with the following (example) command:

python src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path

After conversion, the model and tokenizer can be loaded via:

thon
from transformers import LlamaForCausalLM, LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained(""/output/path"")
model = LlamaForCausalLM.from_pretrained(""/output/path"")

Note that executing the script requires enough CPU RAM to host the whole model in float16 precision (even if the biggest versions
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM). For the 75B model, it's thus 145GB of RAM needed.

The LLaMA tokenizer is a BPE model based on sentencepiece. One quirk of sentencepiece is that when decoding a sequence, if the first token is the start of the word (e.g. ""Banana""), the tokenizer does not prepend the prefix space to the string.

This model was contributed by Arthur Zucker with contributions from Lysandre Debut. The code of the implementation in Hugging Face is based on GPT-NeoX here. The original code of the authors can be found here.
LlamaConfig
[[autodoc]] LlamaConfig
LlamaTokenizer
[[autodoc]] LlamaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
LlamaTokenizerFast
[[autodoc]] LlamaTokenizerFast
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - update_post_processor
    - save_vocabulary
LlamaModel
[[autodoc]] LlamaModel
    - forward
LlamaForCausalLM
[[autodoc]] LlamaForCausalLM
    - forward
LlamaForSequenceClassification
[[autodoc]] LlamaForSequenceClassification
    - forward"
"
OWL-ViT
Overview
The OWL-ViT (short for Vision Transformer for Open-World Localization) was proposed in Simple Open-Vocabulary Object Detection with Vision Transformers by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby. OWL-ViT is an open-vocabulary object detection network trained on a variety of (image, text) pairs. It can be used to query an image with one or multiple text queries to search for and detect target objects described in text.
The abstract from the paper is the following:
Combining simple architectures with large-scale pre-training has led to massive improvements in image classification. For object detection, pre-training and scaling approaches are less well established, especially in the long-tailed and open-vocabulary setting, where training data is relatively scarce. In this paper, we propose a strong recipe for transferring image-text models to open-vocabulary object detection. We use a standard Vision Transformer architecture with minimal modifications, contrastive image-text pre-training, and end-to-end detection fine-tuning. Our analysis of the scaling properties of this setup shows that increasing image-level pre-training and model size yield consistent improvements on the downstream detection task. We provide the adaptation strategies and regularizations needed to attain very strong performance on zero-shot text-conditioned and one-shot image-conditioned object detection. Code and models are available on GitHub.
Usage
OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection.
[OwlViTImageProcessor] can be used to resize (or rescale) and normalize images for the model and [CLIPTokenizer] is used to encode the text. [OwlViTProcessor] wraps [OwlViTImageProcessor] and [CLIPTokenizer] into a single instance to both encode the text and prepare the images. The following example shows how to perform object detection using [OwlViTProcessor] and [OwlViTForObjectDetection].
thon

import requests
from PIL import Image
import torch
from transformers import OwlViTProcessor, OwlViTForObjectDetection
processor = OwlViTProcessor.from_pretrained(""google/owlvit-base-patch32"")
model = OwlViTForObjectDetection.from_pretrained(""google/owlvit-base-patch32"")
url = ""http://images.cocodataset.org/val2017/000000039769.jpg""
image = Image.open(requests.get(url, stream=True).raw)
texts = [[""a photo of a cat"", ""a photo of a dog""]]
inputs = processor(text=texts, images=image, return_tensors=""pt"")
outputs = model(**inputs)
Target image sizes (height, width) to rescale box predictions [batch_size, 2]
target_sizes = torch.Tensor([image.size[::-1]])
Convert outputs (bounding boxes and class logits) to COCO API
results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.1)
i = 0  # Retrieve predictions for the first image for the corresponding text queries
text = texts[i]
boxes, scores, labels = results[i][""boxes""], results[i][""scores""], results[i][""labels""]
for box, score, label in zip(boxes, scores, labels):
     box = [round(i, 2) for i in box.tolist()]
     print(f""Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}"")
Detected a photo of a cat with confidence 0.707 at location [324.97, 20.44, 640.58, 373.29]
Detected a photo of a cat with confidence 0.717 at location [1.46, 55.26, 315.55, 472.17]

This model was contributed by adirik. The original code can be found here.
OwlViTConfig
[[autodoc]] OwlViTConfig
    - from_text_vision_configs
OwlViTTextConfig
[[autodoc]] OwlViTTextConfig
OwlViTVisionConfig
[[autodoc]] OwlViTVisionConfig
OwlViTImageProcessor
[[autodoc]] OwlViTImageProcessor
    - preprocess
    - post_process_object_detection
    - post_process_image_guided_detection
OwlViTFeatureExtractor
[[autodoc]] OwlViTFeatureExtractor
    - call
    - post_process
    - post_process_image_guided_detection
OwlViTProcessor
[[autodoc]] OwlViTProcessor
OwlViTModel
[[autodoc]] OwlViTModel
    - forward
    - get_text_features
    - get_image_features
OwlViTTextModel
[[autodoc]] OwlViTTextModel
    - forward
OwlViTVisionModel
[[autodoc]] OwlViTVisionModel
    - forward
OwlViTForObjectDetection
[[autodoc]] OwlViTForObjectDetection
    - forward
    - image_guided_detection"
"
MarkupLM
Overview
The MarkupLM model was proposed in MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document
Understanding by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei. MarkupLM is BERT, but
applied to HTML pages instead of raw text documents. The model incorporates additional embedding layers to improve
performance, similar to LayoutLM.
The model can be used for tasks like question answering on web pages or information extraction from web pages. It obtains
state-of-the-art results on 2 important benchmarks:
- WebSRC, a dataset for Web-Based Structural Reading Comprehension (a bit like SQuAD but for web pages)
- SWDE, a dataset
for information extraction from web pages (basically named-entity recogntion on web pages)
The abstract from the paper is the following:
Multimodal pre-training with text, layout, and image has made significant progress for Visually-rich Document
Understanding (VrDU), especially the fixed-layout documents such as scanned document images. While, there are still a
large number of digital documents where the layout information is not fixed and needs to be interactively and
dynamically rendered for visualization, making existing layout-based pre-training approaches not easy to apply. In this
paper, we propose MarkupLM for document understanding tasks with markup languages as the backbone such as
HTML/XML-based documents, where text and markup information is jointly pre-trained. Experiment results show that the
pre-trained MarkupLM significantly outperforms the existing strong baseline models on several document understanding
tasks. The pre-trained model and code will be publicly available.
Tips:
- In addition to input_ids, [~MarkupLMModel.forward] expects 2 additional inputs, namely xpath_tags_seq and xpath_subs_seq.
These are the XPATH tags and subscripts respectively for each token in the input sequence.
- One can use [MarkupLMProcessor] to prepare all data for the model. Refer to the usage guide for more info.
- Demo notebooks can be found here.
 
 MarkupLM architecture. Taken from the original paper. 
This model was contributed by nielsr. The original code can be found here.
Usage: MarkupLMProcessor
The easiest way to prepare data for the model is to use [MarkupLMProcessor], which internally combines a feature extractor
([MarkupLMFeatureExtractor]) and a tokenizer ([MarkupLMTokenizer] or [MarkupLMTokenizerFast]). The feature extractor is
used to extract all nodes and xpaths from the HTML strings, which are then provided to the tokenizer, which turns them into the
token-level inputs of the model (input_ids etc.). Note that you can still use the feature extractor and tokenizer separately,
if you only want to handle one of the two tasks.
thon
from transformers import MarkupLMFeatureExtractor, MarkupLMTokenizerFast, MarkupLMProcessor
feature_extractor = MarkupLMFeatureExtractor()
tokenizer = MarkupLMTokenizerFast.from_pretrained(""microsoft/markuplm-base"")
processor = MarkupLMProcessor(feature_extractor, tokenizer)

In short, one can provide HTML strings (and possibly additional data) to [MarkupLMProcessor],
and it will create the inputs expected by the model. Internally, the processor first uses
[MarkupLMFeatureExtractor] to get a list of nodes and corresponding xpaths. The nodes and
xpaths are then provided to [MarkupLMTokenizer] or [MarkupLMTokenizerFast], which converts them
to token-level input_ids, attention_mask, token_type_ids, xpath_subs_seq, xpath_tags_seq.
Optionally, one can provide node labels to the processor, which are turned into token-level labels.
[MarkupLMFeatureExtractor] uses Beautiful Soup, a Python library for
pulling data out of HTML and XML files, under the hood. Note that you can still use your own parsing solution of
choice, and provide the nodes and xpaths yourself to [MarkupLMTokenizer] or [MarkupLMTokenizerFast].
In total, there are 5 use cases that are supported by the processor. Below, we list them all. Note that each of these
use cases work for both batched and non-batched inputs (we illustrate them for non-batched inputs).
Use case 1: web page classification (training, inference) + token classification (inference), parse_html = True
This is the simplest case, in which the processor will use the feature extractor to get all nodes and xpaths from the HTML.
thon

from transformers import MarkupLMProcessor
processor = MarkupLMProcessor.from_pretrained(""microsoft/markuplm-base"")
html_string = """"""
  <!DOCTYPE html>
  
  
  Hello world
  
  
  Welcome
  Here is my website.
  
  """"""
note that you can also add provide all tokenizer parameters here such as padding, truncation
encoding = processor(html_string, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

Use case 2: web page classification (training, inference) + token classification (inference), parse_html=False
In case one already has obtained all nodes and xpaths, one doesn't need the feature extractor. In that case, one should
provide the nodes and corresponding xpaths themselves to the processor, and make sure to set parse_html to False.
thon

from transformers import MarkupLMProcessor
processor = MarkupLMProcessor.from_pretrained(""microsoft/markuplm-base"")
processor.parse_html = False
nodes = [""hello"", ""world"", ""how"", ""are""]
xpaths = [""/html/body/div/li[1]/div/span"", ""/html/body/div/li[1]/div/span"", ""html/body"", ""html/body/div""]
encoding = processor(nodes=nodes, xpaths=xpaths, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

Use case 3: token classification (training), parse_html=False
For token classification tasks (such as SWDE), one can also provide the
corresponding node labels in order to train a model. The processor will then convert these into token-level labels.
By default, it will only label the first wordpiece of a word, and label the remaining wordpieces with -100, which is the
ignore_index of PyTorch's CrossEntropyLoss. In case you want all wordpieces of a word to be labeled, you can
initialize the tokenizer with only_label_first_subword set to False.
thon

from transformers import MarkupLMProcessor
processor = MarkupLMProcessor.from_pretrained(""microsoft/markuplm-base"")
processor.parse_html = False
nodes = [""hello"", ""world"", ""how"", ""are""]
xpaths = [""/html/body/div/li[1]/div/span"", ""/html/body/div/li[1]/div/span"", ""html/body"", ""html/body/div""]
node_labels = [1, 2, 2, 1]
encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq', 'labels'])

Use case 4: web page question answering (inference), parse_html=True
For question answering tasks on web pages, you can provide a question to the processor. By default, the
processor will use the feature extractor to get all nodes and xpaths, and create [CLS] question tokens [SEP] word tokens [SEP].
thon

from transformers import MarkupLMProcessor
processor = MarkupLMProcessor.from_pretrained(""microsoft/markuplm-base"")
html_string = """"""
  <!DOCTYPE html>
  
  
  Hello world
  
  
  Welcome
  My name is Niels.
  
  """"""
question = ""What's his name?""
encoding = processor(html_string, questions=question, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

Use case 5: web page question answering (inference), parse_html=False
For question answering tasks (such as WebSRC), you can provide a question to the processor. If you have extracted
all nodes and xpaths yourself, you can provide them directly to the processor. Make sure to set parse_html to False.
thon

from transformers import MarkupLMProcessor
processor = MarkupLMProcessor.from_pretrained(""microsoft/markuplm-base"")
processor.parse_html = False
nodes = [""hello"", ""world"", ""how"", ""are""]
xpaths = [""/html/body/div/li[1]/div/span"", ""/html/body/div/li[1]/div/span"", ""html/body"", ""html/body/div""]
question = ""What's his name?""
encoding = processor(nodes=nodes, xpaths=xpaths, questions=question, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide

MarkupLMConfig
[[autodoc]] MarkupLMConfig
    - all
MarkupLMFeatureExtractor
[[autodoc]] MarkupLMFeatureExtractor
    - call
MarkupLMTokenizer
[[autodoc]] MarkupLMTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
MarkupLMTokenizerFast
[[autodoc]] MarkupLMTokenizerFast
    - all
MarkupLMProcessor
[[autodoc]] MarkupLMProcessor
    - call
MarkupLMModel
[[autodoc]] MarkupLMModel
    - forward
MarkupLMForSequenceClassification
[[autodoc]] MarkupLMForSequenceClassification
    - forward
MarkupLMForTokenClassification
[[autodoc]] MarkupLMForTokenClassification
    - forward
MarkupLMForQuestionAnswering
[[autodoc]] MarkupLMForQuestionAnswering
    - forward"
"
ERNIE
Overview
ERNIE is a series of powerful models proposed by baidu, especially in Chinese tasks,
including ERNIE1.0, ERNIE2.0,
ERNIE3.0, ERNIE-Gram, ERNIE-health, etc.
These models are contributed by nghuyong and the official code can be found in PaddleNLP (in PaddlePaddle).
How to use
Take ernie-1.0-base-zh as an example:
Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained(""nghuyong/ernie-1.0-base-zh"")
model = AutoModel.from_pretrained(""nghuyong/ernie-1.0-base-zh"")
Supported Models
|     Model Name      | Language |           Description           |
|:-------------------:|:--------:|:-------------------------------:|
|  ernie-1.0-base-zh  | Chinese  | Layer:12, Heads:12, Hidden:768  |
|  ernie-2.0-base-en  | English  | Layer:12, Heads:12, Hidden:768  |
| ernie-2.0-large-en  | English  | Layer:24, Heads:16, Hidden:1024 |
|  ernie-3.0-base-zh  | Chinese  | Layer:12, Heads:12, Hidden:768  |
| ernie-3.0-medium-zh | Chinese  |  Layer:6, Heads:12, Hidden:768  |
|  ernie-3.0-mini-zh  | Chinese  |  Layer:6, Heads:12, Hidden:384  |
| ernie-3.0-micro-zh  | Chinese  |  Layer:4, Heads:12, Hidden:384  |
|  ernie-3.0-nano-zh  | Chinese  |  Layer:4, Heads:12, Hidden:312  |
|   ernie-health-zh   | Chinese  | Layer:12, Heads:12, Hidden:768  |
|    ernie-gram-zh    | Chinese  | Layer:12, Heads:12, Hidden:768  |
You can find all the supported models from huggingface's model hub: huggingface.co/nghuyong, and model details from paddle's official
repo: PaddleNLP
and ERNIE.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

ErnieConfig
[[autodoc]] ErnieConfig
    - all
Ernie specific outputs
[[autodoc]] models.ernie.modeling_ernie.ErnieForPreTrainingOutput
ErnieModel
[[autodoc]] ErnieModel
    - forward
ErnieForPreTraining
[[autodoc]] ErnieForPreTraining
    - forward
ErnieForCausalLM
[[autodoc]] ErnieForCausalLM
    - forward
ErnieForMaskedLM
[[autodoc]] ErnieForMaskedLM
    - forward
ErnieForNextSentencePrediction
[[autodoc]] ErnieForNextSentencePrediction
    - forward
ErnieForSequenceClassification
[[autodoc]] ErnieForSequenceClassification
    - forward
ErnieForMultipleChoice
[[autodoc]] ErnieForMultipleChoice
    - forward
ErnieForTokenClassification
[[autodoc]] ErnieForTokenClassification
    - forward
ErnieForQuestionAnswering
[[autodoc]] ErnieForQuestionAnswering
    - forward"
"
ConvNeXt V2
Overview
The ConvNeXt V2 model was proposed in ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
ConvNeXt V2 is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, and a successor of ConvNeXT.
The abstract from the paper is the following:
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked  autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.
Tips:

See the code examples below each model regarding usage.

 ConvNeXt V2 architecture. Taken from the original paper.
This model was contributed by adirik. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXt V2.

[ConvNextV2ForImageClassification] is supported by this example script and notebook.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ConvNextV2Config
[[autodoc]] ConvNextV2Config
ConvNextV2Model
[[autodoc]] ConvNextV2Model
    - forward
ConvNextV2ForImageClassification
[[autodoc]] ConvNextV2ForImageClassification
    - forward"
"
SwitchTransformers
Overview
The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer.
The Switch Transformer model uses a sparse T5 encoder-decoder architecture, where the MLP are replaced by a Mixture of Experts (MoE). A routing mechanism (top 1 in this case) associates each token to one of the expert, where each expert is a dense MLP. While switch transformers have a lot more weights than their equivalent dense models, the sparsity allows better scaling and better finetuning performance at scale.
During a forward pass, only a fraction of the weights are used. The routing mechanism allows the model to select relevant weights on the fly which increases the model capacity without increasing the number of operations.
The abstract from the paper is the following:
In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model -- with outrageous numbers of parameters -- but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability -- we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the ""Colossal Clean Crawled Corpus"" and achieve a 4x speedup over the T5-XXL model.
Tips:

SwitchTransformers uses the [T5Tokenizer], which can be loaded directly from each model's repository.
The released weights are pretrained on English Masked Language Modeling task, and should be finetuned.

This model was contributed by Younes Belkada and Arthur Zucker .
The original code can be found here.
Resources

Translation task guide
Summarization task guide

SwitchTransformersConfig
[[autodoc]] SwitchTransformersConfig
SwitchTransformersTop1Router
[[autodoc]] SwitchTransformersTop1Router
    - _compute_router_probabilities
    - forward
SwitchTransformersSparseMLP
[[autodoc]] SwitchTransformersSparseMLP
    - forward
SwitchTransformersModel
[[autodoc]] SwitchTransformersModel
    - forward
SwitchTransformersForConditionalGeneration
[[autodoc]] SwitchTransformersForConditionalGeneration
    - forward
SwitchTransformersEncoderModel
[[autodoc]] SwitchTransformersEncoderModel
    - forward"
"
MEGA
Overview
The MEGA model was proposed in Mega: Moving Average Equipped Gated Attention by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
MEGA proposes a new approach to self-attention with each encoder layer having a multi-headed exponential moving average in addition to a single head of standard dot-product attention, giving the attention mechanism 
stronger positional biases. This allows MEGA to perform competitively to Transformers on standard benchmarks including LRA 
while also having significantly fewer parameters. MEGA's compute efficiency allows it to scale to very long sequences, making it an 
attractive option for long-document NLP tasks.
The abstract from the paper is the following:
*The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences. In this paper, we introduce Mega, a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential) moving average to incorporate inductive bias of position-aware local dependencies into the position-agnostic attention mechanism. We further propose a variant of Mega that offers linear time and space complexity yet yields only minimal quality loss, by efficiently splitting the whole sequence into multiple chunks with fixed length. Extensive experiments on a wide range of sequence modeling benchmarks, including the Long Range Arena, neural machine translation, auto-regressive language modeling, and image and speech classification, show that Mega achieves significant improvements over other sequence models, including variants of Transformers and recent state space models. *
Tips:

MEGA can perform quite well with relatively few parameters. See Appendix D in the MEGA paper for examples of architectural specs which perform well in various settings. If using MEGA as a decoder, be sure to set bidirectional=False to avoid errors with default bidirectional. 
Mega-chunk is a variant of mega that reduces time and spaces complexity from quadratic to linear. Utilize chunking with MegaConfig.use_chunking and control chunk size with MegaConfig.chunk_size 

This model was contributed by mnaylor.
The original code can be found here.
Implementation Notes:

The original implementation of MEGA had an inconsistent expectation of attention masks for padding and causal self-attention between the softmax attention and Laplace/squared ReLU method. This implementation addresses that inconsistency.
The original implementation did not include token type embeddings; this implementation adds support for these, with the option controlled by MegaConfig.add_token_type_embeddings

MegaConfig
[[autodoc]] MegaConfig
MegaModel
[[autodoc]] MegaModel
    - forward
MegaForCausalLM
[[autodoc]] MegaForCausalLM
    - forward
MegaForMaskedLM
[[autodoc]] MegaForMaskedLM
    - forward
MegaForSequenceClassification
[[autodoc]] MegaForSequenceClassification
    - forward
MegaForMultipleChoice
[[autodoc]] MegaForMultipleChoice
    - forward
MegaForTokenClassification
[[autodoc]] MegaForTokenClassification
    - forward
MegaForQuestionAnswering
[[autodoc]] MegaForQuestionAnswering
    - forward"
"
REALM
Overview
The REALM model was proposed in REALM: Retrieval-Augmented Language Model Pre-Training by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang. It's a
retrieval-augmented language model that firstly retrieves documents from a textual knowledge corpus and then
utilizes retrieved documents to process question answering tasks.
The abstract from the paper is the following:
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks
such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network,
requiring ever-larger networks to cover more facts. To capture knowledge in a more modular and interpretable way, we
augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend
over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. For the
first time, we show how to pre-train such a knowledge retriever in an unsupervised manner, using masked language
modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents. We
demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the
challenging task of Open-domain Question Answering (Open-QA). We compare against state-of-the-art models for both
explicit and implicit knowledge storage on three popular Open-QA benchmarks, and find that we outperform all previous
methods by a significant margin (4-16% absolute accuracy), while also providing qualitative benefits such as
interpretability and modularity.
This model was contributed by qqaatw. The original code can be found
here.
RealmConfig
[[autodoc]] RealmConfig
RealmTokenizer
[[autodoc]] RealmTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
    - batch_encode_candidates
RealmTokenizerFast
[[autodoc]] RealmTokenizerFast
    - batch_encode_candidates
RealmRetriever
[[autodoc]] RealmRetriever
RealmEmbedder
[[autodoc]] RealmEmbedder
    - forward
RealmScorer
[[autodoc]] RealmScorer
    - forward
RealmKnowledgeAugEncoder
[[autodoc]] RealmKnowledgeAugEncoder
    - forward
RealmReader
[[autodoc]] RealmReader
    - forward
RealmForOpenQA
[[autodoc]] RealmForOpenQA
    - block_embedding_to
    - forward"
"
XGLM
Overview
The XGLM model was proposed in Few-shot Learning with Multilingual Language Models
by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, 
Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, 
Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
The abstract from the paper is the following:
Large-scale autoregressive language models such as GPT-3 are few-shot learners that can perform a wide range of language 
tasks without fine-tuning. While these models are known to be able to jointly represent many different languages, 
their training data is dominated by English, potentially limiting their cross-lingual generalization. 
In this work, we train multilingual autoregressive language models on a balanced corpus covering a diverse set of languages, 
and study their few- and zero-shot learning capabilities in a wide range of tasks. Our largest model with 7.5 billion parameters 
sets new state of the art in few-shot learning in more than 20 representative languages, outperforming GPT-3 of comparable size 
in multilingual commonsense reasoning (with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in 4-shot settings) 
and natural language inference (+5.4% in each of 0-shot and 4-shot settings). On the FLORES-101 machine translation benchmark, 
our model outperforms GPT-3 on 171 out of 182 translation directions with 32 training examples, while surpassing the 
official supervised baseline in 45 directions. We present a detailed analysis of where the model succeeds and fails, 
showing in particular that it enables cross-lingual in-context learning on some tasks, while there is still room for improvement 
on surface form robustness and adaptation to tasks that do not have a natural cloze form. Finally, we evaluate our models 
in social value tasks such as hate speech detection in five languages and find it has limitations similar to comparable sized GPT-3 models.
This model was contributed by Suraj. The original code can be found here.
Documentation resources

Causal language modeling task guide

XGLMConfig
[[autodoc]] XGLMConfig
XGLMTokenizer
[[autodoc]] XGLMTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
XGLMTokenizerFast
[[autodoc]] XGLMTokenizerFast
XGLMModel
[[autodoc]] XGLMModel
    - forward
XGLMForCausalLM
[[autodoc]] XGLMForCausalLM
    - forward
TFXGLMModel
[[autodoc]] TFXGLMModel
    - call
TFXGLMForCausalLM
[[autodoc]] TFXGLMForCausalLM
    - call
FlaxXGLMModel
[[autodoc]] FlaxXGLMModel
    - call
FlaxXGLMForCausalLM
[[autodoc]] FlaxXGLMForCausalLM
    - call"
"
Splinter
Overview
The Splinter model was proposed in Few-Shot Question Answering by Pretraining Span Selection by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy. Splinter
is an encoder-only transformer (similar to BERT) pretrained using the recurring span selection task on a large corpus
comprising Wikipedia and the Toronto Book Corpus.
The abstract from the paper is the following:
In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order
of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where only a few hundred
training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between
current pretraining objectives and question answering. We propose a new pretraining scheme tailored for question
answering: recurring span selection. Given a passage with multiple sets of recurring spans, we mask in each set all
recurring spans but one, and ask the model to select the correct span in the passage for each masked span. Masked spans
are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select
the answer span. The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD
with only 128 training examples), while maintaining competitive performance in the high-resource setting.
Tips:

Splinter was trained to predict answers spans conditioned on a special [QUESTION] token. These tokens contextualize
  to question representations which are used to predict the answers. This layer is called QASS, and is the default
  behaviour in the [SplinterForQuestionAnswering] class. Therefore:
Use [SplinterTokenizer] (rather than [BertTokenizer]), as it already
  contains this special token. Also, its default behavior is to use this token when two sequences are given (for
  example, in the run_qa.py script).
If you plan on using Splinter outside run_qa.py, please keep in mind the question token - it might be important for
  the success of your model, especially in a few-shot setting.
Please note there are two different checkpoints for each size of Splinter. Both are basically the same, except that
  one also has the pretrained weights of the QASS layer (tau/splinter-base-qass and tau/splinter-large-qass) and one
  doesn't (tau/splinter-base and tau/splinter-large). This is done to support randomly initializing this layer at
  fine-tuning, as it is shown to yield better results for some cases in the paper.

This model was contributed by yuvalkirstain and oriram. The original code can be found here.
Documentation resources

Question answering task guide

SplinterConfig
[[autodoc]] SplinterConfig
SplinterTokenizer
[[autodoc]] SplinterTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
SplinterTokenizerFast
[[autodoc]] SplinterTokenizerFast
SplinterModel
[[autodoc]] SplinterModel
    - forward
SplinterForQuestionAnswering
[[autodoc]] SplinterForQuestionAnswering
    - forward
SplinterForPreTraining
[[autodoc]] SplinterForPreTraining
    - forward"
"
DistilBERT

Overview
The DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
distilled version of BERT, and the paper DistilBERT, a
distilled version of BERT: smaller, faster, cheaper and lighter. DistilBERT is a
small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
bert-base-uncased, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language
understanding benchmark.
The abstract from the paper is the following:
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
operating these large models in on-the-edge and/or under constrained computational training or inference budgets
remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
study.
Tips:

DistilBERT doesn't have token_type_ids, you don't need to indicate which token belongs to which segment. Just
  separate your segments with the separation token tokenizer.sep_token (or [SEP]).
DistilBERT doesn't have options to select the input positions (position_ids input). This could be added if
  necessary though, just let us know if you need this option.

Same as BERT but smaller. Trained by distillation of the pretrained BERT model, meaning it’s been trained to predict the same probabilities as the larger model. The actual objective is a combination of:

finding the same probabilities as the teacher model
predicting the masked tokens correctly (but no next-sentence objective)
a cosine similarity between the hidden states of the student and the teacher model

This model was contributed by victorsanh. This model jax version was
contributed by kamalkraj. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DistilBERT. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog post on Getting Started with Sentiment Analysis using Python with DistilBERT.
A blog post on how to train DistilBERT with Blurr for sequence classification.
A blog post on how to use Ray to tune DistilBERT hyperparameters.
A blog post on how to train DistilBERT with Hugging Face and Amazon SageMaker.
A notebook on how to finetune DistilBERT for multi-label classification. 🌎
A notebook on how to finetune DistilBERT for multiclass classification with PyTorch. 🌎
A notebook on how to finetune DistilBERT for text classification in TensorFlow. 🌎
[DistilBertForSequenceClassification] is supported by this example script and notebook.
[TFDistilBertForSequenceClassification] is supported by this example script and notebook.
[FlaxDistilBertForSequenceClassification] is supported by this example script and notebook.
Text classification task guide

[DistilBertForTokenClassification] is supported by this example script and notebook.
[TFDistilBertForTokenClassification] is supported by this example script and notebook.
[FlaxDistilBertForTokenClassification] is supported by this example script.
Token classification chapter of the 🤗 Hugging Face Course.
Token classification task guide

[DistilBertForMaskedLM] is supported by this example script and notebook.
[TFDistilBertForMaskedLM] is supported by this example script and notebook.
[FlaxDistilBertForMaskedLM] is supported by this example script and notebook.
Masked language modeling chapter of the 🤗 Hugging Face Course.
Masked language modeling task guide

[DistilBertForQuestionAnswering] is supported by this example script and notebook.
[TFDistilBertForQuestionAnswering] is supported by this example script and notebook.
[FlaxDistilBertForQuestionAnswering] is supported by this example script.
Question answering chapter of the 🤗 Hugging Face Course.
Question answering task guide

Multiple choice
- [DistilBertForMultipleChoice] is supported by this example script and notebook.
- [TFDistilBertForMultipleChoice] is supported by this example script and notebook.
- Multiple choice task guide
⚗️ Optimization

A blog post on how to quantize DistilBERT with 🤗 Optimum and Intel.
A blog post on how Optimizing Transformers for GPUs with 🤗 Optimum.
A blog post on Optimizing Transformers with Hugging Face Optimum.

⚡️ Inference

A blog post on how to Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia with DistilBERT.
A blog post on Serverless Inference with Hugging Face's Transformers, DistilBERT and Amazon SageMaker.

🚀 Deploy

A blog post on how to deploy DistilBERT on Google Cloud.
A blog post on how to deploy DistilBERT with Amazon SageMaker.
A blog post on how to Deploy BERT with Hugging Face Transformers, Amazon SageMaker and Terraform module.

DistilBertConfig
[[autodoc]] DistilBertConfig
DistilBertTokenizer
[[autodoc]] DistilBertTokenizer
DistilBertTokenizerFast
[[autodoc]] DistilBertTokenizerFast
DistilBertModel
[[autodoc]] DistilBertModel
    - forward
DistilBertForMaskedLM
[[autodoc]] DistilBertForMaskedLM
    - forward
DistilBertForSequenceClassification
[[autodoc]] DistilBertForSequenceClassification
    - forward
DistilBertForMultipleChoice
[[autodoc]] DistilBertForMultipleChoice
    - forward
DistilBertForTokenClassification
[[autodoc]] DistilBertForTokenClassification
    - forward
DistilBertForQuestionAnswering
[[autodoc]] DistilBertForQuestionAnswering
    - forward
TFDistilBertModel
[[autodoc]] TFDistilBertModel
    - call
TFDistilBertForMaskedLM
[[autodoc]] TFDistilBertForMaskedLM
    - call
TFDistilBertForSequenceClassification
[[autodoc]] TFDistilBertForSequenceClassification
    - call
TFDistilBertForMultipleChoice
[[autodoc]] TFDistilBertForMultipleChoice
    - call
TFDistilBertForTokenClassification
[[autodoc]] TFDistilBertForTokenClassification
    - call
TFDistilBertForQuestionAnswering
[[autodoc]] TFDistilBertForQuestionAnswering
    - call
FlaxDistilBertModel
[[autodoc]] FlaxDistilBertModel
    - call
FlaxDistilBertForMaskedLM
[[autodoc]] FlaxDistilBertForMaskedLM
    - call
FlaxDistilBertForSequenceClassification
[[autodoc]] FlaxDistilBertForSequenceClassification
    - call
FlaxDistilBertForMultipleChoice
[[autodoc]] FlaxDistilBertForMultipleChoice
    - call
FlaxDistilBertForTokenClassification
[[autodoc]] FlaxDistilBertForTokenClassification
    - call
FlaxDistilBertForQuestionAnswering
[[autodoc]] FlaxDistilBertForQuestionAnswering
    - call"
"
Speech Encoder Decoder Models
The [SpeechEncoderDecoderModel] can be used to initialize a speech-to-text model
with any pretrained speech autoencoding model as the encoder (e.g. Wav2Vec2, Hubert) and any pretrained autoregressive model as the decoder.
The effectiveness of initializing speech-sequence-to-text-sequence models with pretrained checkpoints for speech
recognition and speech translation has e.g. been shown in Large-Scale Self- and Semi-Supervised Learning for Speech
Translation by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli,
Alexis Conneau.
An example of how to use a [SpeechEncoderDecoderModel] for inference can be seen in Speech2Text2.
Randomly initializing SpeechEncoderDecoderModel from model configurations.
[SpeechEncoderDecoderModel] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [Wav2Vec2Model] configuration for the encoder
and the default [BertForCausalLM] configuration for the decoder.
thon

from transformers import BertConfig, Wav2Vec2Config, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel
config_encoder = Wav2Vec2Config()
config_decoder = BertConfig()
config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
model = SpeechEncoderDecoderModel(config=config)

Initialising SpeechEncoderDecoderModel from a pretrained encoder and a pretrained decoder.
[SpeechEncoderDecoderModel] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained Transformer-based speech model, e.g. Wav2Vec2, Hubert can serve as the encoder and both pretrained auto-encoding models, e.g. BERT, pretrained causal language models, e.g. GPT2, as well as the pretrained decoder part of sequence-to-sequence models, e.g. decoder of BART, can be used as the decoder.
Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized.
Initializing [SpeechEncoderDecoderModel] from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in the Warm-starting-encoder-decoder blog post.
To do so, the SpeechEncoderDecoderModel class provides a [SpeechEncoderDecoderModel.from_encoder_decoder_pretrained] method.
thon

from transformers import SpeechEncoderDecoderModel
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
     ""facebook/hubert-large-ll60k"", ""bert-base-uncased""
 )

Loading an existing SpeechEncoderDecoderModel checkpoint and perform inference.
To load fine-tuned checkpoints of the SpeechEncoderDecoderModel class, [SpeechEncoderDecoderModel] provides the from_pretrained() method just like any other model architecture in Transformers.
To perform inference, one uses the [generate] method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling.
thon

from transformers import Wav2Vec2Processor, SpeechEncoderDecoderModel
from datasets import load_dataset
import torch
load a fine-tuned speech translation model and corresponding processor
model = SpeechEncoderDecoderModel.from_pretrained(""facebook/wav2vec2-xls-r-300m-en-to-15"")
processor = Wav2Vec2Processor.from_pretrained(""facebook/wav2vec2-xls-r-300m-en-to-15"")
let's perform inference on a piece of English speech (which we'll translate to German)
ds = load_dataset(""hf-internal-testing/librispeech_asr_dummy"", ""clean"", split=""validation"")
input_values = processor(ds[0][""audio""][""array""], return_tensors=""pt"").input_values
autoregressively generate transcription (uses greedy decoding by default)
generated_ids = model.generate(input_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
Mr. Quilter ist der Apostel der Mittelschicht und wir freuen uns, sein Evangelium willkommen heißen zu können.

Training
Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model on a dataset of (speech, text) pairs.
As you can see, only 2 inputs are required for the model in order to compute a loss: input_values (which are the
speech inputs) and labels (which are the input_ids of the encoded target sequence).
thon

from transformers import AutoTokenizer, AutoFeatureExtractor, SpeechEncoderDecoderModel
from datasets import load_dataset
encoder_id = ""facebook/wav2vec2-base-960h""  # acoustic model encoder
decoder_id = ""bert-base-uncased""  # text decoder
feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
tokenizer = AutoTokenizer.from_pretrained(decoder_id)
Combine pre-trained encoder and pre-trained decoder to form a Seq2Seq model
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id)
model.config.decoder_start_token_id = tokenizer.cls_token_id
model.config.pad_token_id = tokenizer.pad_token_id
load an audio input and pre-process (normalise mean/std to 0/1)
ds = load_dataset(""hf-internal-testing/librispeech_asr_dummy"", ""clean"", split=""validation"")
input_values = feature_extractor(ds[0][""audio""][""array""], return_tensors=""pt"").input_values
load its corresponding transcription and tokenize to generate labels
labels = tokenizer(ds[0][""text""], return_tensors=""pt"").input_ids
the forward function automatically creates the correct decoder_input_ids
loss = model(**input_features).loss
loss.backward()

SpeechEncoderDecoderConfig
[[autodoc]] SpeechEncoderDecoderConfig
SpeechEncoderDecoderModel
[[autodoc]] SpeechEncoderDecoderModel
    - forward
    - from_encoder_decoder_pretrained
FlaxSpeechEncoderDecoderModel
[[autodoc]] FlaxSpeechEncoderDecoderModel
    - call
    - from_encoder_decoder_pretrained"
"
LiLT
Overview
The LiLT model was proposed in LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding by Jiapeng Wang, Lianwen Jin, Kai Ding.
LiLT allows to combine any pre-trained RoBERTa text encoder with a lightweight Layout Transformer, to enable LayoutLM-like document understanding for many
languages.
The abstract from the paper is the following:
Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure.
Tips:

To combine the Language-Independent Layout Transformer with a new RoBERTa checkpoint from the hub, refer to this guide.
The script will result in config.json and pytorch_model.bin files being stored locally. After doing this, one can do the following (assuming you're logged in with your HuggingFace account):

from transformers import LiltModel
model = LiltModel.from_pretrained(""path_to_your_files"")
model.push_to_hub(""name_of_repo_on_the_hub"")

When preparing data for the model, make sure to use the token vocabulary that corresponds to the RoBERTa checkpoint you combined with the Layout Transformer.
As lilt-roberta-en-base uses the same vocabulary as LayoutLMv3, one can use [LayoutLMv3TokenizerFast] to prepare data for the model.
The same is true for lilt-roberta-en-base: one can use [LayoutXLMTokenizerFast] for that model.

 LiLT architecture. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LiLT.

Demo notebooks for LiLT can be found here.

Documentation resources
- Text classification task guide
- Token classification task guide
- Question answering task guide
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
LiltConfig
[[autodoc]] LiltConfig
LiltModel
[[autodoc]] LiltModel
    - forward
LiltForSequenceClassification
[[autodoc]] LiltForSequenceClassification
    - forward
LiltForTokenClassification
[[autodoc]] LiltForTokenClassification
    - forward
LiltForQuestionAnswering
[[autodoc]] LiltForQuestionAnswering
    - forward"
"
Open-Llama

This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.31.0.
You can do so by running the following command: pip install -U transformers==4.31.0.

This model differs from the OpenLLaMA models on the Hugging Face Hub, which primarily use the LLaMA architecture.

Overview
The Open-Llama model was proposed in Open-Llama project by community developer s-JoL.
The model is mainly based on LLaMA with some modifications, incorporating memory-efficient attention from Xformers, stable embedding from Bloom, and shared input-output embedding from PaLM.
And the model is pre-trained on both Chinese and English, which gives it better performance on Chinese language tasks.
This model was contributed by s-JoL.
The original code can be found Open-Llama.
Checkpoint and usage can be found at s-JoL/Open-Llama-V1.
OpenLlamaConfig
[[autodoc]] OpenLlamaConfig
OpenLlamaModel
[[autodoc]] OpenLlamaModel
    - forward
OpenLlamaForCausalLM
[[autodoc]] OpenLlamaForCausalLM
    - forward
OpenLlamaForSequenceClassification
[[autodoc]] OpenLlamaForSequenceClassification
    - forward"
"
BertJapanese
Overview
The BERT models trained on Japanese text.
There are models with two different tokenization methods:

Tokenize with MeCab and WordPiece. This requires some extra dependencies, fugashi which is a wrapper around MeCab.
Tokenize into characters.

To use MecabTokenizer, you should pip install transformers[""ja""] (or pip install -e .[""ja""] if you install
from source) to install dependencies.
See details on cl-tohoku repository.
Example of using a model with MeCab and WordPiece tokenization:
thon

import torch
from transformers import AutoModel, AutoTokenizer
bertjapanese = AutoModel.from_pretrained(""cl-tohoku/bert-base-japanese"")
tokenizer = AutoTokenizer.from_pretrained(""cl-tohoku/bert-base-japanese"")
Input Japanese Text
line = ""吾輩は猫である。""
inputs = tokenizer(line, return_tensors=""pt"")
print(tokenizer.decode(inputs[""input_ids""][0]))
[CLS] 吾輩 は 猫 で ある 。 [SEP]
outputs = bertjapanese(**inputs)

Example of using a model with Character tokenization:
thon

bertjapanese = AutoModel.from_pretrained(""cl-tohoku/bert-base-japanese-char"")
tokenizer = AutoTokenizer.from_pretrained(""cl-tohoku/bert-base-japanese-char"")
Input Japanese Text
line = ""吾輩は猫である。""
inputs = tokenizer(line, return_tensors=""pt"")
print(tokenizer.decode(inputs[""input_ids""][0]))
[CLS] 吾 輩 は 猫 で あ る 。 [SEP]
outputs = bertjapanese(**inputs)

Tips:

This implementation is the same as BERT, except for tokenization method. Refer to the documentation of BERT for more usage examples.

This model was contributed by cl-tohoku.
BertJapaneseTokenizer
[[autodoc]] BertJapaneseTokenizer"
"
ConvNeXT
Overview
The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.
The abstract from the paper is the following:
The ""Roaring 20s"" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.
A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers
(e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide
variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive
biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually ""modernize"" a standard ResNet toward the design
of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models
dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy
and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.
Tips:

See the code examples below each model regarding usage.

 ConvNeXT architecture. Taken from the original paper.
This model was contributed by nielsr. TensorFlow version of the model was contributed by ariG23498,
gante, and sayakpaul (equal contribution). The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXT.

[ConvNextForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ConvNextConfig
[[autodoc]] ConvNextConfig
ConvNextFeatureExtractor
[[autodoc]] ConvNextFeatureExtractor
ConvNextImageProcessor
[[autodoc]] ConvNextImageProcessor
    - preprocess
ConvNextModel
[[autodoc]] ConvNextModel
    - forward
ConvNextForImageClassification
[[autodoc]] ConvNextForImageClassification
    - forward
TFConvNextModel
[[autodoc]] TFConvNextModel
    - call
TFConvNextForImageClassification
[[autodoc]] TFConvNextForImageClassification
    - call"
"
MobileViTV2
Overview
The MobileViTV2 model was proposed in Separable Self-attention for Mobile Vision Transformers by Sachin Mehta and Mohammad Rastegari.
MobileViTV2 is the second version of MobileViT, constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
The abstract from the paper is the following:
Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across several mobile vision tasks, including classification and detection. Though these models have fewer parameters, they have high latency as compared to convolutional neural network-based models. The main efficiency bottleneck in MobileViT is the multi-headed self-attention (MHA) in transformers, which requires O(k2) time complexity with respect to the number of tokens (or patches) k. Moreover, MHA requires costly operations (e.g., batch-wise matrix multiplication) for computing self-attention, impacting latency on resource-constrained devices. This paper introduces a separable self-attention method with linear complexity, i.e. O(k). A simple yet effective characteristic of the proposed method is that it uses element-wise operations for computing self-attention, making it a good choice for resource-constrained devices. The improved model, MobileViTV2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection. With about three million parameters, MobileViTV2 achieves a top-1 accuracy of 75.6% on the ImageNet dataset, outperforming MobileViT by about 1% while running 3.2× faster on a mobile device.
Tips:

MobileViTV2 is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map.
One can use [MobileViTImageProcessor] to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB).
The available image classification checkpoints are pre-trained on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
The segmentation model uses a DeepLabV3 head. The available semantic segmentation checkpoints are pre-trained on PASCAL VOC.

This model was contributed by shehan97.
The original code can be found here.
MobileViTV2Config
[[autodoc]] MobileViTV2Config
MobileViTV2Model
[[autodoc]] MobileViTV2Model
    - forward
MobileViTV2ForImageClassification
[[autodoc]] MobileViTV2ForImageClassification
    - forward
MobileViTV2ForSemanticSegmentation
[[autodoc]] MobileViTV2ForSemanticSegmentation
    - forward"
"
YOSO
Overview
The YOSO model was proposed in You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh. YOSO approximates standard softmax self-attention
via a Bernoulli sampling scheme based on Locality Sensitive Hashing (LSH). In principle, all the Bernoulli random variables can be sampled with
a single hash. 
The abstract from the paper is the following:
Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is 
the self-attention mechanism, which captures the interactions of token pairs in the input sequences and depends quadratically 
on the sequence length. Training such models on longer sequences is expensive. In this paper, we show that a Bernoulli sampling 
attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear. 
We bypass the quadratic cost by considering self-attention as a sum of individual tokens associated with Bernoulli random 
variables that can, in principle, be sampled at once by a single hash (although in practice, this number may be a small constant). 
This leads to an efficient sampling scheme to estimate self-attention which relies on specific modifications of 
LSH (to enable deployment on GPU architectures). We evaluate our algorithm on the GLUE benchmark with standard 512 sequence 
length where we see favorable performance relative to a standard pretrained Transformer. On the Long Range Arena (LRA) benchmark, 
for evaluating performance on long sequences, our method achieves results consistent with softmax self-attention but with sizable 
speed-ups and memory savings and often outperforms other efficient self-attention methods. Our code is available at this https URL
Tips:

The YOSO attention algorithm is implemented through custom CUDA kernels, functions written in CUDA C++ that can be executed multiple times
in parallel on a GPU.
The kernels provide a fast_hash function, which approximates the random projections of the queries and keys using the Fast Hadamard Transform. Using these
hash codes, the lsh_cumulation function approximates self-attention via LSH-based Bernoulli sampling.
To use the custom kernels, the user should set config.use_expectation = False. To ensure that the kernels are compiled successfully, 
the user must install the correct version of PyTorch and cudatoolkit. By default, config.use_expectation = True, which uses YOSO-E and 
does not require compiling CUDA kernels.

 
 YOSO Attention Algorithm. Taken from the original paper.
This model was contributed by novice03. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

YosoConfig
[[autodoc]] YosoConfig
YosoModel
[[autodoc]] YosoModel
    - forward
YosoForMaskedLM
[[autodoc]] YosoForMaskedLM
    - forward
YosoForSequenceClassification
[[autodoc]] YosoForSequenceClassification
    - forward
YosoForMultipleChoice
[[autodoc]] YosoForMultipleChoice
    - forward
YosoForTokenClassification
[[autodoc]] YosoForTokenClassification
    - forward
YosoForQuestionAnswering
[[autodoc]] YosoForQuestionAnswering
    - forward"
"
DeBERTa-v2
Overview
The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
RoBERTa.
The abstract from the paper is the following:
Recent progress in pre-trained neural language models has significantly improved the performance of many natural
language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with
disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the
disentangled attention mechanism, where each word is represented using two vectors that encode its content and
position, respectively, and the attention weights among words are computed using disentangled matrices on their
contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.
The following information is visible directly on the original implementation
repository. DeBERTa v2 is the second version of the DeBERTa model. It includes
the 1.5B model used for the SuperGLUE single-model submission and achieving 89.9, versus human baseline 89.8. You can
find more details about this submission in the authors'
blog
New in v2:

Vocabulary In v2 the tokenizer is changed to use a new vocabulary of size 128K built from the training data.
  Instead of a GPT2-based tokenizer, the tokenizer is now
  sentencepiece-based tokenizer.
nGiE(nGram Induced Input Encoding) The DeBERTa-v2 model uses an additional convolution layer aside with the first
  transformer layer to better learn the local dependency of input tokens.
Sharing position projection matrix with content projection matrix in attention layer Based on previous
  experiments, this can save parameters without affecting the performance.
Apply bucket to encode relative positions The DeBERTa-v2 model uses log bucket to encode relative positions
  similar to T5.
900M model & 1.5B model Two additional model sizes are available: 900M and 1.5B, which significantly improves the
  performance of downstream tasks.

This model was contributed by DeBERTa. This model TF 2.0 implementation was
contributed by kamalkraj. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

DebertaV2Config
[[autodoc]] DebertaV2Config
DebertaV2Tokenizer
[[autodoc]] DebertaV2Tokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
DebertaV2TokenizerFast
[[autodoc]] DebertaV2TokenizerFast
    - build_inputs_with_special_tokens
    - create_token_type_ids_from_sequences
DebertaV2Model
[[autodoc]] DebertaV2Model
    - forward
DebertaV2PreTrainedModel
[[autodoc]] DebertaV2PreTrainedModel
    - forward
DebertaV2ForMaskedLM
[[autodoc]] DebertaV2ForMaskedLM
    - forward
DebertaV2ForSequenceClassification
[[autodoc]] DebertaV2ForSequenceClassification
    - forward
DebertaV2ForTokenClassification
[[autodoc]] DebertaV2ForTokenClassification
    - forward
DebertaV2ForQuestionAnswering
[[autodoc]] DebertaV2ForQuestionAnswering
    - forward
DebertaV2ForMultipleChoice
[[autodoc]] DebertaV2ForMultipleChoice
    - forward
TFDebertaV2Model
[[autodoc]] TFDebertaV2Model
    - call
TFDebertaV2PreTrainedModel
[[autodoc]] TFDebertaV2PreTrainedModel
    - call
TFDebertaV2ForMaskedLM
[[autodoc]] TFDebertaV2ForMaskedLM
    - call
TFDebertaV2ForSequenceClassification
[[autodoc]] TFDebertaV2ForSequenceClassification
    - call
TFDebertaV2ForTokenClassification
[[autodoc]] TFDebertaV2ForTokenClassification
    - call
TFDebertaV2ForQuestionAnswering
[[autodoc]] TFDebertaV2ForQuestionAnswering
    - call"
"
MVP
Overview
The MVP model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
According to the abstract,

MVP follows a standard Transformer encoder-decoder architecture.
MVP is supervised pre-trained using labeled datasets.
MVP also has task-specific soft prompts to stimulate the model's capacity in performing a certain task.
MVP is specially designed for natural language generation and can be adapted to a wide range of generation tasks, including but not limited to summarization, data-to-text generation, open-ended dialogue system, story generation, question answering, question generation, task-oriented dialogue system, commonsense generation, paraphrase generation, text style transfer, and text simplification. Our model can also be adapted to natural language understanding tasks such as sequence classification and (extractive) question answering.

Tips:
- We have released a series of models here, including MVP, MVP with task-specific prompts, and multi-task pre-trained variants.
- If you want to use a model without prompts (standard Transformer), you can load it through MvpForConditionalGeneration.from_pretrained('RUCAIBox/mvp').
- If you want to use a model with task-specific prompts, such as summarization, you can load it through MvpForConditionalGeneration.from_pretrained('RUCAIBox/mvp-summarization').
- Our model supports lightweight prompt tuning following Prefix-tuning with method set_lightweight_tuning().
This model was contributed by Tianyi Tang. The detailed information and instructions can be found here.
Examples
For summarization, it is an example to use MVP and MVP with summarization-specific prompts.
thon

from transformers import MvpTokenizer, MvpForConditionalGeneration
tokenizer = MvpTokenizer.from_pretrained(""RUCAIBox/mvp"")
model = MvpForConditionalGeneration.from_pretrained(""RUCAIBox/mvp"")
model_with_prompt = MvpForConditionalGeneration.from_pretrained(""RUCAIBox/mvp-summarization"")
inputs = tokenizer(
     ""Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons."",
     return_tensors=""pt"",
 )
generated_ids = model.generate(**inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
[""Why You Shouldn't Quit Your Job""]
generated_ids = model_with_prompt.generate(**inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
[""Don't do it if these are your reasons""]

For data-to-text generation, it is an example to use MVP and multi-task pre-trained variants.
thon

from transformers import MvpTokenizerFast, MvpForConditionalGeneration
tokenizer = MvpTokenizerFast.from_pretrained(""RUCAIBox/mvp"")
model = MvpForConditionalGeneration.from_pretrained(""RUCAIBox/mvp"")
model_with_mtl = MvpForConditionalGeneration.from_pretrained(""RUCAIBox/mtl-data-to-text"")
inputs = tokenizer(
     ""Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man"",
     return_tensors=""pt"",
 )
generated_ids = model.generate(**inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Stan Lee created the character of Iron Man, a fictional superhero appearing in American comic']
generated_ids = model_with_mtl.generate(**inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Iron Man is a fictional superhero appearing in American comic books published by Marvel Comics.']

For lightweight tuning, i.e., fixing the model and only tuning prompts, you can load MVP with randomly initialized prompts or with task-specific prompts. Our code also supports Prefix-tuning with BART following the original paper.
thon

from transformers import MvpForConditionalGeneration
model = MvpForConditionalGeneration.from_pretrained(""RUCAIBox/mvp"", use_prompt=True)
the number of trainable parameters (full tuning)
sum(p.numel() for p in model.parameters() if p.requires_grad)
468116832
lightweight tuning with randomly initialized prompts
model.set_lightweight_tuning()
the number of trainable parameters (lightweight tuning)
sum(p.numel() for p in model.parameters() if p.requires_grad)
61823328
lightweight tuning with task-specific prompts
model = MvpForConditionalGeneration.from_pretrained(""RUCAIBox/mtl-data-to-text"")
model.set_lightweight_tuning()
original lightweight Prefix-tuning
model = MvpForConditionalGeneration.from_pretrained(""facebook/bart-large"", use_prompt=True)
model.set_lightweight_tuning()

Documentation resources

Text classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Translation task guide
Summarization task guide

MvpConfig
[[autodoc]] MvpConfig
MvpTokenizer
[[autodoc]] MvpTokenizer
MvpTokenizerFast
[[autodoc]] MvpTokenizerFast
MvpModel
[[autodoc]] MvpModel
    - forward
MvpForConditionalGeneration
[[autodoc]] MvpForConditionalGeneration
    - forward
MvpForSequenceClassification
[[autodoc]] MvpForSequenceClassification
    - forward
MvpForQuestionAnswering
[[autodoc]] MvpForQuestionAnswering
    - forward
MvpForCausalLM
[[autodoc]] MvpForCausalLM
    - forward"
"
FSMT
DISCLAIMER: If you see something strange, file a Github Issue and assign
@stas00.
Overview
FSMT (FairSeq MachineTranslation) models were introduced in Facebook FAIR's WMT19 News Translation Task Submission by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
The abstract of the paper is the following:
This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two
language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from
last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling
toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes,
as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific
data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the
human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations.
This system improves upon our WMT'18 submission by 4.5 BLEU points.
This model was contributed by stas. The original code can be found
here.
Implementation Notes

FSMT uses source and target vocabulary pairs that aren't combined into one. It doesn't share embeddings tokens
  either. Its tokenizer is very similar to [XLMTokenizer] and the main model is derived from
  [BartModel].

FSMTConfig
[[autodoc]] FSMTConfig
FSMTTokenizer
[[autodoc]] FSMTTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
FSMTModel
[[autodoc]] FSMTModel
    - forward
FSMTForConditionalGeneration
[[autodoc]] FSMTForConditionalGeneration
    - forward"
"
VAN

This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: pip install -U transformers==4.30.0.

Overview
The VAN model was proposed in Visual Attention Network by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
This paper introduces a new attention layer based on convolution operations able to capture both local and distant relationships. This is done by combining normal and large kernel convolution layers. The latter uses a dilated convolution to capture distant correlations.
The abstract from the paper is the following:
While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc. Code is available at this https URL.
Tips:

VAN does not have an embedding layer, thus the hidden_states will have a length equal to the number of stages.

The figure below illustrates the architecture of a Visual Aattention Layer. Taken from the original paper.

This model was contributed by Francesco. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with VAN.

[VanForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
VanConfig
[[autodoc]] VanConfig
VanModel
[[autodoc]] VanModel
    - forward
VanForImageClassification
[[autodoc]] VanForImageClassification
    - forward"
"
DeiT

This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a Github Issue.

Overview
The DeiT model was proposed in Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre
Sablayrolles, Hervé Jégou. The Vision Transformer (ViT) introduced in Dosovitskiy et al., 2020 has shown that one can match or even outperform existing convolutional neural
networks using a Transformer encoder (BERT-like). However, the ViT models introduced in that paper required training on
expensive infrastructure for multiple weeks, using external data. DeiT (data-efficient image transformers) are more
efficiently trained transformers for image classification, requiring far less data and far less computing resources
compared to the original ViT models.
The abstract from the paper is the following:
Recently, neural networks purely based on attention were shown to address image understanding tasks such as image
classification. However, these visual transformers are pre-trained with hundreds of millions of images using an
expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free
transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision
transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external
data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation
token ensuring that the student learns from the teacher through attention. We show the interest of this token-based
distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets
for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and
models.
Tips:

Compared to ViT, DeiT models use a so-called distillation token to effectively learn from a teacher (which, in the
  DeiT paper, is a ResNet like-model). The distillation token is learned through backpropagation, by interacting with
  the class ([CLS]) and patch tokens through the self-attention layers.
There are 2 ways to fine-tune distilled models, either (1) in a classic way, by only placing a prediction head on top
  of the final hidden state of the class token and not using the distillation signal, or (2) by placing both a
  prediction head on top of the class token and on top of the distillation token. In that case, the [CLS] prediction
  head is trained using regular cross-entropy between the prediction of the head and the ground-truth label, while the
  distillation prediction head is trained using hard distillation (cross-entropy between the prediction of the
  distillation head and the label predicted by the teacher). At inference time, one takes the average prediction
  between both heads as final prediction. (2) is also called ""fine-tuning with distillation"", because one relies on a
  teacher that has already been fine-tuned on the downstream dataset. In terms of models, (1) corresponds to
  [DeiTForImageClassification] and (2) corresponds to
  [DeiTForImageClassificationWithTeacher].
Note that the authors also did try soft distillation for (2) (in which case the distillation prediction head is
  trained using KL divergence to match the softmax output of the teacher), but hard distillation gave the best results.
All released checkpoints were pre-trained and fine-tuned on ImageNet-1k only. No external data was used. This is in
  contrast with the original ViT model, which used external data like the JFT-300M dataset/Imagenet-21k for
  pre-training.
The authors of DeiT also released more efficiently trained ViT models, which you can directly plug into
  [ViTModel] or [ViTForImageClassification]. Techniques like data
  augmentation, optimization, and regularization were used in order to simulate training on a much larger dataset
  (while only using ImageNet-1k for pre-training). There are 4 variants available (in 3 different sizes):
  facebook/deit-tiny-patch16-224, facebook/deit-small-patch16-224, facebook/deit-base-patch16-224 and
  facebook/deit-base-patch16-384. Note that one should use [DeiTImageProcessor] in order to
  prepare images for the model.

This model was contributed by nielsr. The TensorFlow version of this model was added by amyeroberts.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DeiT.

[DeiTForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

Besides that:

[DeiTForMaskedImageModeling] is supported by this example script.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
DeiTConfig
[[autodoc]] DeiTConfig
DeiTFeatureExtractor
[[autodoc]] DeiTFeatureExtractor
    - call
DeiTImageProcessor
[[autodoc]] DeiTImageProcessor
    - preprocess
DeiTModel
[[autodoc]] DeiTModel
    - forward
DeiTForMaskedImageModeling
[[autodoc]] DeiTForMaskedImageModeling
    - forward
DeiTForImageClassification
[[autodoc]] DeiTForImageClassification
    - forward
DeiTForImageClassificationWithTeacher
[[autodoc]] DeiTForImageClassificationWithTeacher
    - forward
TFDeiTModel
[[autodoc]] TFDeiTModel
    - call
TFDeiTForMaskedImageModeling
[[autodoc]] TFDeiTForMaskedImageModeling
    - call
TFDeiTForImageClassification
[[autodoc]] TFDeiTForImageClassification
    - call
TFDeiTForImageClassificationWithTeacher
[[autodoc]] TFDeiTForImageClassificationWithTeacher
    - call"
"
MatCha
Overview
MatCha has been proposed in the paper MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering, from Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
The abstract of the paper states the following:
Visual language data such as plots, charts, and infographics are ubiquitous in the human world. However, state-of-the-art vision-language models do not perform well on these data. We propose MatCha (Math reasoning and Chart derendering pretraining) to enhance visual language models' capabilities in jointly modeling charts/plots and language data. Specifically, we propose several pretraining tasks that cover plot deconstruction and numerical reasoning which are the key capabilities in visual language modeling. We perform the MatCha pretraining starting from Pix2Struct, a recently proposed image-to-text visual language model. On standard benchmarks such as PlotQA and ChartQA, the MatCha model outperforms state-of-the-art methods by as much as nearly 20%. We also examine how well MatCha pretraining transfers to domains such as screenshots, textbook diagrams, and document figures and observe overall improvement, verifying the usefulness of MatCha pretraining on broader visual language tasks.
Model description
MatCha is a model that is trained using Pix2Struct architecture. You can find more information about Pix2Struct in the Pix2Struct documentation.
MatCha is a Visual Question Answering subset of Pix2Struct architecture. It renders the input question on the image and predicts the answer.
Usage
Currently 6 checkpoints are available for MatCha:

google/matcha: the base MatCha model, used to fine-tune MatCha on downstream tasks
google/matcha-chartqa: MatCha model fine-tuned on ChartQA dataset. It can be used to answer questions about charts.
google/matcha-plotqa-v1: MatCha model fine-tuned on PlotQA dataset. It can be used to answer questions about plots.
google/matcha-plotqa-v2: MatCha model fine-tuned on PlotQA dataset. It can be used to answer questions about plots.
google/matcha-chart2text-statista: MatCha model fine-tuned on Statista dataset. 
google/matcha-chart2text-pew: MatCha model fine-tuned on Pew dataset.

The models finetuned on chart2text-pew and chart2text-statista are more suited for summarization, whereas the models finetuned on plotqa and chartqa are more suited for question answering.
You can use these models as follows (example on a ChatQA dataset):
thon
from transformers import AutoProcessor, Pix2StructForConditionalGeneration
import requests
from PIL import Image
model = Pix2StructForConditionalGeneration.from_pretrained(""google/matcha-chartqa"").to(0)
processor = AutoProcessor.from_pretrained(""google/matcha-chartqa"")
url = ""https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/20294671002019.png""
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, text=""Is the sum of all 4 places greater than Laos?"", return_tensors=""pt"").to(0)
predictions = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(predictions[0], skip_special_tokens=True))

Fine-tuning
To fine-tune MatCha, refer to the pix2struct fine-tuning notebook. For Pix2Struct models, we have found out that fine-tuning the model with Adafactor and cosine learning rate scheduler leads to faste convergence:
thon
from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup
optimizer = Adafactor(self.parameters(), scale_parameter=False, relative_step=False, lr=0.01, weight_decay=1e-05)
scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=1000, num_training_steps=40000)
```"
"
BigBirdPegasus
Overview
The BigBird model was proposed in Big Bird: Transformers for Longer Sequences by
Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
has been shown that applying sparse, global, and random attention approximates full attention, while being
computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
BigBird has shown improved performance on various long document NLP tasks, such as question answering and
summarization, compared to BERT or RoBERTa.
The abstract from the paper is the following:
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
propose novel applications to genomics data.
Tips:

For an in-detail explanation on how BigBird's attention works, see this blog post.
BigBird comes with 2 implementations: original_full & block_sparse. For the sequence length < 1024, using
  original_full is advised as there is no benefit in using block_sparse attention.
The code currently uses window size of 3 blocks and 2 global blocks.
Sequence length must be divisible by block size.
Current implementation supports only ITC.
Current implementation doesn't support num_random_blocks = 0.
BigBirdPegasus uses the PegasusTokenizer.
BigBird is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.

The original code can be found here.
Documentation resources

Text classification task guide
Question answering task guide
Causal language modeling task guide
Translation task guide
Summarization task guide

BigBirdPegasusConfig
[[autodoc]] BigBirdPegasusConfig
    - all
BigBirdPegasusModel
[[autodoc]] BigBirdPegasusModel
    - forward
BigBirdPegasusForConditionalGeneration
[[autodoc]] BigBirdPegasusForConditionalGeneration
    - forward
BigBirdPegasusForSequenceClassification
[[autodoc]] BigBirdPegasusForSequenceClassification
    - forward
BigBirdPegasusForQuestionAnswering
[[autodoc]] BigBirdPegasusForQuestionAnswering
    - forward
BigBirdPegasusForCausalLM
[[autodoc]] BigBirdPegasusForCausalLM
    - forward"
"
TVLT
Overview
The TVLT model was proposed in TVLT: Textless Vision-Language Transformer
by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal (the first three authors contributed equally). The Textless Vision-Language Transformer (TVLT) is a model that uses raw visual and audio inputs for vision-and-language representation learning, without using text-specific modules such as tokenization or automatic speech recognition (ASR). It can perform various audiovisual and vision-language tasks like retrieval, question answering, etc.
The abstract from the paper is the following:
In this work, we present the Textless Vision-Language Transformer (TVLT), where homogeneous transformer blocks take raw visual and audio inputs for vision-and-language representation learning with minimal modality-specific design, and do not use text-specific modules such as tokenization or automatic speech recognition (ASR). TVLT is trained by reconstructing masked patches of continuous video frames and audio spectrograms (masked autoencoding) and contrastive modeling to align video and audio. TVLT attains performance comparable to its text-based counterpart on various multimodal tasks, such as visual question answering, image retrieval, video retrieval, and multimodal sentiment analysis, with 28x faster inference speed and only 1/3 of the parameters. Our findings suggest the possibility of learning compact and efficient visual-linguistic representations from low-level visual and audio signals without assuming the prior existence of text.
Tips:

TVLT is a model that takes both pixel_values and audio_values as input. One can use [TvltProcessor] to prepare data for the model.
  This processor wraps an image processor (for the image/video modality) and an audio feature extractor (for the audio modality) into one.
TVLT is trained with images/videos and audios of various sizes: the authors resize and crop the input images/videos to 224 and limit the length of audio spectrogram to 2048. To make batching of videos and audios possible, the authors use a pixel_mask that indicates which pixels are real/padding and audio_mask that indicates which audio values are real/padding.
The design of TVLT is very similar to that of a standard Vision Transformer (ViT) and masked autoencoder (MAE) as in ViTMAE. The difference is that the model includes embedding layers for the audio modality.
The PyTorch version of this model is only available in torch 1.10 and higher.

 TVLT architecture. Taken from the https://arxiv.org/abs/2102.03334"">original paper. 
The original code can be found here. This model was contributed by Zineng Tang.
TvltConfig
[[autodoc]] TvltConfig
TvltProcessor
[[autodoc]] TvltProcessor
    - call
TvltImageProcessor
[[autodoc]] TvltImageProcessor
    - preprocess
TvltFeatureExtractor
[[autodoc]] TvltFeatureExtractor
    - call
TvltModel
[[autodoc]] TvltModel
    - forward
TvltForPreTraining
[[autodoc]] TvltForPreTraining
    - forward
TvltForAudioVisualClassification
[[autodoc]] TvltForAudioVisualClassification
    - forward"
"
GroupViT
Overview
The GroupViT model was proposed in GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
Inspired by CLIP, GroupViT is a vision-language model that can perform zero-shot semantic segmentation on any given vocabulary categories.
The abstract from the paper is the following:
Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we propose to bring back the grouping mechanism into deep networks, which allows semantic segments to emerge automatically with only text supervision. We propose a hierarchical Grouping Vision Transformer (GroupViT), which goes beyond the regular grid structure representation and learns to group image regions into progressively larger arbitrary-shaped segments. We train GroupViT jointly with a text encoder on a large-scale image-text dataset via contrastive losses. With only text supervision and without any pixel-level annotations, GroupViT learns to group together semantic regions and successfully transfers to the task of semantic segmentation in a zero-shot manner, i.e., without any further fine-tuning. It achieves a zero-shot accuracy of 52.3% mIoU on the PASCAL VOC 2012 and 22.4% mIoU on PASCAL Context datasets, and performs competitively to state-of-the-art transfer-learning methods requiring greater levels of supervision.
Tips:

You may specify output_segmentation=True in the forward of GroupViTModel to get the segmentation logits of input texts. 

This model was contributed by xvjiarui. The TensorFlow version was contributed by ariG23498 with the help of Yih-Dar SHIEH, Amy Roberts, and Joao Gante.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GroupViT.

The quickest way to get started with GroupViT is by checking the example notebooks (which showcase zero-shot segmentation inference).
One can also check out the HuggingFace Spaces demo to play with GroupViT. 

GroupViTConfig
[[autodoc]] GroupViTConfig
    - from_text_vision_configs
GroupViTTextConfig
[[autodoc]] GroupViTTextConfig
GroupViTVisionConfig
[[autodoc]] GroupViTVisionConfig
GroupViTModel
[[autodoc]] GroupViTModel
    - forward
    - get_text_features
    - get_image_features
GroupViTTextModel
[[autodoc]] GroupViTTextModel
    - forward
GroupViTVisionModel
[[autodoc]] GroupViTVisionModel
    - forward
TFGroupViTModel
[[autodoc]] TFGroupViTModel
    - call
    - get_text_features
    - get_image_features
TFGroupViTTextModel
[[autodoc]] TFGroupViTTextModel
    - call
TFGroupViTVisionModel
[[autodoc]] TFGroupViTVisionModel
    - call"
"
DINOv2
Overview
The DINOv2 model was proposed in DINOv2: Learning Robust Visual Features without Supervision by
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski.
DINOv2 is an upgrade of DINO, a self-supervised method applied on Vision Transformers. This method enables all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning.
The abstract from the paper is the following:
The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in terms of data and model size. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature. In terms of models, we train a ViT model (Dosovitskiy et al., 2020) with 1B parameters and distill it into a series of smaller models that surpass the best available all-purpose features, OpenCLIP (Ilharco et al., 2021) on most of the benchmarks at image and pixel levels.
Tips:

One can use [AutoImageProcessor] class to prepare images for the model.

This model was contributed by nielsr.
The original code can be found here.
Dinov2Config
[[autodoc]] Dinov2Config
Dinov2Model
[[autodoc]] Dinov2Model
    - forward
Dinov2ForImageClassification
[[autodoc]] Dinov2ForImageClassification
    - forward"
"
ELECTRA

Overview
The ELECTRA model was proposed in the paper ELECTRA: Pre-training Text Encoders as Discriminators Rather Than
Generators. ELECTRA is a new pretraining approach which trains two
transformer models: the generator and the discriminator. The generator's role is to replace tokens in a sequence, and
is therefore trained as a masked language model. The discriminator, which is the model we're interested in, tries to
identify which tokens were replaced by the generator in the sequence.
The abstract from the paper is the following:
Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with [MASK]
and then train a model to reconstruct the original tokens. While they produce good results when transferred to
downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a
more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach
corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead
of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that
predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments
demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens
rather than just the small subset that was masked out. As a result, the contextual representations learned by our
approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are
particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained
using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale,
where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when
using the same amount of compute.
Tips:

ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The
  only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller,
  while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from their
  embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no projection
  layer is used.
ELECTRA is a transformer model pretrained with the use of another (small) masked language model. The inputs are corrupted by that language model, which takes an input text that is randomly masked and outputs a text in which ELECTRA has to predict which token is an original and which one has been replaced. Like for GAN training, the small language model is trained for a few steps (but with the original texts as objective, not to fool the ELECTRA model like in a traditional GAN setting) then the ELECTRA model is trained for a few steps.
The ELECTRA checkpoints saved using Google Research's implementation
  contain both the generator and discriminator. The conversion script requires the user to name which model to export
  into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all
  available ELECTRA models, however. This means that the discriminator may be loaded in the
  [ElectraForMaskedLM] model, and the generator may be loaded in the
  [ElectraForPreTraining] model (the classification head will be randomly initialized as it
  doesn't exist in the generator).

This model was contributed by lysandre. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

ElectraConfig
[[autodoc]] ElectraConfig
ElectraTokenizer
[[autodoc]] ElectraTokenizer
ElectraTokenizerFast
[[autodoc]] ElectraTokenizerFast
Electra specific outputs
[[autodoc]] models.electra.modeling_electra.ElectraForPreTrainingOutput
[[autodoc]] models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput
ElectraModel
[[autodoc]] ElectraModel
    - forward
ElectraForPreTraining
[[autodoc]] ElectraForPreTraining
    - forward
ElectraForCausalLM
[[autodoc]] ElectraForCausalLM
    - forward
ElectraForMaskedLM
[[autodoc]] ElectraForMaskedLM
    - forward
ElectraForSequenceClassification
[[autodoc]] ElectraForSequenceClassification
    - forward
ElectraForMultipleChoice
[[autodoc]] ElectraForMultipleChoice
    - forward
ElectraForTokenClassification
[[autodoc]] ElectraForTokenClassification
    - forward
ElectraForQuestionAnswering
[[autodoc]] ElectraForQuestionAnswering
    - forward
TFElectraModel
[[autodoc]] TFElectraModel
    - call
TFElectraForPreTraining
[[autodoc]] TFElectraForPreTraining
    - call
TFElectraForMaskedLM
[[autodoc]] TFElectraForMaskedLM
    - call
TFElectraForSequenceClassification
[[autodoc]] TFElectraForSequenceClassification
    - call
TFElectraForMultipleChoice
[[autodoc]] TFElectraForMultipleChoice
    - call
TFElectraForTokenClassification
[[autodoc]] TFElectraForTokenClassification
    - call
TFElectraForQuestionAnswering
[[autodoc]] TFElectraForQuestionAnswering
    - call
FlaxElectraModel
[[autodoc]] FlaxElectraModel
    - call
FlaxElectraForPreTraining
[[autodoc]] FlaxElectraForPreTraining
    - call
FlaxElectraForCausalLM
[[autodoc]] FlaxElectraForCausalLM
    - call
FlaxElectraForMaskedLM
[[autodoc]] FlaxElectraForMaskedLM
    - call
FlaxElectraForSequenceClassification
[[autodoc]] FlaxElectraForSequenceClassification
    - call
FlaxElectraForMultipleChoice
[[autodoc]] FlaxElectraForMultipleChoice
    - call
FlaxElectraForTokenClassification
[[autodoc]] FlaxElectraForTokenClassification
    - call
FlaxElectraForQuestionAnswering
[[autodoc]] FlaxElectraForQuestionAnswering
    - call"
"
VideoMAE
Overview
The VideoMAE model was proposed in VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
VideoMAE extends masked auto encoders (MAE) to video, claiming state-of-the-art performance on several video classification benchmarks.
The abstract from the paper is the following:
Pre-training video transformers on extra large-scale datasets is generally required to achieve premier performance on relatively small datasets. In this paper, we show that video masked autoencoders (VideoMAE) are data-efficient learners for self-supervised video pre-training (SSVP). We are inspired by the recent ImageMAE and propose customized video tube masking and reconstruction. These simple designs turn out to be effective for overcoming information leakage caused by the temporal correlation during video reconstruction. We obtain three important findings on SSVP: (1) An extremely high proportion of masking ratio (i.e., 90% to 95%) still yields favorable performance of VideoMAE. The temporally redundant video content enables higher masking ratio than that of images. (2) VideoMAE achieves impressive results on very small datasets (i.e., around 3k-4k videos) without using any extra data. This is partially ascribed to the challenging task of video reconstruction to enforce high-level structure learning. (3) VideoMAE shows that data quality is more important than data quantity for SSVP. Domain shift between pre-training and target datasets are important issues in SSVP. Notably, our VideoMAE with the vanilla ViT backbone can achieve 83.9% on Kinects-400, 75.3% on Something-Something V2, 90.8% on UCF101, and 61.1% on HMDB51 without using any extra data.
Tips:

One can use [VideoMAEImageProcessor] to prepare videos for the model. It will resize + normalize all frames of a video for you.
[VideoMAEForPreTraining] includes the decoder on top for self-supervised pre-training.

 VideoMAE pre-training. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with VideoMAE. If
you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll
review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Video classification
- A notebook that shows how
to fine-tune a VideoMAE model on a custom dataset.
- Video classification task guide
- A 🤗 Space showing how to perform inference with a video classification model.
VideoMAEConfig
[[autodoc]] VideoMAEConfig
VideoMAEFeatureExtractor
[[autodoc]] VideoMAEFeatureExtractor
    - call
VideoMAEImageProcessor
[[autodoc]] VideoMAEImageProcessor
    - preprocess
VideoMAEModel
[[autodoc]] VideoMAEModel
    - forward
VideoMAEForPreTraining
[[autodoc]] transformers.VideoMAEForPreTraining
    - forward
VideoMAEForVideoClassification
[[autodoc]] transformers.VideoMAEForVideoClassification
    - forward"
"
LUKE
Overview
The LUKE model was proposed in LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda and Yuji Matsumoto.
It is based on RoBERTa and adds entity embeddings as well as an entity-aware self-attention mechanism, which helps
improve performance on various downstream tasks involving reasoning about entities such as named entity recognition,
extractive and cloze-style question answering, entity typing, and relation classification.
The abstract from the paper is the following:
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new
pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed
model treats words and entities in a given text as independent tokens, and outputs contextualized representations of
them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves
predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also
propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the
transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model
achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains
state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification),
CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question
answering).
Tips:

This implementation is the same as [RobertaModel] with the addition of entity embeddings as well
  as an entity-aware self-attention mechanism, which improves performance on tasks involving reasoning about entities.
LUKE treats entities as input tokens; therefore, it takes entity_ids, entity_attention_mask,
  entity_token_type_ids and entity_position_ids as extra input. You can obtain those using
  [LukeTokenizer].

[LukeTokenizer] takes entities and entity_spans (character-based start and end
  positions of the entities in the input text) as extra input. entities typically consist of [MASK] entities or
  Wikipedia entities. The brief description when inputting these entities are as follows:

Inputting [MASK] entities to compute entity representations: The [MASK] entity is used to mask entities to be
    predicted during pretraining. When LUKE receives the [MASK] entity, it tries to predict the original entity by
    gathering the information about the entity from the input text. Therefore, the [MASK] entity can be used to address
    downstream tasks requiring the information of entities in text such as entity typing, relation classification, and
    named entity recognition.

Inputting Wikipedia entities to compute knowledge-enhanced token representations: LUKE learns rich information
    (or knowledge) about Wikipedia entities during pretraining and stores the information in its entity embedding. By
    using Wikipedia entities as input tokens, LUKE outputs token representations enriched by the information stored in
    the embeddings of these entities. This is particularly effective for tasks requiring real-world knowledge, such as
    question answering.

There are three head models for the former use case:

[LukeForEntityClassification], for tasks to classify a single entity in an input text such as
    entity typing, e.g. the Open Entity dataset.
    This model places a linear head on top of the output entity representation.

[LukeForEntityPairClassification], for tasks to classify the relationship between two entities
    such as relation classification, e.g. the TACRED dataset. This
    model places a linear head on top of the concatenated output representation of the pair of given entities.
[LukeForEntitySpanClassification], for tasks to classify the sequence of entity spans, such as
    named entity recognition (NER). This model places a linear head on top of the output entity representations. You
    can address NER using this model by inputting all possible entity spans in the text to the model.

[LukeTokenizer] has a task argument, which enables you to easily create an input to these
  head models by specifying task=""entity_classification"", task=""entity_pair_classification"", or
  task=""entity_span_classification"". Please refer to the example code of each head models.
A demo notebook on how to fine-tune [LukeForEntityPairClassification] for relation
  classification can be found here.
There are also 3 notebooks available, which showcase how you can reproduce the results as reported in the paper with
  the HuggingFace implementation of LUKE. They can be found here.
Example:
thon

from transformers import LukeTokenizer, LukeModel, LukeForEntityPairClassification
model = LukeModel.from_pretrained(""studio-ousia/luke-base"")
tokenizer = LukeTokenizer.from_pretrained(""studio-ousia/luke-base"")

Example 1: Computing the contextualized entity representation corresponding to the entity mention ""Beyoncé""

text = ""Beyoncé lives in Los Angeles.""
entity_spans = [(0, 7)]  # character-based entity span corresponding to ""Beyoncé""
inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors=""pt"")
outputs = model(**inputs)
word_last_hidden_state = outputs.last_hidden_state
entity_last_hidden_state = outputs.entity_last_hidden_state

Example 2: Inputting Wikipedia entities to obtain enriched contextualized representations

entities = [
     ""Beyoncé"",
     ""Los Angeles"",
 ]  # Wikipedia entity titles corresponding to the entity mentions ""Beyoncé"" and ""Los Angeles""
entity_spans = [(0, 7), (17, 28)]  # character-based entity spans corresponding to ""Beyoncé"" and ""Los Angeles""
inputs = tokenizer(text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors=""pt"")
outputs = model(**inputs)
word_last_hidden_state = outputs.last_hidden_state
entity_last_hidden_state = outputs.entity_last_hidden_state

Example 3: Classifying the relationship between two entities using LukeForEntityPairClassification head model

model = LukeForEntityPairClassification.from_pretrained(""studio-ousia/luke-large-finetuned-tacred"")
tokenizer = LukeTokenizer.from_pretrained(""studio-ousia/luke-large-finetuned-tacred"")
entity_spans = [(0, 7), (17, 28)]  # character-based entity spans corresponding to ""Beyoncé"" and ""Los Angeles""
inputs = tokenizer(text, entity_spans=entity_spans, return_tensors=""pt"")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = int(logits[0].argmax())
print(""Predicted class:"", model.config.id2label[predicted_class_idx])

This model was contributed by ikuyamada and nielsr. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

LukeConfig
[[autodoc]] LukeConfig
LukeTokenizer
[[autodoc]] LukeTokenizer
    - call
    - save_vocabulary
LukeModel
[[autodoc]] LukeModel
    - forward
LukeForMaskedLM
[[autodoc]] LukeForMaskedLM
    - forward
LukeForEntityClassification
[[autodoc]] LukeForEntityClassification
    - forward
LukeForEntityPairClassification
[[autodoc]] LukeForEntityPairClassification
    - forward
LukeForEntitySpanClassification
[[autodoc]] LukeForEntitySpanClassification
    - forward
LukeForSequenceClassification
[[autodoc]] LukeForSequenceClassification
    - forward
LukeForMultipleChoice
[[autodoc]] LukeForMultipleChoice
    - forward
LukeForTokenClassification
[[autodoc]] LukeForTokenClassification
    - forward
LukeForQuestionAnswering
[[autodoc]] LukeForQuestionAnswering
    - forward"
"
Auto Classes
In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
are supplying to the from_pretrained() method. AutoClasses are here to do this job for you so that you
automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.
Instantiating one of [AutoConfig], [AutoModel], and
[AutoTokenizer] will directly create a class of the relevant architecture. For instance
python
model = AutoModel.from_pretrained(""bert-base-cased"")
will create a model that is an instance of [BertModel].
There is one class of AutoModel for each task, and for each backend (PyTorch, TensorFlow, or Flax).
Extending the Auto Classes
Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model NewModel, make sure you have a NewModelConfig then you can add those to the auto
classes like this:
thon
from transformers import AutoConfig, AutoModel
AutoConfig.register(""new-model"", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)

You will then be able to use the auto classes like you would usually do!

If your NewModelConfig is a subclass of [~transformer.PretrainedConfig], make sure its
model_type attribute is set to the same key you use when registering the config (here ""new-model"").
Likewise, if your NewModel is a subclass of [PreTrainedModel], make sure its
config_class attribute is set to the same class you use when registering the model (here
NewModelConfig).

AutoConfig
[[autodoc]] AutoConfig
AutoTokenizer
[[autodoc]] AutoTokenizer
AutoFeatureExtractor
[[autodoc]] AutoFeatureExtractor
AutoImageProcessor
[[autodoc]] AutoImageProcessor
AutoProcessor
[[autodoc]] AutoProcessor
Generic model classes
The following auto classes are available for instantiating a base model class without a specific head.
AutoModel
[[autodoc]] AutoModel
TFAutoModel
[[autodoc]] TFAutoModel
FlaxAutoModel
[[autodoc]] FlaxAutoModel
Generic pretraining classes
The following auto classes are available for instantiating a model with a pretraining head.
AutoModelForPreTraining
[[autodoc]] AutoModelForPreTraining
TFAutoModelForPreTraining
[[autodoc]] TFAutoModelForPreTraining
FlaxAutoModelForPreTraining
[[autodoc]] FlaxAutoModelForPreTraining
Natural Language Processing
The following auto classes are available for the following natural language processing tasks.
AutoModelForCausalLM
[[autodoc]] AutoModelForCausalLM
TFAutoModelForCausalLM
[[autodoc]] TFAutoModelForCausalLM
FlaxAutoModelForCausalLM
[[autodoc]] FlaxAutoModelForCausalLM
AutoModelForMaskedLM
[[autodoc]] AutoModelForMaskedLM
TFAutoModelForMaskedLM
[[autodoc]] TFAutoModelForMaskedLM
FlaxAutoModelForMaskedLM
[[autodoc]] FlaxAutoModelForMaskedLM
AutoModelForMaskGeneration
[[autodoc]] AutoModelForMaskGeneration
TFAutoModelForMaskGeneration
[[autodoc]] TFAutoModelForMaskGeneration
AutoModelForSeq2SeqLM
[[autodoc]] AutoModelForSeq2SeqLM
TFAutoModelForSeq2SeqLM
[[autodoc]] TFAutoModelForSeq2SeqLM
FlaxAutoModelForSeq2SeqLM
[[autodoc]] FlaxAutoModelForSeq2SeqLM
AutoModelForSequenceClassification
[[autodoc]] AutoModelForSequenceClassification
TFAutoModelForSequenceClassification
[[autodoc]] TFAutoModelForSequenceClassification
FlaxAutoModelForSequenceClassification
[[autodoc]] FlaxAutoModelForSequenceClassification
AutoModelForMultipleChoice
[[autodoc]] AutoModelForMultipleChoice
TFAutoModelForMultipleChoice
[[autodoc]] TFAutoModelForMultipleChoice
FlaxAutoModelForMultipleChoice
[[autodoc]] FlaxAutoModelForMultipleChoice
AutoModelForNextSentencePrediction
[[autodoc]] AutoModelForNextSentencePrediction
TFAutoModelForNextSentencePrediction
[[autodoc]] TFAutoModelForNextSentencePrediction
FlaxAutoModelForNextSentencePrediction
[[autodoc]] FlaxAutoModelForNextSentencePrediction
AutoModelForTokenClassification
[[autodoc]] AutoModelForTokenClassification
TFAutoModelForTokenClassification
[[autodoc]] TFAutoModelForTokenClassification
FlaxAutoModelForTokenClassification
[[autodoc]] FlaxAutoModelForTokenClassification
AutoModelForQuestionAnswering
[[autodoc]] AutoModelForQuestionAnswering
TFAutoModelForQuestionAnswering
[[autodoc]] TFAutoModelForQuestionAnswering
FlaxAutoModelForQuestionAnswering
[[autodoc]] FlaxAutoModelForQuestionAnswering
AutoModelForTextEncoding
[[autodoc]] AutoModelForTextEncoding
TFAutoModelForTextEncoding
[[autodoc]] TFAutoModelForTextEncoding
Computer vision
The following auto classes are available for the following computer vision tasks.
AutoModelForDepthEstimation
[[autodoc]] AutoModelForDepthEstimation
AutoModelForImageClassification
[[autodoc]] AutoModelForImageClassification
TFAutoModelForImageClassification
[[autodoc]] TFAutoModelForImageClassification
FlaxAutoModelForImageClassification
[[autodoc]] FlaxAutoModelForImageClassification
AutoModelForVideoClassification
[[autodoc]] AutoModelForVideoClassification
AutoModelForMaskedImageModeling
[[autodoc]] AutoModelForMaskedImageModeling
TFAutoModelForMaskedImageModeling
[[autodoc]] TFAutoModelForMaskedImageModeling
AutoModelForObjectDetection
[[autodoc]] AutoModelForObjectDetection
AutoModelForImageSegmentation
[[autodoc]] AutoModelForImageSegmentation
AutoModelForSemanticSegmentation
[[autodoc]] AutoModelForSemanticSegmentation
TFAutoModelForSemanticSegmentation
[[autodoc]] TFAutoModelForSemanticSegmentation
AutoModelForInstanceSegmentation
[[autodoc]] AutoModelForInstanceSegmentation
AutoModelForUniversalSegmentation
[[autodoc]] AutoModelForUniversalSegmentation
AutoModelForZeroShotImageClassification
[[autodoc]] AutoModelForZeroShotImageClassification
TFAutoModelForZeroShotImageClassification
[[autodoc]] TFAutoModelForZeroShotImageClassification
AutoModelForZeroShotObjectDetection
[[autodoc]] AutoModelForZeroShotObjectDetection
Audio
The following auto classes are available for the following audio tasks.
AutoModelForAudioClassification
[[autodoc]] AutoModelForAudioClassification
AutoModelForAudioFrameClassification
[[autodoc]] TFAutoModelForAudioClassification
TFAutoModelForAudioFrameClassification
[[autodoc]] AutoModelForAudioFrameClassification
AutoModelForCTC
[[autodoc]] AutoModelForCTC
AutoModelForSpeechSeq2Seq
[[autodoc]] AutoModelForSpeechSeq2Seq
TFAutoModelForSpeechSeq2Seq
[[autodoc]] TFAutoModelForSpeechSeq2Seq
FlaxAutoModelForSpeechSeq2Seq
[[autodoc]] FlaxAutoModelForSpeechSeq2Seq
AutoModelForAudioXVector
[[autodoc]] AutoModelForAudioXVector
Multimodal
The following auto classes are available for the following multimodal tasks.
AutoModelForTableQuestionAnswering
[[autodoc]] AutoModelForTableQuestionAnswering
TFAutoModelForTableQuestionAnswering
[[autodoc]] TFAutoModelForTableQuestionAnswering
AutoModelForDocumentQuestionAnswering
[[autodoc]] AutoModelForDocumentQuestionAnswering
TFAutoModelForDocumentQuestionAnswering
[[autodoc]] TFAutoModelForDocumentQuestionAnswering
AutoModelForVisualQuestionAnswering
[[autodoc]] AutoModelForVisualQuestionAnswering
AutoModelForVision2Seq
[[autodoc]] AutoModelForVision2Seq
TFAutoModelForVision2Seq
[[autodoc]] TFAutoModelForVision2Seq
FlaxAutoModelForVision2Seq
[[autodoc]] FlaxAutoModelForVision2Seq"
"
GPT-J
Overview
The GPT-J model was released in the kingoflolz/mesh-transformer-jax repository by Ben Wang and Aran Komatsuzaki. It is a GPT-2-like
causal language model trained on the Pile dataset.
This model was contributed by Stella Biderman.
Tips:

To load GPT-J in float32 one would need at least 2x model size
  RAM: 1x for initial weights and another 1x to load the checkpoint. So for GPT-J it would take at least 48GB
  RAM to just load the model. To reduce the RAM usage there are a few options. The torch_dtype argument can be
  used to initialize the model in half-precision on a CUDA device only. There is also a fp16 branch which stores the fp16 weights,
  which could be used to further minimize the RAM usage:

thon

from transformers import GPTJForCausalLM
import torch
device = ""cuda""
model = GPTJForCausalLM.from_pretrained(
     ""EleutherAI/gpt-j-6B"",
     revision=""float16"",
     torch_dtype=torch.float16,
 ).to(device)

The model should fit on 16GB GPU for inference. For training/fine-tuning it would take much more GPU RAM. Adam
  optimizer for example makes four copies of the model: model, gradients, average and squared average of the gradients.
  So it would need at least 4x model size GPU memory, even with mixed precision as gradient updates are in fp32. This
  is not including the activations and data batches, which would again require some more GPU RAM. So one should explore
  solutions such as DeepSpeed, to train/fine-tune the model. Another option is to use the original codebase to
  train/fine-tune the model on TPU and then convert the model to Transformers format for inference. Instructions for
  that could be found here

Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer. These extra
  tokens are added for the sake of efficiency on TPUs. To avoid the mismatch between embedding matrix size and vocab
  size, the tokenizer for GPT-J contains 143 extra tokens
  <|extratoken_1|> <|extratoken_143|>, so the vocab_size of tokenizer also becomes 50400.

Generation
The [~generation.GenerationMixin.generate] method can be used to generate text using GPT-J
model.
thon

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(""EleutherAI/gpt-j-6B"")
tokenizer = AutoTokenizer.from_pretrained(""EleutherAI/gpt-j-6B"")
prompt = (
     ""In a shocking finding, scientists discovered a herd of unicorns living in a remote, ""
     ""previously unexplored valley, in the Andes Mountains. Even more surprising to the ""
     ""researchers was the fact that the unicorns spoke perfect English.""
 )
input_ids = tokenizer(prompt, return_tensors=""pt"").input_ids
gen_tokens = model.generate(
     input_ids,
     do_sample=True,
     temperature=0.9,
     max_length=100,
 )
gen_text = tokenizer.batch_decode(gen_tokens)[0]

or in float16 precision:
thon

from transformers import GPTJForCausalLM, AutoTokenizer
import torch
device = ""cuda""
model = GPTJForCausalLM.from_pretrained(""EleutherAI/gpt-j-6B"", torch_dtype=torch.float16).to(device)
tokenizer = AutoTokenizer.from_pretrained(""EleutherAI/gpt-j-6B"")
prompt = (
     ""In a shocking finding, scientists discovered a herd of unicorns living in a remote, ""
     ""previously unexplored valley, in the Andes Mountains. Even more surprising to the ""
     ""researchers was the fact that the unicorns spoke perfect English.""
 )
input_ids = tokenizer(prompt, return_tensors=""pt"").input_ids.to(device)
gen_tokens = model.generate(
     input_ids,
     do_sample=True,
     temperature=0.9,
     max_length=100,
 )
gen_text = tokenizer.batch_decode(gen_tokens)[0]

Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT-J. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

Description of GPT-J.
A blog on how to Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker.
A blog on how to Accelerate GPT-J inference with DeepSpeed-Inference on GPUs.
A blog post introducing GPT-J-6B: 6B JAX-Based Transformer. 🌎
A notebook for GPT-J-6B Inference Demo. 🌎
Another notebook demonstrating Inference with GPT-J-6B.  
Causal language modeling chapter of the 🤗 Hugging Face Course.
[GPTJForCausalLM] is supported by this causal language modeling example script, text generation example script, and notebook.
[TFGPTJForCausalLM] is supported by this causal language modeling example script and notebook.
[FlaxGPTJForCausalLM] is supported by this causal language modeling example script and notebook.

Documentation resources
- Text classification task guide
- Question answering task guide
- Causal language modeling task guide
GPTJConfig
[[autodoc]] GPTJConfig
    - all
GPTJModel
[[autodoc]] GPTJModel
    - forward
GPTJForCausalLM
[[autodoc]] GPTJForCausalLM
    - forward
GPTJForSequenceClassification
[[autodoc]] GPTJForSequenceClassification
    - forward
GPTJForQuestionAnswering
[[autodoc]] GPTJForQuestionAnswering
    - forward
TFGPTJModel
[[autodoc]] TFGPTJModel
    - call
TFGPTJForCausalLM
[[autodoc]] TFGPTJForCausalLM
    - call
TFGPTJForSequenceClassification
[[autodoc]] TFGPTJForSequenceClassification
    - call
TFGPTJForQuestionAnswering
[[autodoc]] TFGPTJForQuestionAnswering
    - call
FlaxGPTJModel
[[autodoc]] FlaxGPTJModel
    - call
FlaxGPTJForCausalLM
[[autodoc]] FlaxGPTJForCausalLM
    - call"
"
ViLT
Overview
The ViLT model was proposed in ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
by Wonjae Kim, Bokyung Son, Ildoo Kim. ViLT incorporates text embeddings into a Vision Transformer (ViT), allowing it to have a minimal design
for Vision-and-Language Pre-training (VLP).
The abstract from the paper is the following:
Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks.
Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision
(e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we
find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more
computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive
power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model,
Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically
simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of
times faster than previous VLP models, yet with competitive or better downstream task performance.
Tips:

The quickest way to get started with ViLT is by checking the example notebooks
  (which showcase both inference and fine-tuning on custom data).
ViLT is a model that takes both pixel_values and input_ids as input. One can use [ViltProcessor] to prepare data for the model.
  This processor wraps a image processor (for the image modality) and a tokenizer (for the language modality) into one.
ViLT is trained with images of various sizes: the authors resize the shorter edge of input images to 384 and limit the longer edge to
  under 640 while preserving the aspect ratio. To make batching of images possible, the authors use a pixel_mask that indicates
  which pixel values are real and which are padding. [ViltProcessor] automatically creates this for you.
The design of ViLT is very similar to that of a standard Vision Transformer (ViT). The only difference is that the model includes
  additional embedding layers for the language modality.

 ViLT architecture. Taken from the original paper. 
This model was contributed by nielsr. The original code can be found here.
Tips:

The PyTorch version of this model is only available in torch 1.10 and higher.

ViltConfig
[[autodoc]] ViltConfig
ViltFeatureExtractor
[[autodoc]] ViltFeatureExtractor
    - call
ViltImageProcessor
[[autodoc]] ViltImageProcessor
    - preprocess
ViltProcessor
[[autodoc]] ViltProcessor
    - call
ViltModel
[[autodoc]] ViltModel
    - forward
ViltForMaskedLM
[[autodoc]] ViltForMaskedLM
    - forward
ViltForQuestionAnswering
[[autodoc]] ViltForQuestionAnswering
    - forward
ViltForImagesAndTextClassification
[[autodoc]] ViltForImagesAndTextClassification
    - forward
ViltForImageAndTextRetrieval
[[autodoc]] ViltForImageAndTextRetrieval
    - forward
ViltForTokenClassification
[[autodoc]] ViltForTokenClassification
    - forward"
"
MPNet
Overview
The MPNet model was proposed in MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
masked language modeling and permuted language modeling for natural language understanding.
The abstract from the paper is the following:
BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
BERT, XLNet, RoBERTa) under the same model setting.
Tips:

MPNet doesn't have token_type_ids, you don't need to indicate which token belongs to which segment. just
  separate your segments with the separation token tokenizer.sep_token (or [sep]).

The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

MPNetConfig
[[autodoc]] MPNetConfig
MPNetTokenizer
[[autodoc]] MPNetTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
MPNetTokenizerFast
[[autodoc]] MPNetTokenizerFast
MPNetModel
[[autodoc]] MPNetModel
    - forward
MPNetForMaskedLM
[[autodoc]] MPNetForMaskedLM
    - forward
MPNetForSequenceClassification
[[autodoc]] MPNetForSequenceClassification
    - forward
MPNetForMultipleChoice
[[autodoc]] MPNetForMultipleChoice
    - forward
MPNetForTokenClassification
[[autodoc]] MPNetForTokenClassification
    - forward
MPNetForQuestionAnswering
[[autodoc]] MPNetForQuestionAnswering
    - forward
TFMPNetModel
[[autodoc]] TFMPNetModel
    - call
TFMPNetForMaskedLM
[[autodoc]] TFMPNetForMaskedLM
    - call
TFMPNetForSequenceClassification
[[autodoc]] TFMPNetForSequenceClassification
    - call
TFMPNetForMultipleChoice
[[autodoc]] TFMPNetForMultipleChoice
    - call
TFMPNetForTokenClassification
[[autodoc]] TFMPNetForTokenClassification
    - call
TFMPNetForQuestionAnswering
[[autodoc]] TFMPNetForQuestionAnswering
    - call"
"
EfficientNet
Overview
The EfficientNet model was proposed in EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 
by Mingxing Tan and Quoc V. Le. EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models.
The abstract from the paper is the following:
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.
To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.
This model was contributed by adirik.
The original code can be found here.
EfficientNetConfig
[[autodoc]] EfficientNetConfig
EfficientNetImageProcessor
[[autodoc]] EfficientNetImageProcessor
    - preprocess
EfficientNetModel
[[autodoc]] EfficientNetModel
    - forward
EfficientNetForImageClassification
[[autodoc]] EfficientNetForImageClassification
    - forward"
"
Hubert
Overview
Hubert was proposed in HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan
Salakhutdinov, Abdelrahman Mohamed.
The abstract from the paper is the following:
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are
multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training
phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we
propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an
offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our
approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined
acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised
clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means
teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the
state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h,
10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER
reduction on the more challenging dev-other and test-other evaluation subsets.
Tips:

Hubert is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
Hubert model was fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
  using [Wav2Vec2CTCTokenizer].

This model was contributed by patrickvonplaten.
Documentation resources

Audio classification task guide
Automatic speech recognition task guide

HubertConfig
[[autodoc]] HubertConfig
HubertModel
[[autodoc]] HubertModel
    - forward
HubertForCTC
[[autodoc]] HubertForCTC
    - forward
HubertForSequenceClassification
[[autodoc]] HubertForSequenceClassification
    - forward
TFHubertModel
[[autodoc]] TFHubertModel
    - call
TFHubertForCTC
[[autodoc]] TFHubertForCTC
    - call"
"
FlauBERT

Overview
The FlauBERT model was proposed in the paper FlauBERT: Unsupervised Language Model Pre-training for French by Hang Le et al. It's a transformer model pretrained using a masked language
modeling (MLM) objective (like BERT).
The abstract from the paper is the following:
Language models have become a key step to achieve state-of-the art results in many different Natural Language
Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way
to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their
contextualization at the sentence level. This has been widely demonstrated for English using contextualized
representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al.,
2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and
heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for
Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the
time they outperform other pretraining approaches. Different versions of FlauBERT as well as a unified evaluation
protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research
community for further reproducible experiments in French NLP.
This model was contributed by formiel. The original code can be found here.
Tips:
- Like RoBERTa, without the sentence ordering prediction (so just trained on the MLM objective).
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

FlaubertConfig
[[autodoc]] FlaubertConfig
FlaubertTokenizer
[[autodoc]] FlaubertTokenizer
FlaubertModel
[[autodoc]] FlaubertModel
    - forward
FlaubertWithLMHeadModel
[[autodoc]] FlaubertWithLMHeadModel
    - forward
FlaubertForSequenceClassification
[[autodoc]] FlaubertForSequenceClassification
    - forward
FlaubertForMultipleChoice
[[autodoc]] FlaubertForMultipleChoice
    - forward
FlaubertForTokenClassification
[[autodoc]] FlaubertForTokenClassification
    - forward
FlaubertForQuestionAnsweringSimple
[[autodoc]] FlaubertForQuestionAnsweringSimple
    - forward
FlaubertForQuestionAnswering
[[autodoc]] FlaubertForQuestionAnswering
    - forward
TFFlaubertModel
[[autodoc]] TFFlaubertModel
    - call
TFFlaubertWithLMHeadModel
[[autodoc]] TFFlaubertWithLMHeadModel
    - call
TFFlaubertForSequenceClassification
[[autodoc]] TFFlaubertForSequenceClassification
    - call
TFFlaubertForMultipleChoice
[[autodoc]] TFFlaubertForMultipleChoice
    - call
TFFlaubertForTokenClassification
[[autodoc]] TFFlaubertForTokenClassification
    - call
TFFlaubertForQuestionAnsweringSimple
[[autodoc]] TFFlaubertForQuestionAnsweringSimple
    - call"
"
SqueezeBERT
Overview
The SqueezeBERT model was proposed in SqueezeBERT: What can computer vision teach NLP about efficient neural networks? by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, Kurt W. Keutzer. It's a
bidirectional transformer similar to the BERT model. The key difference between the BERT architecture and the
SqueezeBERT architecture is that SqueezeBERT uses grouped convolutions
instead of fully-connected layers for the Q, K, V and FFN layers.
The abstract from the paper is the following:
Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets,
large computing systems, and better neural network models, natural language processing (NLP) technology has made
significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant
opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. In particular, we
consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today's
highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with
BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. In this work, we observe that methods
such as grouped convolutions have yielded significant speedups for computer vision networks, but many of these
techniques have not been adopted by NLP neural network designers. We demonstrate how to replace several operations in
self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called
SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test
set. The SqueezeBERT code will be released.
Tips:

SqueezeBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
  rather than the left.
SqueezeBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
  with a causal language modeling (CLM) objective are better in that regard.
For best results when finetuning on sequence classification tasks, it is recommended to start with the
  squeezebert/squeezebert-mnli-headless checkpoint.

This model was contributed by forresti.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

SqueezeBertConfig
[[autodoc]] SqueezeBertConfig
SqueezeBertTokenizer
[[autodoc]] SqueezeBertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
SqueezeBertTokenizerFast
[[autodoc]] SqueezeBertTokenizerFast
SqueezeBertModel
[[autodoc]] SqueezeBertModel
SqueezeBertForMaskedLM
[[autodoc]] SqueezeBertForMaskedLM
SqueezeBertForSequenceClassification
[[autodoc]] SqueezeBertForSequenceClassification
SqueezeBertForMultipleChoice
[[autodoc]] SqueezeBertForMultipleChoice
SqueezeBertForTokenClassification
[[autodoc]] SqueezeBertForTokenClassification
SqueezeBertForQuestionAnswering
[[autodoc]] SqueezeBertForQuestionAnswering"
"
GPTSAN-japanese
Overview
The GPTSAN-japanese model was released in the repository by Toshiyuki Sakamoto (tanreinama).
GPTSAN is a Japanese language model using Switch Transformer. It has the same structure as the model introduced as Prefix LM
in the T5 paper, and support both Text Generation and Masked Language Modeling tasks. These basic tasks similarly can
fine-tune for translation or summarization.
Generation
The generate() method can be used to generate text using GPTSAN-Japanese model.
thon

from transformers import AutoModel, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained(""Tanrei/GPTSAN-japanese"")
model = AutoModel.from_pretrained(""Tanrei/GPTSAN-japanese"").cuda()
x_tok = tokenizer(""は、"", prefix_text=""織田信長"", return_tensors=""pt"")
torch.manual_seed(0)
gen_tok = model.generate(x_tok.input_ids.cuda(), token_type_ids=x_tok.token_type_ids.cuda(), max_new_tokens=20)
tokenizer.decode(gen_tok[0])
'織田信長は、2004年に『戦国BASARA』のために、豊臣秀吉'

GPTSAN Features
GPTSAN has some unique features. It has a model structure of Prefix-LM. It works as a shifted Masked Language Model for Prefix Input tokens. Un-prefixed inputs behave like normal generative models.
The Spout vector is a GPTSAN specific input. Spout is pre-trained with random inputs, but you can specify a class of text or an arbitrary vector during fine-tuning. This allows you to indicate the tendency of the generated text.
GPTSAN has a sparse Feed Forward based on Switch-Transformer. You can also add other layers and train them partially. See the original GPTSAN repository for details.
Prefix-LM Model
GPTSAN has the structure of the model named Prefix-LM in the T5 paper. (The original GPTSAN repository calls it hybrid)
In GPTSAN, the Prefix part of Prefix-LM, that is, the input position that can be referenced by both tokens, can be specified with any length.
Arbitrary lengths can also be specified differently for each batch.
This length applies to the text entered in prefix_text for the tokenizer.
The tokenizer returns the mask of the Prefix part of Prefix-LM as token_type_ids.
The model treats the part where token_type_ids is 1 as a Prefix part, that is, the input can refer to both tokens before and after.
Tips:
Specifying the Prefix part is done with a mask passed to self-attention.
When token_type_ids=None or all zero, it is equivalent to regular causal mask
for example:

x_token = tokenizer(""ｱｲｳｴ"")
input_ids:      | SOT | SEG | ｱ | ｲ | ｳ | ｴ |
token_type_ids: | 1   | 0   | 0 | 0 | 0 | 0 |
prefix_lm_mask:
SOT | 1 0 0 0 0 0 |
SEG | 1 1 0 0 0 0 |
ｱ   | 1 1 1 0 0 0 |
ｲ   | 1 1 1 1 0 0 |
ｳ   | 1 1 1 1 1 0 |
ｴ   | 1 1 1 1 1 1 |
x_token = tokenizer("""", prefix_text=""ｱｲｳｴ"")
input_ids:      | SOT | ｱ | ｲ | ｳ | ｴ | SEG |
token_type_ids: | 1   | 1 | 1 | 1 | 1 | 0  |
prefix_lm_mask:
SOT | 1 1 1 1 1 0 |
ｱ   | 1 1 1 1 1 0 |
ｲ   | 1 1 1 1 1 0 |
ｳ   | 1 1 1 1 1 0 |
ｴ   | 1 1 1 1 1 0 |
SEG | 1 1 1 1 1 1 |
x_token = tokenizer(""ｳｴ"", prefix_text=""ｱｲ"")
input_ids:      | SOT | ｱ | ｲ | SEG | ｳ | ｴ |
token_type_ids: | 1   | 1 | 1 | 0   | 0 | 0 |
prefix_lm_mask:
SOT | 1 1 1 0 0 0 |
ｱ   | 1 1 1 0 0 0 |
ｲ   | 1 1 1 0 0 0 |
SEG | 1 1 1 1 0 0 |
ｳ   | 1 1 1 1 1 0 |
ｴ   | 1 1 1 1 1 1 |

Spout Vector
A Spout Vector is a special vector for controlling text generation.
This vector is treated as the first embedding in self-attention to bring extraneous attention to the generated tokens.
In the pre-trained model published from Tanrei/GPTSAN-japanese, the Spout Vector is a 128-dimensional vector that passes through 8 fully connected layers in the model and is projected into the space acting as external attention.
The Spout Vector projected by the fully connected layer is split to be passed to all self-attentions.
GPTSanJapaneseConfig
[[autodoc]] GPTSanJapaneseConfig
GPTSanJapaneseTokenizer
[[autodoc]] GPTSanJapaneseTokenizer
GPTSanJapaneseModel
[[autodoc]] GPTSanJapaneseModel
GPTSanJapaneseForConditionalGeneration
[[autodoc]] GPTSanJapaneseForConditionalGeneration
    - forward"
"
Dilated Neighborhood Attention Transformer
Overview
DiNAT was proposed in Dilated Neighborhood Attention Transformer
by Ali Hassani and Humphrey Shi.
It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context,
and shows significant performance improvements over it.
The abstract from the paper is the following:
*Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities,
domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have
also gained significant attention, thanks to their performance and easy integration into existing frameworks.
These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA)
or Swin Transformer's Shifted Window Self Attention. While effective at reducing self attention's quadratic complexity,
local attention weakens two of the most desirable properties of self attention: long range inter-dependency modeling,
and global receptive field. In this paper, we introduce Dilated Neighborhood Attention (DiNA), a natural, flexible and
efficient extension to NA that can capture more global context and expand receptive fields exponentially at no
additional cost. NA's local attention and DiNA's sparse global attention complement each other, and therefore we
introduce Dilated Neighborhood Attention Transformer (DiNAT), a new hierarchical vision transformer built upon both.
DiNAT variants enjoy significant improvements over strong baselines such as NAT, Swin, and ConvNeXt.
Our large model is faster and ahead of its Swin counterpart by 1.5% box AP in COCO object detection,
1.3% mask AP in COCO instance segmentation, and 1.1% mIoU in ADE20K semantic segmentation.
Paired with new frameworks, our large variant is the new state of the art panoptic segmentation model on COCO (58.2 PQ)
and ADE20K (48.5 PQ), and instance segmentation model on Cityscapes (44.5 AP) and ADE20K (35.4 AP) (no extra data).
It also matches the state of the art specialized semantic segmentation models on ADE20K (58.2 mIoU),
and ranks second on Cityscapes (84.5 mIoU) (no extra data). *
Tips:
- One can use the [AutoImageProcessor] API to prepare images for the model.
- DiNAT can be used as a backbone. When output_hidden_states = True,
it will output both hidden_states and reshaped_hidden_states. The reshaped_hidden_states have a shape of (batch, num_channels, height, width) rather than (batch_size, height, width, num_channels).
Notes:
- DiNAT depends on NATTEN's implementation of Neighborhood Attention and Dilated Neighborhood Attention.
You can install it with pre-built wheels for Linux by referring to shi-labs.com/natten, or build on your system by running pip install natten.
Note that the latter will likely take time to compile. NATTEN does not support Windows devices yet.
- Patch size of 4 is only supported at the moment.

 Neighborhood Attention with different dilation values.
Taken from the original paper.
This model was contributed by Ali Hassani.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DiNAT.

[DinatForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
DinatConfig
[[autodoc]] DinatConfig
DinatModel
[[autodoc]] DinatModel
    - forward
DinatForImageClassification
[[autodoc]] DinatForImageClassification
    - forward"
"
Wav2Vec2
Overview
The Wav2Vec2 model was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
The abstract from the paper is the following:
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on
transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks
the speech input in the latent space and solves a contrastive task defined over a quantization of the latent
representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the
clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state
of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
recognition with limited amounts of labeled data.
Tips:

Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
  using [Wav2Vec2CTCTokenizer].

This model was contributed by patrickvonplaten.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Wav2Vec2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A notebook on how to leverage a pretrained Wav2Vec2 model for emotion classification. 🌎
[Wav2Vec2ForCTC] is supported by this example script and notebook.
Audio classification task guide

A blog post on boosting Wav2Vec2 with n-grams in 🤗 Transformers.
A blog post on how to finetune Wav2Vec2 for English ASR with 🤗 Transformers.
A blog post on finetuning XLS-R for Multi-Lingual ASR with 🤗 Transformers.
A notebook on how to create YouTube captions from any video by transcribing audio with Wav2Vec2. 🌎
[Wav2Vec2ForCTC] is supported by a notebook on how to finetune a speech recognition model in English, and how to finetune a speech recognition model in any language.
Automatic speech recognition task guide

🚀 Deploy

A blog post on how to deploy Wav2Vec2 for Automatic Speech Recogntion with Hugging Face's Transformers & Amazon SageMaker.

Wav2Vec2Config
[[autodoc]] Wav2Vec2Config
Wav2Vec2CTCTokenizer
[[autodoc]] Wav2Vec2CTCTokenizer
    - call
    - save_vocabulary
    - decode
    - batch_decode
    - set_target_lang
Wav2Vec2FeatureExtractor
[[autodoc]] Wav2Vec2FeatureExtractor
    - call
Wav2Vec2Processor
[[autodoc]] Wav2Vec2Processor
    - call
    - pad
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
Wav2Vec2ProcessorWithLM
[[autodoc]] Wav2Vec2ProcessorWithLM
    - call
    - pad
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
Decoding multiple audios
If you are planning to decode multiple batches of audios, you should consider using [~Wav2Vec2ProcessorWithLM.batch_decode] and passing an instantiated multiprocessing.Pool.
Otherwise, [~Wav2Vec2ProcessorWithLM.batch_decode] performance will be slower than calling [~Wav2Vec2ProcessorWithLM.decode] for each audio individually, as it internally instantiates a new Pool for every call. See the example below:
thon

Let's see how to use a user-managed pool for batch decoding multiple audios
from multiprocessing import get_context
from transformers import AutoTokenizer, AutoProcessor, AutoModelForCTC
from datasets import load_dataset
import datasets
import torch
import model, feature extractor, tokenizer
model = AutoModelForCTC.from_pretrained(""patrickvonplaten/wav2vec2-base-100h-with-lm"").to(""cuda"")
processor = AutoProcessor.from_pretrained(""patrickvonplaten/wav2vec2-base-100h-with-lm"")
load example dataset
dataset = load_dataset(""hf-internal-testing/librispeech_asr_dummy"", ""clean"", split=""validation"")
dataset = dataset.cast_column(""audio"", datasets.Audio(sampling_rate=16_000))
def map_to_array(batch):
     batch[""speech""] = batch[""audio""][""array""]
     return batch
prepare speech data for batch inference
dataset = dataset.map(map_to_array, remove_columns=[""audio""])
def map_to_pred(batch, pool):
     inputs = processor(batch[""speech""], sampling_rate=16_000, padding=True, return_tensors=""pt"")
     inputs = {k: v.to(""cuda"") for k, v in inputs.items()}

     with torch.no_grad():
         logits = model(**inputs).logits
     transcription = processor.batch_decode(logits.cpu().numpy(), pool).text
     batch[""transcription""] = transcription
     return batch

note: pool should be instantiated after Wav2Vec2ProcessorWithLM.
otherwise, the LM won't be available to the pool's sub-processes
select number of processes and batch_size based on number of CPU cores available and on dataset size
with get_context(""fork"").Pool(processes=2) as pool:
     result = dataset.map(
         map_to_pred, batched=True, batch_size=2, fn_kwargs={""pool"": pool}, remove_columns=[""speech""]
     )
result[""transcription""][:2]
['MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL', ""NOR IS MISTER COULTER'S MANNER LESS INTERESTING THAN HIS MATTER""]

Wav2Vec2 specific outputs
[[autodoc]] models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
[[autodoc]] models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput
[[autodoc]] models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput
[[autodoc]] models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput
[[autodoc]] models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput
Wav2Vec2Model
[[autodoc]] Wav2Vec2Model
    - forward
Wav2Vec2ForCTC
[[autodoc]] Wav2Vec2ForCTC
    - forward
    - load_adapter
Wav2Vec2ForSequenceClassification
[[autodoc]] Wav2Vec2ForSequenceClassification
    - forward
Wav2Vec2ForAudioFrameClassification
[[autodoc]] Wav2Vec2ForAudioFrameClassification
    - forward
Wav2Vec2ForXVector
[[autodoc]] Wav2Vec2ForXVector
    - forward
Wav2Vec2ForPreTraining
[[autodoc]] Wav2Vec2ForPreTraining
    - forward
TFWav2Vec2Model
[[autodoc]] TFWav2Vec2Model
    - call
TFWav2Vec2ForSequenceClassification
[[autodoc]] TFWav2Vec2ForSequenceClassification
    - call
TFWav2Vec2ForCTC
[[autodoc]] TFWav2Vec2ForCTC
    - call
FlaxWav2Vec2Model
[[autodoc]] FlaxWav2Vec2Model
    - call
FlaxWav2Vec2ForCTC
[[autodoc]] FlaxWav2Vec2ForCTC
    - call
FlaxWav2Vec2ForPreTraining
[[autodoc]] FlaxWav2Vec2ForPreTraining
    - call"
"
DialoGPT
Overview
DialoGPT was proposed in DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao,
Jianfeng Gao, Jingjing Liu, Bill Dolan. It's a GPT2 Model trained on 147M conversation-like exchanges extracted from
Reddit.
The abstract from the paper is the following:
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained
transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning
from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human
both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems
that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline
systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response
generation and the development of more intelligent open-domain dialogue systems.
Tips:

DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
  than the left.
DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful
  at response generation in open-domain dialogue systems.
DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on DialoGPT's model card.

Training:
In order to train or fine-tune DialoGPT, one can use causal language modeling training. To cite the official paper: We
follow the OpenAI GPT-2 to model a multiturn dialogue session as a long text and frame the generation task as language
modeling. We first concatenate all dialog turns within a dialogue session into a long text x_1,, x_N (N is the
sequence length), ended by the end-of-text token. For more information please confer to the original paper.
DialoGPT's architecture is based on the GPT2 model, so one can refer to GPT2's documentation page.
The original code can be found here."
"
Mask2Former
Overview
The Mask2Former model was proposed in Masked-attention Mask Transformer for Universal Image Segmentation by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar. Mask2Former is a unified framework for panoptic, instance and semantic segmentation and features significant performance and efficiency improvements over MaskFormer.
The abstract from the paper is the following:
Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice
of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).
Tips:
- Mask2Former uses the same preprocessing and postprocessing steps as MaskFormer. Use [Mask2FormerImageProcessor] or [AutoImageProcessor] to prepare images and optional targets for the model.
- To get the final segmentation, depending on the task, you can call [~Mask2FormerImageProcessor.post_process_semantic_segmentation] or [~Mask2FormerImageProcessor.post_process_instance_segmentation] or [~Mask2FormerImageProcessor.post_process_panoptic_segmentation]. All three tasks can be solved using [Mask2FormerForUniversalSegmentation] output, panoptic segmentation accepts an optional label_ids_to_fuse argument to fuse instances of the target object/s (e.g. sky) together.

 Mask2Former architecture. Taken from the original paper. 
This model was contributed by Shivalika Singh and Alara Dirik. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mask2Former.

Demo notebooks regarding inference + fine-tuning Mask2Former on custom data can be found here.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.
MaskFormer specific outputs
[[autodoc]] models.mask2former.modeling_mask2former.Mask2FormerModelOutput
[[autodoc]] models.mask2former.modeling_mask2former.Mask2FormerForUniversalSegmentationOutput
Mask2FormerConfig
[[autodoc]] Mask2FormerConfig
Mask2FormerModel
[[autodoc]] Mask2FormerModel
    - forward
Mask2FormerForUniversalSegmentation
[[autodoc]] Mask2FormerForUniversalSegmentation
    - forward
Mask2FormerImageProcessor
[[autodoc]] Mask2FormerImageProcessor
    - preprocess
    - encode_inputs
    - post_process_semantic_segmentation
    - post_process_instance_segmentation
    - post_process_panoptic_segmentation"
"
UPerNet
Overview
The UPerNet model was proposed in Unified Perceptual Parsing for Scene Understanding
by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun. UPerNet is a general framework to effectively segment
a wide range of concepts from images, leveraging any vision backbone like ConvNeXt or Swin.
The abstract from the paper is the following:
Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes.

 UPerNet framework. Taken from the original paper. 
This model was contributed by nielsr. The original code is based on OpenMMLab's mmsegmentation here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with UPerNet.

Demo notebooks for UPerNet can be found here.
[UperNetForSemanticSegmentation] is supported by this example script and notebook.
See also: Semantic segmentation task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Usage
UPerNet is a general framework for semantic segmentation. It can be used with any vision backbone, like so:

from transformers import SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
backbone_config = SwinConfig(out_features=[""stage1"", ""stage2"", ""stage3"", ""stage4""])
config = UperNetConfig(backbone_config=backbone_config)
model = UperNetForSemanticSegmentation(config)

To use another vision backbone, like ConvNeXt, simply instantiate the model with the appropriate backbone:

from transformers import ConvNextConfig, UperNetConfig, UperNetForSemanticSegmentation
backbone_config = ConvNextConfig(out_features=[""stage1"", ""stage2"", ""stage3"", ""stage4""])
config = UperNetConfig(backbone_config=backbone_config)
model = UperNetForSemanticSegmentation(config)

Note that this will randomly initialize all the weights of the model.
UperNetConfig
[[autodoc]] UperNetConfig
UperNetForSemanticSegmentation
[[autodoc]] UperNetForSemanticSegmentation
    - forward"
"
Blenderbot Small
Note that [BlenderbotSmallModel] and
[BlenderbotSmallForConditionalGeneration] are only used in combination with the checkpoint
facebook/blenderbot-90M. Larger Blenderbot checkpoints should
instead be used with [BlenderbotModel] and
[BlenderbotForConditionalGeneration]
Overview
The Blender chatbot model was proposed in Recipes for building an open-domain chatbot Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.
The abstract of the paper is the following:
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that
scaling neural models in the number of parameters and the size of the data they are trained on gives improved results,
we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of
skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to
their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent
persona. We show that large scale models can learn these skills when given appropriate training data and choice of
generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models
and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn
dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
failure cases of our models.
Tips:

Blenderbot Small is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.

This model was contributed by patrickvonplaten. The authors' code can be
found here.
Documentation resources

Causal language modeling task guide
Translation task guide
Summarization task guide

BlenderbotSmallConfig
[[autodoc]] BlenderbotSmallConfig
BlenderbotSmallTokenizer
[[autodoc]] BlenderbotSmallTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
BlenderbotSmallTokenizerFast
[[autodoc]] BlenderbotSmallTokenizerFast
BlenderbotSmallModel
[[autodoc]] BlenderbotSmallModel
    - forward
BlenderbotSmallForConditionalGeneration
[[autodoc]] BlenderbotSmallForConditionalGeneration
    - forward
BlenderbotSmallForCausalLM
[[autodoc]] BlenderbotSmallForCausalLM
    - forward
TFBlenderbotSmallModel
[[autodoc]] TFBlenderbotSmallModel
    - call
TFBlenderbotSmallForConditionalGeneration
[[autodoc]] TFBlenderbotSmallForConditionalGeneration
    - call
FlaxBlenderbotSmallModel
[[autodoc]] FlaxBlenderbotSmallModel
    - call
    - encode
    - decode
FlaxBlenderbotForConditionalGeneration
[[autodoc]] FlaxBlenderbotSmallForConditionalGeneration
    - call
    - encode
    - decode"
"
Pyramid Vision Transformer (PVT)
Overview
The PVT model was proposed in
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. The PVT is a type of
vision transformer that utilizes a pyramid structure to make it an effective backbone for dense prediction tasks. Specifically
it allows for more fine-grained inputs (4 x 4 pixels per patch) to be used, while simultaneously shrinking the sequence length
of the Transformer as it deepens - reducing the computational cost. Additionally, a spatial-reduction attention (SRA) layer
is used to further reduce the resource consumption when learning high-resolution features.
The abstract from the paper is the following:
Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a 
simpler, convolution-free backbone network useful for many dense prediction tasks. Unlike the recently proposed Vision 
Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer 
(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several 
merits compared to current state of the arts. Different from ViT that typically yields low resolution outputs and 
incurs high computational and memory costs, PVT not only can be trained on dense partitions of an image to achieve high 
output resolution, which is important for dense prediction, but also uses a progressive shrinking pyramid to reduce the 
computations of large feature maps. PVT inherits the advantages of both CNN and Transformer, making it a unified 
backbone for various vision tasks without convolutions, where it can be used as a direct replacement for CNN backbones. 
We validate PVT through extensive experiments, showing that it boosts the performance of many downstream tasks, including
object detection, instance and semantic segmentation. For example, with a comparable number of parameters, PVT+RetinaNet 
achieves 40.4 AP on the COCO dataset, surpassing ResNet50+RetinNet (36.3 AP) by 4.1 absolute AP (see Figure 2). We hope 
that PVT could serve as an alternative and useful backbone for pixel-level predictions and facilitate future research.
This model was contributed by Xrenya. The original code can be found here.

PVTv1 on ImageNet-1K

| Model variant  |Size |Acc@1|Params (M)|
|--------------------|:-------:|:-------:|:------------:|
| PVT-Tiny           |    224  |   75.1  |     13.2     |
| PVT-Small          |    224  |   79.8  |     24.5     |
| PVT-Medium         |    224  |   81.2  |     44.2     |
| PVT-Large          |    224  |   81.7  |     61.4     |
PvtConfig
[[autodoc]] PvtConfig
PvtImageProcessor
[[autodoc]] PvtImageProcessor
    - preprocess
PvtForImageClassification
[[autodoc]] PvtForImageClassification
    - forward
PvtModel
[[autodoc]] PvtModel
    - forward"
"
LED
Overview
The LED model was proposed in Longformer: The Long-Document Transformer by Iz
Beltagy, Matthew E. Peters, Arman Cohan.
The abstract from the paper is the following:
Transformer-based models are unable to process long sequences due to their self-attention operation, which scales
quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention
mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or
longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local
windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we
evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In
contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our
pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting
long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization
dataset.
Tips:

[LEDForConditionalGeneration] is an extension of
  [BartForConditionalGeneration] exchanging the traditional self-attention layer with
  Longformer's chunked self-attention layer. [LEDTokenizer] is an alias of
  [BartTokenizer].
LED works very well on long-range sequence-to-sequence tasks where the input_ids largely exceed a length of
  1024 tokens.
LED pads the input_ids to be a multiple of config.attention_window if required. Therefore a small speed-up is
  gained, when [LEDTokenizer] is used with the pad_to_multiple_of argument.
LED makes use of global attention by means of the global_attention_mask (see
  [LongformerModel]). For summarization, it is advised to put global attention only on the first
  <s> token. For question answering, it is advised to put global attention on all tokens of the question.
To fine-tune LED on all 16384, gradient checkpointing can be enabled in case training leads to out-of-memory (OOM)
  errors. This can be done by executing model.gradient_checkpointing_enable(). 
 Moreover, the use_cache=False
  flag can be used to disable the caching mechanism to save memory.
A notebook showing how to evaluate LED, can be accessed here.
A notebook showing how to fine-tune LED, can be accessed here.
LED is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.

This model was contributed by patrickvonplaten.
Documentation resources

Text classification task guide
Question answering task guide
Translation task guide
Summarization task guide

LEDConfig
[[autodoc]] LEDConfig
LEDTokenizer
[[autodoc]] LEDTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
LEDTokenizerFast
[[autodoc]] LEDTokenizerFast
LED specific outputs
[[autodoc]] models.led.modeling_led.LEDEncoderBaseModelOutput
[[autodoc]] models.led.modeling_led.LEDSeq2SeqModelOutput
[[autodoc]] models.led.modeling_led.LEDSeq2SeqLMOutput
[[autodoc]] models.led.modeling_led.LEDSeq2SeqSequenceClassifierOutput
[[autodoc]] models.led.modeling_led.LEDSeq2SeqQuestionAnsweringModelOutput
[[autodoc]] models.led.modeling_tf_led.TFLEDEncoderBaseModelOutput
[[autodoc]] models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput
[[autodoc]] models.led.modeling_tf_led.TFLEDSeq2SeqLMOutput
LEDModel
[[autodoc]] LEDModel
    - forward
LEDForConditionalGeneration
[[autodoc]] LEDForConditionalGeneration
    - forward
LEDForSequenceClassification
[[autodoc]] LEDForSequenceClassification
    - forward
LEDForQuestionAnswering
[[autodoc]] LEDForQuestionAnswering
    - forward
TFLEDModel
[[autodoc]] TFLEDModel
    - call
TFLEDForConditionalGeneration
[[autodoc]] TFLEDForConditionalGeneration
    - call"
"
SEW
Overview
SEW (Squeezed and Efficient Wav2Vec) was proposed in Performance-Efficiency Trade-offs in Unsupervised Pre-training
for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q.
Weinberger, Yoav Artzi.
The abstract from the paper is the following:
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition
(ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance
and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a
pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a
variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x
inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference
time, SEW reduces word error rate by 25-50% across different model sizes.
Tips:

SEW is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
SEWForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded using
  [Wav2Vec2CTCTokenizer].

This model was contributed by anton-l.
Documentation resources

Audio classification task guide
Automatic speech recognition task guide

SEWConfig
[[autodoc]] SEWConfig
SEWModel
[[autodoc]] SEWModel
    - forward
SEWForCTC
[[autodoc]] SEWForCTC
    - forward
SEWForSequenceClassification
[[autodoc]] SEWForSequenceClassification
    - forward"
"
CodeGen
Overview
The CodeGen model was proposed in A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong.
CodeGen is an autoregressive language model for program synthesis trained sequentially on The Pile, BigQuery, and BigPython.
The abstract from the paper is the following:
Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. We train a family of large language models, called CodeGen, on natural language and programming language data. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI's Codex on the HumanEval benchmark. We make the training library JaxFormer including checkpoints available as open source contribution: this https URL. 
This model was contributed by Hiroaki Hayashi.
The original code can be found here.
Checkpoint Naming

CodeGen model checkpoints are available on different pre-training data with variable sizes.
The format is: Salesforce/codegen-{size}-{data}, where
size: 350M, 2B, 6B, 16B
data: 
nl: Pre-trained on the Pile
multi: Initialized with nl, then further pre-trained on multiple programming languages data
mono: Initialized with multi, then further pre-trained on Python data

For example, Salesforce/codegen-350M-mono offers a 350 million-parameter checkpoint pre-trained sequentially on the Pile, multiple programming languages, and Python.

How to use
thon

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = ""Salesforce/codegen-350M-mono""
model = AutoModelForCausalLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
text = ""def hello_world():""
completion = model.generate(**tokenizer(text, return_tensors=""pt""))
print(tokenizer.decode(completion[0]))
def hello_world():
    print(""Hello World"")

hello_world()

Documentation resources

Causal language modeling task guide

CodeGenConfig
[[autodoc]] CodeGenConfig
    - all
CodeGenTokenizer
[[autodoc]] CodeGenTokenizer
    - save_vocabulary
CodeGenTokenizerFast
[[autodoc]] CodeGenTokenizerFast
CodeGenModel
[[autodoc]] CodeGenModel
    - forward
CodeGenForCausalLM
[[autodoc]] CodeGenForCausalLM
    - forward"
"
XLM-ProphetNet

DISCLAIMER: If you see something strange, file a Github Issue and assign
@patrickvonplaten
Overview
The XLM-ProphetNet model was proposed in ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
Zhang, Ming Zhou on 13 Jan, 2020.
XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for ""ngram"" language modeling instead of
just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual
""wiki100"" Wikipedia dump.
The abstract from the paper is the following:
In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.
The Authors' code can be found here.
Tips:

XLM-ProphetNet's model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained on the cross-lingual dataset XGLUE.

Documentation resources

Causal language modeling task guide
Translation task guide
Summarization task guide

XLMProphetNetConfig
[[autodoc]] XLMProphetNetConfig
XLMProphetNetTokenizer
[[autodoc]] XLMProphetNetTokenizer
XLMProphetNetModel
[[autodoc]] XLMProphetNetModel
XLMProphetNetEncoder
[[autodoc]] XLMProphetNetEncoder
XLMProphetNetDecoder
[[autodoc]] XLMProphetNetDecoder
XLMProphetNetForConditionalGeneration
[[autodoc]] XLMProphetNetForConditionalGeneration
XLMProphetNetForCausalLM
[[autodoc]] XLMProphetNetForCausalLM"
"
BERTweet
Overview
The BERTweet model was proposed in BERTweet: A pre-trained language model for English Tweets by Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen.
The abstract from the paper is the following:
We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having
the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et
al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al.,
2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks:
Part-of-speech tagging, Named-entity recognition and text classification.
Example of use:
thon

import torch
from transformers import AutoModel, AutoTokenizer
bertweet = AutoModel.from_pretrained(""vinai/bertweet-base"")
For transformers v4.x+:
tokenizer = AutoTokenizer.from_pretrained(""vinai/bertweet-base"", use_fast=False)
For transformers v3.x:
tokenizer = AutoTokenizer.from_pretrained(""vinai/bertweet-base"")
INPUT TWEET IS ALREADY NORMALIZED!
line = ""SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:""
input_ids = torch.tensor([tokenizer.encode(line)])
with torch.no_grad():
     features = bertweet(input_ids)  # Models outputs are now tuples
With TensorFlow 2.0+:
from transformers import TFAutoModel
bertweet = TFAutoModel.from_pretrained(""vinai/bertweet-base"")

This model was contributed by dqnguyen. The original code can be found here.
BertweetTokenizer
[[autodoc]] BertweetTokenizer"
"
CPM
Overview
The CPM model was proposed in CPM: A Large-scale Generative Chinese Pre-trained Language Model by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin,
Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen,
Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
The abstract from the paper is the following:
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3,
with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even
zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus
of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the
Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best
of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained
language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation,
cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many
NLP tasks in the settings of few-shot (even zero-shot) learning.
This model was contributed by canwenxu. The original implementation can be found
here: https://github.com/TsinghuaAI/CPM-Generate
Note: We only have a tokenizer here, since the model architecture is the same as GPT-2.
CpmTokenizer
[[autodoc]] CpmTokenizer
CpmTokenizerFast
[[autodoc]] CpmTokenizerFast"
"
XLM-V
Overview
XLM-V is multilingual language model with a one million token vocabulary trained on 2.5TB of data from Common Crawl (same as XLM-R).
It was introduced in the XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
paper by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer and Madian Khabsa.
From the abstract of the XLM-V paper:
Large multilingual language models typically rely on a single vocabulary shared across 100+ languages.
As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged.
This vocabulary bottleneck limits the representational capabilities of multilingual models like XLM-R.
In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by
de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity
to achieve sufficient coverage for each individual language. Tokenizations using our vocabulary are typically
more semantically meaningful and shorter compared to XLM-R. Leveraging this improved vocabulary, we train XLM-V,
a multilingual language model with a one million token vocabulary. XLM-V outperforms XLM-R on every task we
tested on ranging from natural language inference (XNLI), question answering (MLQA, XQuAD, TyDiQA), and
named entity recognition (WikiAnn) to low-resource tasks (Americas NLI, MasakhaNER).
Tips:

XLM-V is compatible with the XLM-RoBERTa model architecture, only model weights from fairseq
  library had to be converted.
The XLMTokenizer implementation is used to load the vocab and performs tokenization.

A XLM-V (base size) model is available under the facebook/xlm-v-base identifier.
This model was contributed by stefan-it, including detailed experiments with XLM-V on downstream tasks.
The experiments repository can be found here."
"
Funnel Transformer

Overview
The Funnel Transformer model was proposed in the paper Funnel-Transformer: Filtering out Sequential Redundancy for
Efficient Language Processing. It is a bidirectional transformer model, like
BERT, but with a pooling operation after each block of layers, a bit like in traditional convolutional neural networks
(CNN) in computer vision.
The abstract from the paper is the following:
With the success of language pretraining, it is highly desirable to develop more efficient architectures of good
scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the
much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only
require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which
gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More
importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further
improve the model capacity. In addition, to perform token-level predictions as required by common pretraining
objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence
via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on
a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading
comprehension.
Tips:

Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers. This way, their length is divided by 2, which speeds up the computation of the next hidden states.
  The base model therefore has a final sequence length that is a quarter of the original one. This model can be used
  directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other
  tasks, the full model is used; this full model has a decoder that upsamples the final hidden states to the same
  sequence length as the input.
For tasks such as classification, this is not a problem, but for tasks like masked language modeling or token classification, we need a hidden state with the same sequence length as the original input. In those cases, the final hidden states are upsampled to the input sequence length and go through two additional layers. That's why there are two versions of each checkpoint. The version suffixed with “-base” contains only the three blocks, while the version without that suffix contains the three blocks and the upsampling head with its additional layers.
The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should be
  used for [FunnelModel], [FunnelForPreTraining],
  [FunnelForMaskedLM], [FunnelForTokenClassification] and
  [FunnelForQuestionAnswering]. The second ones should be used for
  [FunnelBaseModel], [FunnelForSequenceClassification] and
  [FunnelForMultipleChoice].

This model was contributed by sgugger. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

FunnelConfig
[[autodoc]] FunnelConfig
FunnelTokenizer
[[autodoc]] FunnelTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
FunnelTokenizerFast
[[autodoc]] FunnelTokenizerFast
Funnel specific outputs
[[autodoc]] models.funnel.modeling_funnel.FunnelForPreTrainingOutput
[[autodoc]] models.funnel.modeling_tf_funnel.TFFunnelForPreTrainingOutput
FunnelBaseModel
[[autodoc]] FunnelBaseModel
    - forward
FunnelModel
[[autodoc]] FunnelModel
    - forward
FunnelModelForPreTraining
[[autodoc]] FunnelForPreTraining
    - forward
FunnelForMaskedLM
[[autodoc]] FunnelForMaskedLM
    - forward
FunnelForSequenceClassification
[[autodoc]] FunnelForSequenceClassification
    - forward
FunnelForMultipleChoice
[[autodoc]] FunnelForMultipleChoice
    - forward
FunnelForTokenClassification
[[autodoc]] FunnelForTokenClassification
    - forward
FunnelForQuestionAnswering
[[autodoc]] FunnelForQuestionAnswering
    - forward
TFFunnelBaseModel
[[autodoc]] TFFunnelBaseModel
    - call
TFFunnelModel
[[autodoc]] TFFunnelModel
    - call
TFFunnelModelForPreTraining
[[autodoc]] TFFunnelForPreTraining
    - call
TFFunnelForMaskedLM
[[autodoc]] TFFunnelForMaskedLM
    - call
TFFunnelForSequenceClassification
[[autodoc]] TFFunnelForSequenceClassification
    - call
TFFunnelForMultipleChoice
[[autodoc]] TFFunnelForMultipleChoice
    - call
TFFunnelForTokenClassification
[[autodoc]] TFFunnelForTokenClassification
    - call
TFFunnelForQuestionAnswering
[[autodoc]] TFFunnelForQuestionAnswering
    - call"
"
NLLB
DISCLAIMER: The default behaviour for the tokenizer has recently been fixed (and thus changed)!
The previous version adds [self.eos_token_id, self.cur_lang_code] at the end of the token sequence for both target and source tokenization. This is wrong as the NLLB paper mentions (page 48, 6.1.1. Model Architecture) :
Note that we prefix the source sequence with the source language, as opposed to the target
language as previously done in several works (Arivazhagan et al., 2019; Johnson et al.,
2017). This is primarily because we prioritize optimizing zero-shot performance of our
model on any pair of 200 languages at a minor cost to supervised performance.
Previous behaviour:
thon

from transformers import NllbTokenizer
tokenizer = NllbTokenizer.from_pretrained(""facebook/nllb-200-distilled-600M"")
tokenizer(""How was your day?"").input_ids
[13374, 1398, 4260, 4039, 248130, 2, 256047]
2: ''
256047 : 'eng_Latn'

New behaviour

thon

from transformers import NllbTokenizer
tokenizer = NllbTokenizer.from_pretrained(""facebook/nllb-200-distilled-600M"")
tokenizer(""How was your day?"").input_ids
[256047, 13374, 1398, 4260, 4039, 248130, 2]
 

Enabling the old behaviour can be done as follows:
thon

from transformers import NllbTokenizer
tokenizer = NllbTokenizer.from_pretrained(""facebook/nllb-200-distilled-600M"", legacy_behaviour=True)

For more details, feel free to check the linked PR and Issue.
Overview of NLLB
The NLLB model was presented in No Language Left Behind: Scaling Human-Centered Machine Translation by Marta R. Costa-jussà, James Cross, Onur Çelebi,
Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula,
Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews,
Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers,
Safiyyah Saleem, Holger Schwenk, and Jeff Wang.
The abstract of the paper is the following:
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the
200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by
first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed
at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of
Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training
improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using
a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety.
Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.
This implementation contains the dense models available on release.
The sparse model NLLB-MoE (Mixture of Expert) is now available! More details here
This model was contributed by Lysandre. The authors' code can be found here.
Generating with NLLB
While generating the target text set the forced_bos_token_id to the target language id. The following
example shows how to translate English to French using the facebook/nllb-200-distilled-600M model.
Note that we're using the BCP-47 code for French fra_Latn. See here
for the list of all BCP-47 in the Flores 200 dataset.
thon

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""facebook/nllb-200-distilled-600M"")
model = AutoModelForSeq2SeqLM.from_pretrained(""facebook/nllb-200-distilled-600M"")
article = ""UN Chief says there is no military solution in Syria""
inputs = tokenizer(article, return_tensors=""pt"")
translated_tokens = model.generate(
     **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[""fra_Latn""], max_length=30
 )
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
Le chef de l'ONU dit qu'il n'y a pas de solution militaire en Syrie

Generating from any other language than English
English (eng_Latn) is set as the default language from which to translate. In order to specify that you'd like to translate from a different language,
you should specify the BCP-47 code in the src_lang keyword argument of the tokenizer initialization.
See example below for a translation from romanian to german:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
     ""facebook/nllb-200-distilled-600M"", use_auth_token=True, src_lang=""ron_Latn""
 )
model = AutoModelForSeq2SeqLM.from_pretrained(""facebook/nllb-200-distilled-600M"", use_auth_token=True)
article = ""Şeful ONU spune că nu există o soluţie militară în Siria""
inputs = tokenizer(article, return_tensors=""pt"")
translated_tokens = model.generate(
     **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[""deu_Latn""], max_length=30
 )
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
UN-Chef sagt, es gibt keine militärische Lösung in Syrien

Documentation resources

Translation task guide
Summarization task guide

NllbTokenizer
[[autodoc]] NllbTokenizer
    - build_inputs_with_special_tokens
NllbTokenizerFast
[[autodoc]] NllbTokenizerFast"
"
Blenderbot
DISCLAIMER: If you see something strange, file a Github Issue .
Overview
The Blender chatbot model was proposed in Recipes for building an open-domain chatbot Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.
The abstract of the paper is the following:
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that
scaling neural models in the number of parameters and the size of the data they are trained on gives improved results,
we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of
skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to
their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent
persona. We show that large scale models can learn these skills when given appropriate training data and choice of
generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models
and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn
dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
failure cases of our models.
Tips:

Blenderbot is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.

This model was contributed by sshleifer. The authors' code can be found here .
Implementation Notes

Blenderbot uses a standard seq2seq model transformer based architecture.
Available checkpoints can be found in the model hub.
This is the default Blenderbot model class. However, some smaller checkpoints, such as
  facebook/blenderbot_small_90M, have a different architecture and consequently should be used with
  BlenderbotSmall.

Usage
Here is an example of model usage:
thon

from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
mname = ""facebook/blenderbot-400M-distill""
model = BlenderbotForConditionalGeneration.from_pretrained(mname)
tokenizer = BlenderbotTokenizer.from_pretrained(mname)
UTTERANCE = ""My friends are cool but they eat too many carbs.""
inputs = tokenizer([UTTERANCE], return_tensors=""pt"")
reply_ids = model.generate(**inputs)
print(tokenizer.batch_decode(reply_ids))
["" That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?""]

Documentation resources

Causal language modeling task guide
Translation task guide
Summarization task guide

BlenderbotConfig
[[autodoc]] BlenderbotConfig
BlenderbotTokenizer
[[autodoc]] BlenderbotTokenizer
    - build_inputs_with_special_tokens
BlenderbotTokenizerFast
[[autodoc]] BlenderbotTokenizerFast
    - build_inputs_with_special_tokens
BlenderbotModel
See transformers.BartModel for arguments to forward and generate
[[autodoc]] BlenderbotModel
    - forward
BlenderbotForConditionalGeneration
See [~transformers.BartForConditionalGeneration] for arguments to forward and generate
[[autodoc]] BlenderbotForConditionalGeneration
    - forward
BlenderbotForCausalLM
[[autodoc]] BlenderbotForCausalLM
    - forward
TFBlenderbotModel
[[autodoc]] TFBlenderbotModel
    - call
TFBlenderbotForConditionalGeneration
[[autodoc]] TFBlenderbotForConditionalGeneration
    - call
FlaxBlenderbotModel
[[autodoc]] FlaxBlenderbotModel
    - call
    - encode
    - decode
FlaxBlenderbotForConditionalGeneration
[[autodoc]] FlaxBlenderbotForConditionalGeneration
    - call
    - encode
    - decode"
"
InstructBLIP
Overview
The InstructBLIP model was proposed in InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
InstructBLIP leverages the BLIP-2 architecture for visual instruction tuning.
The abstract from the paper is the following:
General-purpose language models that can solve various language-domain tasks have emerged driven by the pre-training and instruction-tuning pipeline. However, building general-purpose vision-language models is challenging due to the increased task discrepancy introduced by the additional visual input. Although vision-language pre-training has been widely studied, vision-language instruction tuning remains relatively less explored. In this paper, we conduct a systematic and comprehensive study on vision-language instruction tuning based on the pre-trained BLIP-2 models. We gather a wide variety of 26 publicly available datasets, transform them into instruction tuning format and categorize them into two clusters for held-in instruction tuning and held-out zero-shot evaluation. Additionally, we introduce instruction-aware visual feature extraction, a crucial method that enables the model to extract informative features tailored to the given instruction. The resulting InstructBLIP models achieve state-of-the-art zero-shot performance across all 13 held-out datasets, substantially outperforming BLIP-2 and the larger Flamingo. Our models also lead to state-of-the-art performance when finetuned on individual downstream tasks (e.g., 90.7% accuracy on ScienceQA IMG). Furthermore, we qualitatively demonstrate the advantages of InstructBLIP over concurrent multimodal models.
Tips:

InstructBLIP uses the same architecture as BLIP-2 with a tiny but important difference: it also feeds the text prompt (instruction) to the Q-Former.

 InstructBLIP architecture. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
InstructBlipConfig
[[autodoc]] InstructBlipConfig
    - from_vision_qformer_text_configs
InstructBlipVisionConfig
[[autodoc]] InstructBlipVisionConfig
InstructBlipQFormerConfig
[[autodoc]] InstructBlipQFormerConfig
InstructBlipProcessor
[[autodoc]] InstructBlipProcessor
InstructBlipVisionModel
[[autodoc]] InstructBlipVisionModel
    - forward
InstructBlipQFormerModel
[[autodoc]] InstructBlipQFormerModel
    - forward
InstructBlipForConditionalGeneration
[[autodoc]] InstructBlipForConditionalGeneration
    - forward
    - generate"
"
BLIP-2
Overview
The BLIP-2 model was proposed in BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by
Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi. BLIP-2 leverages frozen pre-trained image encoders and large language models (LLMs) by training a lightweight, 12-layer Transformer
encoder in between them, achieving state-of-the-art performance on various vision-language tasks. Most notably, BLIP-2 improves upon Flamingo, an 80 billion parameter model, by 8.7%
on zero-shot VQAv2 with 54x fewer trainable parameters. 
The abstract from the paper is the following:
The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.
Tips:

BLIP-2 can be used for conditional text generation given an image and an optional text prompt. At inference time, it's recommended to use the [generate] method.
One can use [Blip2Processor] to prepare images for the model, and decode the predicted tokens ID's back to text.

 
 BLIP-2 architecture. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BLIP-2.

Demo notebooks for BLIP-2 for image captioning, visual question answering (VQA) and chat-like conversations can be found here.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Blip2Config
[[autodoc]] Blip2Config
    - from_vision_qformer_text_configs
Blip2VisionConfig
[[autodoc]] Blip2VisionConfig
Blip2QFormerConfig
[[autodoc]] Blip2QFormerConfig
Blip2Processor
[[autodoc]] Blip2Processor
Blip2VisionModel
[[autodoc]] Blip2VisionModel
    - forward
Blip2QFormerModel
[[autodoc]] Blip2QFormerModel
    - forward
Blip2Model
[[autodoc]] Blip2Model
    - forward
    - get_text_features
    - get_image_features
    - get_qformer_features
Blip2ForConditionalGeneration
[[autodoc]] Blip2ForConditionalGeneration
    - forward
    - generate"
"
Wav2Vec2-Conformer
Overview
The Wav2Vec2-Conformer was added to an updated version of fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
The official results of the model can be found in Table 3 and Table 4 of the paper.
The Wav2Vec2-Conformer weights were released by the Meta AI team within the Fairseq library.
Tips:

Wav2Vec2-Conformer follows the same architecture as Wav2Vec2, but replaces the Attention-block with a Conformer-block
  as introduced in Conformer: Convolution-augmented Transformer for Speech Recognition.
For the same number of layers, Wav2Vec2-Conformer requires more parameters than Wav2Vec2, but also yields 
an improved word error rate.
Wav2Vec2-Conformer uses the same tokenizer and feature extractor as Wav2Vec2.
Wav2Vec2-Conformer can use either no relative position embeddings, Transformer-XL-like position embeddings, or
  rotary position embeddings by setting the correct config.position_embeddings_type.

This model was contributed by patrickvonplaten.
The original code can be found here.
Documentation resources

Audio classification task guide
Automatic speech recognition task guide

Wav2Vec2ConformerConfig
[[autodoc]] Wav2Vec2ConformerConfig
Wav2Vec2Conformer specific outputs
[[autodoc]] models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput
Wav2Vec2ConformerModel
[[autodoc]] Wav2Vec2ConformerModel
    - forward
Wav2Vec2ConformerForCTC
[[autodoc]] Wav2Vec2ConformerForCTC
    - forward
Wav2Vec2ConformerForSequenceClassification
[[autodoc]] Wav2Vec2ConformerForSequenceClassification
    - forward
Wav2Vec2ConformerForAudioFrameClassification
[[autodoc]] Wav2Vec2ConformerForAudioFrameClassification
    - forward
Wav2Vec2ConformerForXVector
[[autodoc]] Wav2Vec2ConformerForXVector
    - forward
Wav2Vec2ConformerForPreTraining
[[autodoc]] Wav2Vec2ConformerForPreTraining
    - forward"
"
MusicGen
Overview
The MusicGen model was proposed in the paper Simple and Controllable Music Generation
by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
MusicGen is a single stage auto-regressive Transformer model capable of generating high-quality music samples conditioned
on text descriptions or audio prompts. The text descriptions are passed through a frozen text encoder model to obtain a
sequence of hidden-state representations. MusicGen is then trained to predict discrete audio tokens, or audio codes,
conditioned on these hidden-states. These audio tokens are then decoded using an audio compression model, such as EnCodec,
to recover the audio waveform.
Through an efficient token interleaving pattern, MusicGen does not require a self-supervised semantic representation of
the text/audio prompts, thus eliminating the need to cascade multiple models to predict a set of codebooks (e.g.
hierarchically or upsampling). Instead, it is able to generate all the codebooks in a single forward pass.
The abstract from the paper is the following:
We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates
over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised
of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for
cascading several models, e.g., hierarchically or upsampling. Following this approach, we demonstrate how MusicGen
can generate high-quality samples, while being conditioned on textual description or melodic features, allowing better
controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human
studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark.
Through ablation studies, we shed light over the importance of each of the components comprising MusicGen.
This model was contributed by sanchit-gandhi. The original code can be found
here. The pre-trained checkpoints can be found on the
Hugging Face Hub.
Generation
MusicGen is compatible with two generation modes: greedy and sampling. In practice, sampling leads to significantly
better results than greedy, thus we encourage sampling mode to be used where possible. Sampling is enabled by default,
and can be explicitly specified by setting do_sample=True in the call to [MusicgenForConditionalGeneration.generate],
or by overriding the model's generation config (see below).
Unconditional Generation
The inputs for unconditional (or 'null') generation can be obtained through the method
[MusicgenForConditionalGeneration.get_unconditional_inputs]:
thon

from transformers import MusicgenForConditionalGeneration
model = MusicgenForConditionalGeneration.from_pretrained(""facebook/musicgen-small"")
unconditional_inputs = model.get_unconditional_inputs(num_samples=1)
audio_values = model.generate(**unconditional_inputs, do_sample=True, max_new_tokens=256)

The audio outputs are a three-dimensional Torch tensor of shape (batch_size, num_channels, sequence_length). To listen
to the generated audio samples, you can either play them in an ipynb notebook:
thon
from IPython.display import Audio
sampling_rate = model.config.audio_encoder.sampling_rate
Audio(audio_values[0].numpy(), rate=sampling_rate)

Or save them as a .wav file using a third-party library, e.g. scipy:
thon

import scipy
sampling_rate = model.config.audio_encoder.sampling_rate
scipy.io.wavfile.write(""musicgen_out.wav"", rate=sampling_rate, data=audio_values[0, 0].numpy())

Text-Conditional Generation
The model can generate an audio sample conditioned on a text prompt through use of the [MusicgenProcessor] to pre-process
the inputs:
thon

from transformers import AutoProcessor, MusicgenForConditionalGeneration
processor = AutoProcessor.from_pretrained(""facebook/musicgen-small"")
model = MusicgenForConditionalGeneration.from_pretrained(""facebook/musicgen-small"")
inputs = processor(
     text=[""80s pop track with bassy drums and synth"", ""90s rock song with loud guitars and heavy drums""],
     padding=True,
     return_tensors=""pt"",
 )
audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)

The guidance_scale is used in classifier free guidance (CFG), setting the weighting between the conditional logits
(which are predicted from the text prompts) and the unconditional logits (which are predicted from an unconditional or
'null' prompt). Higher guidance scale encourages the model to generate samples that are more closely linked to the input
prompt, usually at the expense of poorer audio quality. CFG is enabled by setting guidance_scale > 1. For best results,
use guidance_scale=3 (default).
Audio-Prompted Generation
The same [MusicgenProcessor] can be used to pre-process an audio prompt that is used for audio continuation. In the
following example, we load an audio file using the 🤗 Datasets library, which can be pip installed through the command
below:
pip install --upgrade pip
pip install datasets[audio]
thon

from transformers import AutoProcessor, MusicgenForConditionalGeneration
from datasets import load_dataset
processor = AutoProcessor.from_pretrained(""facebook/musicgen-small"")
model = MusicgenForConditionalGeneration.from_pretrained(""facebook/musicgen-small"")
dataset = load_dataset(""sanchit-gandhi/gtzan"", split=""train"", streaming=True)
sample = next(iter(dataset))[""audio""]
take the first half of the audio sample
sample[""array""] = sample[""array""][: len(sample[""array""]) // 2]
inputs = processor(
     audio=sample[""array""],
     sampling_rate=sample[""sampling_rate""],
     text=[""80s blues track with groovy saxophone""],
     padding=True,
     return_tensors=""pt"",
 )
audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)

For batched audio-prompted generation, the generated audio_values can be post-processed to remove padding by using the
[MusicgenProcessor] class:
thon

from transformers import AutoProcessor, MusicgenForConditionalGeneration
from datasets import load_dataset
processor = AutoProcessor.from_pretrained(""facebook/musicgen-small"")
model = MusicgenForConditionalGeneration.from_pretrained(""facebook/musicgen-small"")
dataset = load_dataset(""sanchit-gandhi/gtzan"", split=""train"", streaming=True)
sample = next(iter(dataset))[""audio""]
take the first quarter of the audio sample
sample_1 = sample[""array""][: len(sample[""array""]) // 4]
take the first half of the audio sample
sample_2 = sample[""array""][: len(sample[""array""]) // 2]
inputs = processor(
     audio=[sample_1, sample_2],
     sampling_rate=sample[""sampling_rate""],
     text=[""80s blues track with groovy saxophone"", ""90s rock song with loud guitars and heavy drums""],
     padding=True,
     return_tensors=""pt"",
 )
audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)
post-process to remove padding from the batched audio
audio_values = processor.batch_decode(audio_values, padding_mask=inputs.padding_mask)

Generation Configuration
The default parameters that control the generation process, such as sampling, guidance scale and number of generated 
tokens, can be found in the model's generation config, and updated as desired:
thon

from transformers import MusicgenForConditionalGeneration
model = MusicgenForConditionalGeneration.from_pretrained(""facebook/musicgen-small"")
inspect the default generation config
model.generation_config
increase the guidance scale to 4.0
model.generation_config.guidance_scale = 4.0
decrease the max length to 256 tokens
model.generation_config.max_length = 256

Note that any arguments passed to the generate method will supersede those in the generation config, so setting 
do_sample=False in the call to generate will supersede the setting of model.generation_config.do_sample in the 
generation config.
Model Structure
The MusicGen model can be de-composed into three distinct stages:
1. Text encoder: maps the text inputs to a sequence of hidden-state representations. The pre-trained MusicGen models use a frozen text encoder from either T5 or Flan-T5
2. MusicGen decoder: a language model (LM) that auto-regressively generates audio tokens (or codes) conditional on the encoder hidden-state representations
3. Audio encoder/decoder: used to encode an audio prompt to use as prompt tokens, and recover the audio waveform from the audio tokens predicted by the decoder
Thus, the MusicGen model can either be used as a standalone decoder model, corresponding to the class [MusicgenForCausalLM],
or as a composite model that includes the text encoder and audio encoder/decoder, corresponding to the class
[MusicgenForConditionalGeneration].
Since the text encoder and audio encoder/decoder models are frozen during training, the MusicGen decoder [MusicgenForCausalLM]
can be trained standalone on a dataset of encoder hidden-states and audio codes. For inference, the trained decoder can
be combined with the frozen text encoder and audio encoder/decoders to recover the composite [MusicgenForConditionalGeneration]
model.
Below, we demonstrate how to construct the composite [MusicgenForConditionalGeneration] model from its three constituent
parts, as would typically be done following training of the MusicGen decoder LM:
thon

from transformers import AutoConfig, AutoModelForTextEncoding, AutoModel, MusicgenForCausalLM, MusicgenForConditionalGeneration
text_encoder = AutoModelForTextEncoding.from_pretrained(""t5-base"")
audio_encoder = AutoModel.from_pretrained(""facebook/encodec_32khz"")
decoder_config = AutoConfig.from_pretrained(""facebook/musicgen-small"").decoder
decoder = MusicgenForCausalLM.from_pretrained(""facebook/musicgen-small"", **decoder_config)
model = MusicgenForConditionalGeneration.from_sub_models_pretrained(text_encoder, audio_encoder, decoder)

If only the decoder needs to be loaded from the pre-trained checkpoint for the composite model, it can be loaded by first 
specifying the correct config, or be accessed through the .decoder attribute of the composite model:
thon

from transformers import AutoConfig, MusicgenForCausalLM, MusicgenForConditionalGeneration
Option 1: get decoder config and pass to .from_pretrained
decoder_config = AutoConfig.from_pretrained(""facebook/musicgen-small"").decoder
decoder = MusicgenForCausalLM.from_pretrained(""facebook/musicgen-small"", **decoder_config)
Option 2: load the entire composite model, but only return the decoder
decoder = MusicgenForConditionalGeneration.from_pretrained(""facebook/musicgen-small"").decoder

Tips:
* MusicGen is trained on the 32kHz checkpoint of Encodec. You should ensure you use a compatible version of the Encodec model.
* Sampling mode tends to deliver better results than greedy - you can toggle sampling with the variable do_sample in the call to [MusicgenForConditionalGeneration.generate]
MusicgenDecoderConfig
[[autodoc]] MusicgenDecoderConfig
MusicgenConfig
[[autodoc]] MusicgenConfig
MusicgenProcessor
[[autodoc]] MusicgenProcessor
MusicgenModel
[[autodoc]] MusicgenModel
    - forward
MusicgenForCausalLM
[[autodoc]] MusicgenForCausalLM
    - forward
MusicgenForConditionalGeneration
[[autodoc]] MusicgenForConditionalGeneration
    - forward"
"
XLS-R
Overview
The XLS-R model was proposed in XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman
Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
The abstract from the paper is the following:
This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0.
We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128
languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range
of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation
benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into
English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as
VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107
language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform
English-only pretraining when translating English speech into other languages, a setting which favors monolingual
pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.
Tips:

XLS-R is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
XLS-R model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using
  [Wav2Vec2CTCTokenizer].

Relevant checkpoints can be found under https://huggingface.co/models?other=xls_r.
XLS-R's architecture is based on the Wav2Vec2 model, so one can refer to Wav2Vec2's documentation page.
The original code can be found here."
"
XLNet

Overview
The XLNet model was proposed in XLNet: Generalized Autoregressive Pretraining for Language Understanding by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov,
Quoc V. Le. XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method to learn
bidirectional contexts by maximizing the expected likelihood over all permutations of the input sequence factorization
order.
The abstract from the paper is the following:
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves
better performance than pretraining approaches based on autoregressive language modeling. However, relying on
corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a
pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all
permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into
pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large
margin, including question answering, natural language inference, sentiment analysis, and document ranking.
Tips:

The specific attention pattern can be controlled at training and test time using the perm_mask input.
Due to the difficulty of training a fully auto-regressive model over various factorization order, XLNet is pretrained
  using only a sub-set of the output tokens as target which are selected with the target_mapping input.
To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the perm_mask and
  target_mapping inputs to control the attention span and outputs (see examples in
  examples/pytorch/text-generation/run_generation.py)
XLNet is one of the few models that has no sequence length limit.
XLNet is not a traditional autoregressive model but uses a training strategy that builds on that. It permutes the tokens in the sentence, then allows the model to use the last n tokens to predict the token n+1. Since this is all done with a mask, the sentence is actually fed in the model in the right order, but instead of masking the first n tokens for n+1, XLNet uses a mask that hides the previous tokens in some given permutation of 1,…,sequence length.
XLNet also uses the same recurrence mechanism as Transformer-XL to build long-term dependencies.

This model was contributed by thomwolf. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Multiple choice task guide

XLNetConfig
[[autodoc]] XLNetConfig
XLNetTokenizer
[[autodoc]] XLNetTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
XLNetTokenizerFast
[[autodoc]] XLNetTokenizerFast
XLNet specific outputs
[[autodoc]] models.xlnet.modeling_xlnet.XLNetModelOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetLMHeadModelOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForSequenceClassificationOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForMultipleChoiceOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForTokenClassificationOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringSimpleOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetModelOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetLMHeadModelOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput
XLNetModel
[[autodoc]] XLNetModel
    - forward
XLNetLMHeadModel
[[autodoc]] XLNetLMHeadModel
    - forward
XLNetForSequenceClassification
[[autodoc]] XLNetForSequenceClassification
    - forward
XLNetForMultipleChoice
[[autodoc]] XLNetForMultipleChoice
    - forward
XLNetForTokenClassification
[[autodoc]] XLNetForTokenClassification
    - forward
XLNetForQuestionAnsweringSimple
[[autodoc]] XLNetForQuestionAnsweringSimple
    - forward
XLNetForQuestionAnswering
[[autodoc]] XLNetForQuestionAnswering
    - forward
TFXLNetModel
[[autodoc]] TFXLNetModel
    - call
TFXLNetLMHeadModel
[[autodoc]] TFXLNetLMHeadModel
    - call
TFXLNetForSequenceClassification
[[autodoc]] TFXLNetForSequenceClassification
    - call
TFLNetForMultipleChoice
[[autodoc]] TFXLNetForMultipleChoice
    - call
TFXLNetForTokenClassification
[[autodoc]] TFXLNetForTokenClassification
    - call
TFXLNetForQuestionAnsweringSimple
[[autodoc]] TFXLNetForQuestionAnsweringSimple
    - call"
"
Pix2Struct
Overview
The Pix2Struct model was proposed in Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
The abstract from the paper is the following:

Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy, we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions are rendered directly on top of the input image. For the first time, we show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images.

Tips:
Pix2Struct has been fine tuned on a variety of tasks and datasets, ranging from image captioning, visual question answering (VQA) over different inputs (books, charts, science diagrams), captioning UI components etc. The full list can be found in Table 1 of the paper.
We therefore advise you to use these models for the tasks they have been fine tuned on. For instance, if you want to use Pix2Struct for UI captioning, you should use the model fine tuned on the UI dataset. If you want to use Pix2Struct for image captioning, you should use the model fine tuned on the natural images captioning dataset and so on.
If you want to use the model to perform conditional text captioning, make sure to use the processor with add_special_tokens=False.
This model was contributed by ybelkada.
The original code can be found here.
Resources

Fine-tuning Notebook
All models

Pix2StructConfig
[[autodoc]] Pix2StructConfig
    - from_text_vision_configs
Pix2StructTextConfig
[[autodoc]] Pix2StructTextConfig
Pix2StructVisionConfig
[[autodoc]] Pix2StructVisionConfig
Pix2StructProcessor
[[autodoc]] Pix2StructProcessor
Pix2StructImageProcessor
[[autodoc]] Pix2StructImageProcessor
    - preprocess
Pix2StructTextModel
[[autodoc]] Pix2StructTextModel
    - forward
Pix2StructVisionModel
[[autodoc]] Pix2StructVisionModel
    - forward
Pix2StructForConditionalGeneration
[[autodoc]] Pix2StructForConditionalGeneration
    - forward"
"
GPTBigCode
Overview
The GPTBigCode model was proposed in SantaCoder: don't reach for the stars! by BigCode. The listed authors are: Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
The abstract from the paper is the following:uery
The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at this https URL.
The model is a an optimized GPT2 model with support for Multi-Query Attention.
Technical details
The main differences compared to GPT2.
- Added support for Multi-Query Attention.
- Use gelu_pytorch_tanh instead of classic gelu.
- Avoid unnecessary synchronizations (this has since been added to GPT2 in #20061, but wasn't in the reference codebase).
- Use Linear layers instead of Conv1D (good speedup but makes the checkpoints incompatible).
- Merge _attn and _upcast_and_reordered_attn. Always merge the matmul with scaling. Rename reorder_and_upcast_attn->attention_softmax_in_fp32
- Cache the attention mask value to avoid recreating it every time.
- Use jit to fuse the attention fp32 casting, masking, softmax, and scaling.
- Combine the attention and causal masks into a single one, pre-computed for the whole model instead of every layer.
- Merge the key and value caches into one (this changes the format of layer_past/ present, does it risk creating problems?)
- Use the memory layout (self.num_heads, 3, self.head_dim) instead of (3, self.num_heads, self.head_dim) for the QKV tensor with MHA. (prevents an overhead with the merged key and values, but makes the checkpoints incompatible with the original gpt2 model).
You can read more about the optimizations in the original pull request
GPTBigCodeConfig
[[autodoc]] GPTBigCodeConfig
GPTBigCodeModel
[[autodoc]] GPTBigCodeModel
    - forward
GPTBigCodeForCausalLM
[[autodoc]] GPTBigCodeForCausalLM
    - forward
GPTBigCodeForSequenceClassification
[[autodoc]] GPTBigCodeForSequenceClassification
    - forward
GPTBigCodeForTokenClassification
[[autodoc]] GPTBigCodeForTokenClassification
    - forward"
"
LayoutLM

Overview
The LayoutLM model was proposed in the paper LayoutLM: Pre-training of Text and Layout for Document Image
Understanding by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
information extraction tasks, such as form understanding and receipt understanding. It obtains state-of-the-art results
on several downstream tasks:

form understanding: the FUNSD dataset (a collection of 199 annotated
  forms comprising more than 30,000 words).
receipt understanding: the SROIE dataset (a collection of 626 receipts for
  training and 347 receipts for testing).
document image classification: the RVL-CDIP dataset (a collection of
  400,000 images belonging to one of 16 classes).

The abstract from the paper is the following:
Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the
widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation,
while neglecting layout and style information that is vital for document image understanding. In this paper, we propose
the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is
beneficial for a great number of real-world document image understanding tasks such as information extraction from
scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM.
To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for
document-level pretraining. It achieves new state-of-the-art results in several downstream tasks, including form
understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification
(from 93.07 to 94.42).
Tips:

In addition to input_ids, [~transformers.LayoutLMModel.forward] also expects the input bbox, which are
  the bounding boxes (i.e. 2D-positions) of the input tokens. These can be obtained using an external OCR engine such
  as Google's Tesseract (there's a Python wrapper available). Each bounding box should be in (x0, y0, x1, y1) format, where
  (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the
  position of the lower right corner. Note that one first needs to normalize the bounding boxes to be on a 0-1000
  scale. To normalize, you can use the following function:

python
def normalize_bbox(bbox, width, height):
    return [
        int(1000 * (bbox[0] / width)),
        int(1000 * (bbox[1] / height)),
        int(1000 * (bbox[2] / width)),
        int(1000 * (bbox[3] / height)),
    ]
Here, width and height correspond to the width and height of the original document in which the token
occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:
thon
from PIL import Image
Document can be a png, jpg, etc. PDFs must be converted to images.
image = Image.open(name_of_your_document).convert(""RGB"")
width, height = image.size

Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLM. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog post on fine-tuning
  LayoutLM for document-understanding using Keras & Hugging Face
  Transformers.

A blog post on how to fine-tune LayoutLM for document-understanding using only Hugging Face Transformers.

A notebook on how to fine-tune LayoutLM on the FUNSD dataset with image embeddings.

See also: Document question answering task guide

A notebook on how to fine-tune LayoutLM for sequence classification on the RVL-CDIP dataset.
Text classification task guide

A notebook on how to  fine-tune LayoutLM for token classification on the FUNSD dataset.
Token classification task guide

Other resources
- Masked language modeling task guide
🚀 Deploy

A blog post on how to Deploy LayoutLM with Hugging Face Inference Endpoints.

LayoutLMConfig
[[autodoc]] LayoutLMConfig
LayoutLMTokenizer
[[autodoc]] LayoutLMTokenizer
LayoutLMTokenizerFast
[[autodoc]] LayoutLMTokenizerFast
LayoutLMModel
[[autodoc]] LayoutLMModel
LayoutLMForMaskedLM
[[autodoc]] LayoutLMForMaskedLM
LayoutLMForSequenceClassification
[[autodoc]] LayoutLMForSequenceClassification
LayoutLMForTokenClassification
[[autodoc]] LayoutLMForTokenClassification
LayoutLMForQuestionAnswering
[[autodoc]] LayoutLMForQuestionAnswering
TFLayoutLMModel
[[autodoc]] TFLayoutLMModel
TFLayoutLMForMaskedLM
[[autodoc]] TFLayoutLMForMaskedLM
TFLayoutLMForSequenceClassification
[[autodoc]] TFLayoutLMForSequenceClassification
TFLayoutLMForTokenClassification
[[autodoc]] TFLayoutLMForTokenClassification
TFLayoutLMForQuestionAnswering
[[autodoc]] TFLayoutLMForQuestionAnswering"
"
BigBird
Overview
The BigBird model was proposed in Big Bird: Transformers for Longer Sequences by
Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
has been shown that applying sparse, global, and random attention approximates full attention, while being
computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
BigBird has shown improved performance on various long document NLP tasks, such as question answering and
summarization, compared to BERT or RoBERTa.
The abstract from the paper is the following:
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
propose novel applications to genomics data.
Tips:

For an in-detail explanation on how BigBird's attention works, see this blog post.
BigBird comes with 2 implementations: original_full & block_sparse. For the sequence length < 1024, using
  original_full is advised as there is no benefit in using block_sparse attention.
The code currently uses window size of 3 blocks and 2 global blocks.
Sequence length must be divisible by block size.
Current implementation supports only ITC.
Current implementation doesn't support num_random_blocks = 0
BigBird is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.

This model was contributed by vasudevgupta. The original code can be found
here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

BigBirdConfig
[[autodoc]] BigBirdConfig
BigBirdTokenizer
[[autodoc]] BigBirdTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
BigBirdTokenizerFast
[[autodoc]] BigBirdTokenizerFast
BigBird specific outputs
[[autodoc]] models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput
BigBirdModel
[[autodoc]] BigBirdModel
    - forward
BigBirdForPreTraining
[[autodoc]] BigBirdForPreTraining
    - forward
BigBirdForCausalLM
[[autodoc]] BigBirdForCausalLM
    - forward
BigBirdForMaskedLM
[[autodoc]] BigBirdForMaskedLM
    - forward
BigBirdForSequenceClassification
[[autodoc]] BigBirdForSequenceClassification
    - forward
BigBirdForMultipleChoice
[[autodoc]] BigBirdForMultipleChoice
    - forward
BigBirdForTokenClassification
[[autodoc]] BigBirdForTokenClassification
    - forward
BigBirdForQuestionAnswering
[[autodoc]] BigBirdForQuestionAnswering
    - forward
FlaxBigBirdModel
[[autodoc]] FlaxBigBirdModel
    - call
FlaxBigBirdForPreTraining
[[autodoc]] FlaxBigBirdForPreTraining
    - call
FlaxBigBirdForCausalLM
[[autodoc]] FlaxBigBirdForCausalLM
    - call
FlaxBigBirdForMaskedLM
[[autodoc]] FlaxBigBirdForMaskedLM
    - call
FlaxBigBirdForSequenceClassification
[[autodoc]] FlaxBigBirdForSequenceClassification
    - call
FlaxBigBirdForMultipleChoice
[[autodoc]] FlaxBigBirdForMultipleChoice
    - call
FlaxBigBirdForTokenClassification
[[autodoc]] FlaxBigBirdForTokenClassification
    - call
FlaxBigBirdForQuestionAnswering
[[autodoc]] FlaxBigBirdForQuestionAnswering
    - call"
"
Table Transformer
Overview
The Table Transformer model was proposed in PubTables-1M: Towards comprehensive table extraction from unstructured documents by
Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured documents,
as well as table structure recognition and functional analysis. The authors train 2 DETR models, one for table detection and one for table structure recognition, dubbed Table Transformers.
The abstract from the paper is the following:
Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents.
However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more
comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input
modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant
source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a
significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based
object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any
special customization for these tasks.
Tips:

The authors released 2 models, one for table detection in documents, one for table structure recognition (the task of recognizing the individual rows, columns etc. in a table).
One can use the [AutoImageProcessor] API to prepare images and optional targets for the model. This will load a [DetrImageProcessor] behind the scenes.

 Table detection and table structure recognition clarified. Taken from the original paper. 
This model was contributed by nielsr. The original code can be
found here.
Resources

A demo notebook for the Table Transformer can be found here.
It turns out padding of images is quite important for detection. An interesting Github thread with replies from the authors can be found here.

TableTransformerConfig
[[autodoc]] TableTransformerConfig
TableTransformerModel
[[autodoc]] TableTransformerModel
    - forward
TableTransformerForObjectDetection
[[autodoc]] TableTransformerForObjectDetection
    - forward"
"
XLSR-Wav2Vec2
Overview
The XLSR-Wav2Vec2 model was proposed in Unsupervised Cross-Lingual Representation Learning For Speech Recognition by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael
Auli.
The abstract from the paper is the following:
This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw
waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over
masked latent speech representations and jointly learns a quantization of the latents shared across languages. The
resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly
outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction
of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to
a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong
individual models. Analysis shows that the latent discrete speech representations are shared across languages with
increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing
XLSR-53, a large model pretrained in 53 languages.
Tips:

XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be
  decoded using [Wav2Vec2CTCTokenizer].

XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to Wav2Vec2's documentation page.
The original code can be found here."
"
CLIP
Overview
The CLIP model was proposed in Learning Transferable Visual Models From Natural Language Supervision by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh,
Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. CLIP
(Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be
instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing
for the task, similarly to the zero-shot capabilities of GPT-2 and 3.
The abstract from the paper is the following:
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This
restricted form of supervision limits their generality and usability since additional labeled data is needed to specify
any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a
much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes
with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400
million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference
learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study
the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks
such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The
model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need
for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot
without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained
model weights at this https URL.
Usage
CLIP is a multi-modal vision and language model. It can be used for image-text similarity and for zero-shot image
classification. CLIP uses a ViT like transformer to get visual features and a causal language model to get the text
features. Both the text and visual features are then projected to a latent space with identical dimension. The dot
product between the projected image and text features is then used as a similar score.
To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image. The authors
also add absolute position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder.
The [CLIPImageProcessor] can be used to resize (or rescale) and normalize images for the model.
The [CLIPTokenizer] is used to encode the text. The [CLIPProcessor] wraps
[CLIPImageProcessor] and [CLIPTokenizer] into a single instance to both
encode the text and prepare the images. The following example shows how to get the image-text similarity scores using
[CLIPProcessor] and [CLIPModel].
thon

from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained(""openai/clip-vit-base-patch32"")
processor = CLIPProcessor.from_pretrained(""openai/clip-vit-base-patch32"")
url = ""http://images.cocodataset.org/val2017/000000039769.jpg""
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=[""a photo of a cat"", ""a photo of a dog""], images=image, return_tensors=""pt"", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities

This model was contributed by valhalla. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIP.

A blog post on How to fine-tune CLIP on 10,000 image-text pairs.
CLIP is supported by this example script.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.
CLIPConfig
[[autodoc]] CLIPConfig
    - from_text_vision_configs
CLIPTextConfig
[[autodoc]] CLIPTextConfig
CLIPVisionConfig
[[autodoc]] CLIPVisionConfig
CLIPTokenizer
[[autodoc]] CLIPTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
CLIPTokenizerFast
[[autodoc]] CLIPTokenizerFast
CLIPImageProcessor
[[autodoc]] CLIPImageProcessor
    - preprocess
CLIPFeatureExtractor
[[autodoc]] CLIPFeatureExtractor
CLIPProcessor
[[autodoc]] CLIPProcessor
CLIPModel
[[autodoc]] CLIPModel
    - forward
    - get_text_features
    - get_image_features
CLIPTextModel
[[autodoc]] CLIPTextModel
    - forward
CLIPTextModelWithProjection
[[autodoc]] CLIPTextModelWithProjection
    - forward
CLIPVisionModelWithProjection
[[autodoc]] CLIPVisionModelWithProjection
    - forward
CLIPVisionModel
[[autodoc]] CLIPVisionModel
    - forward
TFCLIPModel
[[autodoc]] TFCLIPModel
    - call
    - get_text_features
    - get_image_features
TFCLIPTextModel
[[autodoc]] TFCLIPTextModel
    - call
TFCLIPVisionModel
[[autodoc]] TFCLIPVisionModel
    - call
FlaxCLIPModel
[[autodoc]] FlaxCLIPModel
    - call
    - get_text_features
    - get_image_features
FlaxCLIPTextModel
[[autodoc]] FlaxCLIPTextModel
    - call
FlaxCLIPVisionModel
[[autodoc]] FlaxCLIPVisionModel
    - call"
"
Vision Transformer (ViT)
Overview
The Vision Transformer (ViT) model was proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition
at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk
Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob
Uszkoreit, Neil Houlsby. It's the first paper that successfully trains a Transformer encoder on ImageNet, attaining
very good results compared to familiar convolutional architectures.
The abstract from the paper is the following:
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its
applications to computer vision remain limited. In vision, attention is either applied in conjunction with
convolutional networks, or used to replace certain components of convolutional networks while keeping their overall
structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to
sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of
data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.),
Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring
substantially fewer computational resources to train.
Tips:

Demo notebooks regarding inference as well as fine-tuning ViT on custom data can be found here.
To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
  which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image, which can be
  used for classification. The authors also add absolute position embeddings, and feed the resulting sequence of
  vectors to a standard Transformer encoder.
As the Vision Transformer expects each image to be of the same size (resolution), one can use
  [ViTImageProcessor] to resize (or rescale) and normalize images for the model.
Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of
  each checkpoint. For example, google/vit-base-patch16-224 refers to a base-sized architecture with patch
  resolution of 16x16 and fine-tuning resolution of 224x224. All checkpoints can be found on the hub.
The available checkpoints are either (1) pre-trained on ImageNet-21k (a collection of
  14 million images and 21k classes) only, or (2) also fine-tuned on ImageNet (also referred to as ILSVRC 2012, a collection of 1.3 million
  images and 1,000 classes).
The Vision Transformer was pre-trained using a resolution of 224x224. During fine-tuning, it is often beneficial to
  use a higher resolution than pre-training (Touvron et al., 2019), (Kolesnikov
  et al., 2020). In order to fine-tune at higher resolution, the authors perform
  2D interpolation of the pre-trained position embeddings, according to their location in the original image.
The best results are obtained with supervised pre-training, which is not the case in NLP. The authors also performed
  an experiment with a self-supervised pre-training objective, namely masked patched prediction (inspired by masked
  language modeling). With this approach, the smaller ViT-B/16 model achieves 79.9% accuracy on ImageNet, a significant
  improvement of 2% to training from scratch, but still 4% behind supervised pre-training.

 ViT architecture. Taken from the original paper. 
Following the original Vision Transformer, some follow-up works have been made:

DeiT (Data-efficient Image Transformers) by Facebook AI. DeiT models are distilled vision transformers.
  The authors of DeiT also released more efficiently trained ViT models, which you can directly plug into [ViTModel] or
  [ViTForImageClassification]. There are 4 variants available (in 3 different sizes): facebook/deit-tiny-patch16-224,
  facebook/deit-small-patch16-224, facebook/deit-base-patch16-224 and facebook/deit-base-patch16-384. Note that one should
  use [DeiTImageProcessor] in order to prepare images for the model.

BEiT (BERT pre-training of Image Transformers) by Microsoft Research. BEiT models outperform supervised pre-trained
  vision transformers using a self-supervised method inspired by BERT (masked image modeling) and based on a VQ-VAE.

DINO (a method for self-supervised training of Vision Transformers) by Facebook AI. Vision Transformers trained using
  the DINO method show very interesting properties not seen with convolutional models. They are capable of segmenting
  objects, without having ever been trained to do so. DINO checkpoints can be found on the hub.

MAE (Masked Autoencoders) by Facebook AI. By pre-training Vision Transformers to reconstruct pixel values for a high portion
  (75%) of masked patches (using an asymmetric encoder-decoder architecture), the authors show that this simple method outperforms
  supervised pre-training after fine-tuning.

This model was contributed by nielsr. The original code (written in JAX) can be
found here.
Note that we converted the weights from Ross Wightman's timm library, who already converted the weights from JAX to PyTorch. Credits
go to him!
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT.

[ViTForImageClassification] is supported by this example script and notebook.
A blog on fine-tuning [ViTForImageClassification] on a custom dataset can be found here.
More demo notebooks to fine-tune [ViTForImageClassification] can be found here.
Image classification task guide

Besides that:

[ViTForMaskedImageModeling] is supported by this example script.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ViTForImageClassification is supported by:

A blog post on how to Fine-Tune ViT for Image Classification with Hugging Face Transformers
A blog post on Image Classification with Hugging Face Transformers and Keras
A notebook on Fine-tuning for Image Classification with Hugging Face Transformers
A notebook on how to Fine-tune the Vision Transformer on CIFAR-10 with the Hugging Face Trainer
A notebook on how to Fine-tune the Vision Transformer on CIFAR-10 with PyTorch Lightning

⚗️ Optimization

A blog post on how to Accelerate Vision Transformer (ViT) with Quantization using Optimum

⚡️ Inference

A notebook on Quick demo: Vision Transformer (ViT) by Google Brain

🚀 Deploy

A blog post on Deploying Tensorflow Vision Models in Hugging Face with TF Serving
A blog post on Deploying Hugging Face ViT on Vertex AI
A blog post on Deploying Hugging Face ViT on Kubernetes with TF Serving

ViTConfig
[[autodoc]] ViTConfig
ViTFeatureExtractor
[[autodoc]] ViTFeatureExtractor
    - call
ViTImageProcessor
[[autodoc]] ViTImageProcessor
    - preprocess
ViTModel
[[autodoc]] ViTModel
    - forward
ViTForMaskedImageModeling
[[autodoc]] ViTForMaskedImageModeling
    - forward
ViTForImageClassification
[[autodoc]] ViTForImageClassification
    - forward
TFViTModel
[[autodoc]] TFViTModel
    - call
TFViTForImageClassification
[[autodoc]] TFViTForImageClassification
    - call
FlaxVitModel
[[autodoc]] FlaxViTModel
    - call
FlaxViTForImageClassification
[[autodoc]] FlaxViTForImageClassification
    - call"
"
OPT
Overview
The OPT model was proposed in Open Pre-trained Transformer Language Models by Meta AI.
OPT is a series of open-sourced large causal language models which perform similar in performance to GPT3.
The abstract from the paper is the following:
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models.
Tips:
- OPT has the same architecture as [BartDecoder].
- Contrary to GPT2, OPT adds the EOS token </s> to the beginning of every prompt.
This model was contributed by Arthur Zucker, Younes Belkada, and Patrick Von Platen.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with OPT. If you're
interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.

A notebook on fine-tuning OPT with PEFT, bitsandbytes, and Transformers. 🌎
A blog post on decoding strategies with OPT.
Causal language modeling chapter of the 🤗 Hugging Face Course.
[OPTForCausalLM] is supported by this causal language modeling example script and notebook.
[TFOPTForCausalLM] is supported by this causal language modeling example script and notebook.
[FlaxOPTForCausalLM] is supported by this causal language modeling example script.

Text classification task guide
[OPTForSequenceClassification] is supported by this example script and notebook.

[OPTForQuestionAnswering] is supported by this question answering example script and notebook.
Question answering chapter
  of the 🤗 Hugging Face Course.

⚡️ Inference

A blog post on How 🤗 Accelerate runs very large models thanks to PyTorch with OPT.

OPTConfig
[[autodoc]] OPTConfig
OPTModel
[[autodoc]] OPTModel
    - forward
OPTForCausalLM
[[autodoc]] OPTForCausalLM
    - forward
TFOPTModel
[[autodoc]] TFOPTModel
    - call
TFOPTForCausalLM
[[autodoc]] TFOPTForCausalLM
    - call
OPTForSequenceClassification
[[autodoc]] OPTForSequenceClassification
    - forward
OPTForQuestionAnswering
[[autodoc]] OPTForQuestionAnswering
    - forward
FlaxOPTModel
[[autodoc]] FlaxOPTModel
    - call
FlaxOPTForCausalLM
[[autodoc]] FlaxOPTForCausalLM
    - call"
"
Graphormer
Overview
The Graphormer model was proposed in Do Transformers Really Perform Bad for Graph Representation?  by 
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen and Tie-Yan Liu. It is a Graph Transformer model, modified to allow computations on graphs instead of text sequences by generating embeddings and features of interest during preprocessing and collation, then using a modified attention.
The abstract from the paper is the following:
The Transformer architecture has become a dominant choice in many domains, such as natural language processing and computer vision. Yet, it has not achieved competitive performance on popular leaderboards of graph-level prediction compared to mainstream GNN variants. Therefore, it remains a mystery how Transformers could perform well for graph representation learning. In this paper, we solve this mystery by presenting Graphormer, which is built upon the standard Transformer architecture, and could attain excellent results on a broad range of graph representation learning tasks, especially on the recent OGB Large-Scale Challenge. Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model. To this end, we propose several simple yet effective structural encoding methods to help Graphormer better model graph-structured data. Besides, we mathematically characterize the expressive power of Graphormer and exhibit that with our ways of encoding the structural information of graphs, many popular GNN variants could be covered as the special cases of Graphormer.
Tips:
This model will not work well on large graphs (more than 100 nodes/edges), as it will make the memory explode.
You can reduce the batch size, increase your RAM, or decrease the UNREACHABLE_NODE_DISTANCE parameter in algos_graphormer.pyx, but it will be hard to go above 700 nodes/edges.
This model does not use a tokenizer, but instead a special collator during training.
This model was contributed by clefourrier. The original code can be found here.
GraphormerConfig
[[autodoc]] GraphormerConfig
GraphormerModel
[[autodoc]] GraphormerModel
    - forward
GraphormerForGraphClassification
[[autodoc]] GraphormerForGraphClassification
    - forward"
"
ProphetNet

DISCLAIMER: If you see something strange, file a Github Issue and assign
@patrickvonplaten
Overview
The ProphetNet model was proposed in ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
Zhang, Ming Zhou on 13 Jan, 2020.
ProphetNet is an encoder-decoder model and can predict n-future tokens for ""ngram"" language modeling instead of just
the next token.
The abstract from the paper is the following:
In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.
Tips:

ProphetNet is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
The model architecture is based on the original Transformer, but replaces the “standard” self-attention mechanism in the decoder by a a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.

The Authors' code can be found here.
Documentation resources

Causal language modeling task guide
Translation task guide
Summarization task guide

ProphetNetConfig
[[autodoc]] ProphetNetConfig
ProphetNetTokenizer
[[autodoc]] ProphetNetTokenizer
ProphetNet specific outputs
[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput
[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput
[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput
[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput
ProphetNetModel
[[autodoc]] ProphetNetModel
    - forward
ProphetNetEncoder
[[autodoc]] ProphetNetEncoder
    - forward
ProphetNetDecoder
[[autodoc]] ProphetNetDecoder
    - forward
ProphetNetForConditionalGeneration
[[autodoc]] ProphetNetForConditionalGeneration
    - forward
ProphetNetForCausalLM
[[autodoc]] ProphetNetForCausalLM
    - forward"
"
Transformer XL

Overview
The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
Salakhutdinov. It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can
reuse previously computed hidden-states to attend to longer context (memory). This model also uses adaptive softmax
inputs and outputs (tied).
The abstract from the paper is the following:
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the
setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency
beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a
novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the
context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450%
longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+
times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of
bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn
Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably
coherent, novel text articles with thousands of tokens.
Tips:

Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right. The
  original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
Transformer-XL is one of the few models that has no sequence length limit.
Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular RNNs with two consecutive inputs). In this context, a segment is a number of consecutive tokens (for instance 512) that may span across multiple documents, and segments are fed in order to the model.
Basically, the hidden states of the previous segment are concatenated to the current input to compute the attention scores. This allows the model to pay attention to information that was in the previous segment as well as the current one. By stacking multiple attention layers, the receptive field can be increased to multiple previous segments.
This changes the positional embeddings to positional relative embeddings (as the regular positional embeddings would give the same results in the current input and the current hidden state at a given position) and needs to make some adjustments in the way attention scores are computed.

This model was contributed by thomwolf. The original code can be found here.

TransformerXL does not work with torch.nn.DataParallel due to a bug in PyTorch, see issue #36035

Documentation resources

Text classification task guide
Causal language modeling task guide

TransfoXLConfig
[[autodoc]] TransfoXLConfig
TransfoXLTokenizer
[[autodoc]] TransfoXLTokenizer
    - save_vocabulary
TransfoXL specific outputs
[[autodoc]] models.transfo_xl.modeling_transfo_xl.TransfoXLModelOutput
[[autodoc]] models.transfo_xl.modeling_transfo_xl.TransfoXLLMHeadModelOutput
[[autodoc]] models.transfo_xl.modeling_tf_transfo_xl.TFTransfoXLModelOutput
[[autodoc]] models.transfo_xl.modeling_tf_transfo_xl.TFTransfoXLLMHeadModelOutput
TransfoXLModel
[[autodoc]] TransfoXLModel
    - forward
TransfoXLLMHeadModel
[[autodoc]] TransfoXLLMHeadModel
    - forward
TransfoXLForSequenceClassification
[[autodoc]] TransfoXLForSequenceClassification
    - forward
TFTransfoXLModel
[[autodoc]] TFTransfoXLModel
    - call
TFTransfoXLLMHeadModel
[[autodoc]] TFTransfoXLLMHeadModel
    - call
TFTransfoXLForSequenceClassification
[[autodoc]] TFTransfoXLForSequenceClassification
    - call
Internal Layers
[[autodoc]] AdaptiveEmbedding
[[autodoc]] TFAdaptiveEmbedding"
"
Swin Transformer V2
Overview
The Swin Transformer V2 model was proposed in Swin Transformer V2: Scaling Up Capacity and Resolution by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
The abstract from the paper is the following:
Large-scale NLP models have been shown to significantly improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities like that of human beings. This paper aims to explore large-scale models in computer vision. We tackle three major issues in training and application of large vision models, including training instability, resolution gaps between pre-training and fine-tuning, and hunger on labelled data. Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. Through these techniques, this paper successfully trained a 3 billion-parameter Swin Transformer V2 model, which is the largest dense vision model to date, and makes it capable of training with images of up to 1,536×1,536 resolution. It set new performance records on 4 representative vision tasks, including ImageNet-V2 image classification, COCO object detection, ADE20K semantic segmentation, and Kinetics-400 video action classification. Also note our training is much more efficient than that in Google's billion-level visual models, which consumes 40 times less labelled data and 40 times less training time.
Tips:
- One can use the [AutoImageProcessor] API to prepare images for the model.
This model was contributed by nandwalritik.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Swin Transformer v2.

[Swinv2ForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

Besides that:

[Swinv2ForMaskedImageModeling] is supported by this example script.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Swinv2Config
[[autodoc]] Swinv2Config
Swinv2Model
[[autodoc]] Swinv2Model
    - forward
Swinv2ForMaskedImageModeling
[[autodoc]] Swinv2ForMaskedImageModeling
    - forward
Swinv2ForImageClassification
[[autodoc]] transformers.Swinv2ForImageClassification
    - forward"
"
LLaMA
Overview
The LLaMA model was proposed in LLaMA: Open and Efficient Foundation Language Models by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample. It is a collection of foundation language models ranging from 7B to 65B parameters.
The abstract from the paper is the following:
*We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community. *
Tips:

Weights for the LLaMA models can be obtained from by filling out this form
After downloading the weights, they will need to be converted to the Hugging Face Transformers format using the conversion script. The script can be called with the following (example) command:

python src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path

After conversion, the model and tokenizer can be loaded via:

thon
from transformers import LlamaForCausalLM, LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained(""/output/path"")
model = LlamaForCausalLM.from_pretrained(""/output/path"")

Note that executing the script requires enough CPU RAM to host the whole model in float16 precision (even if the biggest versions
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM). For the 65B model, it's thus 130GB of RAM needed.

The LLaMA tokenizer is a BPE model based on sentencepiece. One quirk of sentencepiece is that when decoding a sequence, if the first token is the start of the word (e.g. ""Banana""), the tokenizer does not prepend the prefix space to the string.

This model was contributed by zphang with contributions from BlackSamorez. The code of the implementation in Hugging Face is based on GPT-NeoX here. The original code of the authors can be found here.
Based on the original LLaMA model, Meta AI has released some follow-up works:

Llama2: Llama2 is an improved version of Llama with some architectural tweaks (Grouped Query Attention), and is pre-trained on 2Trillion tokens. Refer to the documentation of Llama2 which can be found here.

LlamaConfig
[[autodoc]] LlamaConfig
LlamaTokenizer
[[autodoc]] LlamaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
LlamaTokenizerFast
[[autodoc]] LlamaTokenizerFast
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - update_post_processor
    - save_vocabulary
LlamaModel
[[autodoc]] LlamaModel
    - forward
LlamaForCausalLM
[[autodoc]] LlamaForCausalLM
    - forward
LlamaForSequenceClassification
[[autodoc]] LlamaForSequenceClassification
    - forward"
"
Vision Encoder Decoder Models
Overview
The [VisionEncoderDecoderModel] can be used to initialize an image-to-text model with any
pretrained Transformer-based vision model as the encoder (e.g. ViT, BEiT, DeiT, Swin)
and any pretrained language model as the decoder (e.g. RoBERTa, GPT2, BERT, DistilBERT).
The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for
example) TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
Zhoujun Li, Furu Wei.
After such a [VisionEncoderDecoderModel] has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples below
for more information).
An example application is image captioning, in which the encoder is used to encode the image, after which an autoregressive language model generates
the caption. Another example is optical character recognition. Refer to TrOCR, which is an instance of [VisionEncoderDecoderModel].
Randomly initializing VisionEncoderDecoderModel from model configurations.
[VisionEncoderDecoderModel] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [ViTModel] configuration for the encoder
and the default [BertForCausalLM] configuration for the decoder.
thon

from transformers import BertConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel
config_encoder = ViTConfig()
config_decoder = BertConfig()
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
model = VisionEncoderDecoderModel(config=config)

Initialising VisionEncoderDecoderModel from a pretrained encoder and a pretrained decoder.
[VisionEncoderDecoderModel] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained Transformer-based vision model, e.g. Swin, can serve as the encoder and both pretrained auto-encoding models, e.g. BERT, pretrained causal language models, e.g. GPT2, as well as the pretrained decoder part of sequence-to-sequence models, e.g. decoder of BART, can be used as the decoder.
Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized.
Initializing [VisionEncoderDecoderModel] from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in the Warm-starting-encoder-decoder blog post.
To do so, the VisionEncoderDecoderModel class provides a [VisionEncoderDecoderModel.from_encoder_decoder_pretrained] method.
thon

from transformers import VisionEncoderDecoderModel
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
     ""microsoft/swin-base-patch4-window7-224-in22k"", ""bert-base-uncased""
 )

Loading an existing VisionEncoderDecoderModel checkpoint and perform inference.
To load fine-tuned checkpoints of the VisionEncoderDecoderModel class, [VisionEncoderDecoderModel] provides the from_pretrained() method just like any other model architecture in Transformers.
To perform inference, one uses the [generate] method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling.
thon

import requests
from PIL import Image
from transformers import GPT2TokenizerFast, ViTImageProcessor, VisionEncoderDecoderModel
load a fine-tuned image captioning model and corresponding tokenizer and image processor
model = VisionEncoderDecoderModel.from_pretrained(""nlpconnect/vit-gpt2-image-captioning"")
tokenizer = GPT2TokenizerFast.from_pretrained(""nlpconnect/vit-gpt2-image-captioning"")
image_processor = ViTImageProcessor.from_pretrained(""nlpconnect/vit-gpt2-image-captioning"")
let's perform inference on an image
url = ""http://images.cocodataset.org/val2017/000000039769.jpg""
image = Image.open(requests.get(url, stream=True).raw)
pixel_values = image_processor(image, return_tensors=""pt"").pixel_values
autoregressively generate caption (uses greedy decoding by default)
generated_ids = model.generate(pixel_values)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
a cat laying on a blanket next to a cat laying on a bed

Loading a PyTorch checkpoint into TFVisionEncoderDecoderModel.
[TFVisionEncoderDecoderModel.from_pretrained] currently doesn't support initializing the model from a
PyTorch checkpoint. Passing from_pt=True to this method will throw an exception. If there are only PyTorch
checkpoints for a particular vision encoder-decoder model, a workaround is:
thon

from transformers import VisionEncoderDecoderModel, TFVisionEncoderDecoderModel
_model = VisionEncoderDecoderModel.from_pretrained(""nlpconnect/vit-gpt2-image-captioning"")
_model.encoder.save_pretrained(""./encoder"")
_model.decoder.save_pretrained(""./decoder"")
model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
     ""./encoder"", ""./decoder"", encoder_from_pt=True, decoder_from_pt=True
 )
This is only for copying some specific attributes of this particular model.
model.config = _model.config

Training
Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model on a dataset of (image, text) pairs.
As you can see, only 2 inputs are required for the model in order to compute a loss: pixel_values (which are the
images) and labels (which are the input_ids of the encoded target sequence).
thon

from transformers import ViTImageProcessor, BertTokenizer, VisionEncoderDecoderModel
from datasets import load_dataset
image_processor = ViTImageProcessor.from_pretrained(""google/vit-base-patch16-224-in21k"")
tokenizer = BertTokenizer.from_pretrained(""bert-base-uncased"")
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
     ""google/vit-base-patch16-224-in21k"", ""bert-base-uncased""
 )
model.config.decoder_start_token_id = tokenizer.cls_token_id
model.config.pad_token_id = tokenizer.pad_token_id
dataset = load_dataset(""huggingface/cats-image"")
image = dataset[""test""][""image""][0]
pixel_values = image_processor(image, return_tensors=""pt"").pixel_values
labels = tokenizer(
     ""an image of two cats chilling on a couch"",
     return_tensors=""pt"",
 ).input_ids
the forward function automatically creates the correct decoder_input_ids
loss = model(pixel_values=pixel_values, labels=labels).loss

This model was contributed by nielsr. This model's TensorFlow and Flax versions
were contributed by ydshieh.
VisionEncoderDecoderConfig
[[autodoc]] VisionEncoderDecoderConfig
VisionEncoderDecoderModel
[[autodoc]] VisionEncoderDecoderModel
    - forward
    - from_encoder_decoder_pretrained
TFVisionEncoderDecoderModel
[[autodoc]] TFVisionEncoderDecoderModel
    - call
    - from_encoder_decoder_pretrained
FlaxVisionEncoderDecoderModel
[[autodoc]] FlaxVisionEncoderDecoderModel
    - call
    - from_encoder_decoder_pretrained"
"
Data2Vec
Overview
The Data2Vec model was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
Data2Vec proposes a unified framework for self-supervised learning across different data modalities - text, audio and images.
Importantly, predicted targets for pre-training are contextualized latent representations of the inputs, rather than modality-specific, context-independent targets.
The abstract from the paper is the following:
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and
objectives differ widely because they were developed with a single modality in mind. To get us closer to general
self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech,
NLP or computer vision. The core idea is to predict latent representations of the full input data based on a
masked view of the input in a selfdistillation setup using a standard Transformer architecture.
Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which
are local in nature, data2vec predicts contextualized latent representations that contain information from
the entire input. Experiments on the major benchmarks of speech recognition, image classification, and
natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
Models and code are available at www.github.com/pytorch/fairseq/tree/master/examples/data2vec.
Tips:

Data2VecAudio, Data2VecText, and Data2VecVision have all been trained using the same self-supervised learning method.
For Data2VecAudio, preprocessing is identical to [Wav2Vec2Model], including feature extraction
For Data2VecText, preprocessing is identical to [RobertaModel], including tokenization.
For Data2VecVision, preprocessing is identical to [BeitModel], including feature extraction.

This model was contributed by edugp and patrickvonplaten.
sayakpaul and Rocketknight1 contributed Data2Vec for vision in TensorFlow.
The original code (for NLP and Speech) can be found here.
The original code for vision can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Data2Vec.

[Data2VecVisionForImageClassification] is supported by this example script and notebook.
To fine-tune [TFData2VecVisionForImageClassification] on a custom dataset, see this notebook.

Data2VecText documentation resources
- Text classification task guide
- Token classification task guide
- Question answering task guide
- Causal language modeling task guide
- Masked language modeling task guide
- Multiple choice task guide
Data2VecAudio documentation resources
- Audio classification task guide
- Automatic speech recognition task guide
Data2VecVision documentation resources
- Image classification
- Semantic segmentation
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Data2VecTextConfig
[[autodoc]] Data2VecTextConfig
Data2VecAudioConfig
[[autodoc]] Data2VecAudioConfig
Data2VecVisionConfig
[[autodoc]] Data2VecVisionConfig
Data2VecAudioModel
[[autodoc]] Data2VecAudioModel
    - forward
Data2VecAudioForAudioFrameClassification
[[autodoc]] Data2VecAudioForAudioFrameClassification
    - forward
Data2VecAudioForCTC
[[autodoc]] Data2VecAudioForCTC
    - forward
Data2VecAudioForSequenceClassification
[[autodoc]] Data2VecAudioForSequenceClassification
    - forward
Data2VecAudioForXVector
[[autodoc]] Data2VecAudioForXVector
    - forward
Data2VecTextModel
[[autodoc]] Data2VecTextModel
    - forward
Data2VecTextForCausalLM
[[autodoc]] Data2VecTextForCausalLM
    - forward
Data2VecTextForMaskedLM
[[autodoc]] Data2VecTextForMaskedLM
    - forward
Data2VecTextForSequenceClassification
[[autodoc]] Data2VecTextForSequenceClassification
    - forward
Data2VecTextForMultipleChoice
[[autodoc]] Data2VecTextForMultipleChoice
    - forward
Data2VecTextForTokenClassification
[[autodoc]] Data2VecTextForTokenClassification
    - forward
Data2VecTextForQuestionAnswering
[[autodoc]] Data2VecTextForQuestionAnswering
    - forward
Data2VecVisionModel
[[autodoc]] Data2VecVisionModel
    - forward
Data2VecVisionForImageClassification
[[autodoc]] Data2VecVisionForImageClassification
    - forward
Data2VecVisionForSemanticSegmentation
[[autodoc]] Data2VecVisionForSemanticSegmentation
    - forward
TFData2VecVisionModel
[[autodoc]] TFData2VecVisionModel
    - call
TFData2VecVisionForImageClassification
[[autodoc]] TFData2VecVisionForImageClassification
    - call
TFData2VecVisionForSemanticSegmentation
[[autodoc]] TFData2VecVisionForSemanticSegmentation
    - call"
"
ConvBERT

Overview
The ConvBERT model was proposed in ConvBERT: Improving BERT with Span-based Dynamic Convolution by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng
Yan.
The abstract from the paper is the following:
Pre-trained language models like BERT and its variants have recently achieved impressive performance in various
natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers
large memory footprint and computation cost. Although all its attention heads query on the whole input sequence for
generating the attention map from a global perspective, we observe some heads only need to learn local dependencies,
which means the existence of computation redundancy. We therefore propose a novel span-based dynamic convolution to
replace these self-attention heads to directly model local dependencies. The novel convolution heads, together with the
rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context
learning. We equip BERT with this mixed attention design and build a ConvBERT model. Experiments have shown that
ConvBERT significantly outperforms BERT and its variants in various downstream tasks, with lower training cost and
fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while
using less than 1/4 training cost. Code and pre-trained models will be released.
ConvBERT training tips are similar to those of BERT.
This model was contributed by abhishek. The original implementation can be found
here: https://github.com/yitu-opensource/ConvBert
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

ConvBertConfig
[[autodoc]] ConvBertConfig
ConvBertTokenizer
[[autodoc]] ConvBertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
ConvBertTokenizerFast
[[autodoc]] ConvBertTokenizerFast
ConvBertModel
[[autodoc]] ConvBertModel
    - forward
ConvBertForMaskedLM
[[autodoc]] ConvBertForMaskedLM
    - forward
ConvBertForSequenceClassification
[[autodoc]] ConvBertForSequenceClassification
    - forward
ConvBertForMultipleChoice
[[autodoc]] ConvBertForMultipleChoice
    - forward
ConvBertForTokenClassification
[[autodoc]] ConvBertForTokenClassification
    - forward
ConvBertForQuestionAnswering
[[autodoc]] ConvBertForQuestionAnswering
    - forward
TFConvBertModel
[[autodoc]] TFConvBertModel
    - call
TFConvBertForMaskedLM
[[autodoc]] TFConvBertForMaskedLM
    - call
TFConvBertForSequenceClassification
[[autodoc]] TFConvBertForSequenceClassification
    - call
TFConvBertForMultipleChoice
[[autodoc]] TFConvBertForMultipleChoice
    - call
TFConvBertForTokenClassification
[[autodoc]] TFConvBertForTokenClassification
    - call
TFConvBertForQuestionAnswering
[[autodoc]] TFConvBertForQuestionAnswering
    - call"
"
Donut
Overview
The Donut model was proposed in OCR-free Document Understanding Transformer by
Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
Donut consists of an image Transformer encoder and an autoregressive text Transformer decoder to perform document understanding
tasks such as document image classification, form understanding and visual question answering.
The abstract from the paper is the following:
Understanding document images (e.g., invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document. Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs. Although such OCR-based approaches have shown promising performance, they suffer from 1) high computational costs for using OCR; 2) inflexibility of OCR models on languages or types of document; 3) OCR error propagation to the subsequent process. To address these issues, in this paper, we introduce a novel OCR-free VDU model named Donut, which stands for Document understanding transformer. As the first step in OCR-free VDU research, we propose a simple architecture (i.e., Transformer) with a pre-training objective (i.e., cross-entropy loss). Donut is conceptually simple yet effective. Through extensive experiments and analyses, we show a simple OCR-free VDU model, Donut, achieves state-of-the-art performances on various VDU tasks in terms of both speed and accuracy. In addition, we offer a synthetic data generator that helps the model pre-training to be flexible in various languages and domains.

 Donut high-level overview. Taken from the original paper. 
This model was contributed by nielsr. The original code can be found
here.
Tips:

The quickest way to get started with Donut is by checking the tutorial
  notebooks, which show how to use the model
  at inference time as well as fine-tuning on custom data.
Donut is always used within the VisionEncoderDecoder framework.

Inference
Donut's [VisionEncoderDecoder] model accepts images as input and makes use of
[~generation.GenerationMixin.generate] to autoregressively generate text given the input image.
The [DonutImageProcessor] class is responsible for preprocessing the input image and
[XLMRobertaTokenizer/XLMRobertaTokenizerFast] decodes the generated target tokens to the target string. The
[DonutProcessor] wraps [DonutImageProcessor] and [XLMRobertaTokenizer/XLMRobertaTokenizerFast]
into a single instance to both extract the input features and decode the predicted token ids.

Step-by-step Document Image Classification

import re
from transformers import DonutProcessor, VisionEncoderDecoderModel
from datasets import load_dataset
import torch
processor = DonutProcessor.from_pretrained(""naver-clova-ix/donut-base-finetuned-rvlcdip"")
model = VisionEncoderDecoderModel.from_pretrained(""naver-clova-ix/donut-base-finetuned-rvlcdip"")
device = ""cuda"" if torch.cuda.is_available() else ""cpu""
model.to(device)  # doctest: +IGNORE_RESULT
load document image
dataset = load_dataset(""hf-internal-testing/example-documents"", split=""test"")
image = dataset[1][""image""]
prepare decoder inputs
task_prompt = """"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors=""pt"").input_ids
pixel_values = processor(image, return_tensors=""pt"").pixel_values
outputs = model.generate(
     pixel_values.to(device),
     decoder_input_ids=decoder_input_ids.to(device),
     max_length=model.decoder.config.max_position_embeddings,
     early_stopping=True,
     pad_token_id=processor.tokenizer.pad_token_id,
     eos_token_id=processor.tokenizer.eos_token_id,
     use_cache=True,
     num_beams=1,
     bad_words_ids=[[processor.tokenizer.unk_token_id]],
     return_dict_in_generate=True,
 )
sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, """").replace(processor.tokenizer.pad_token, """")
sequence = re.sub(r""<.*?>"", """", sequence, count=1).strip()  # remove first task start token
print(processor.token2json(sequence))
{'class': 'advertisement'}

Step-by-step Document Parsing

import re
from transformers import DonutProcessor, VisionEncoderDecoderModel
from datasets import load_dataset
import torch
processor = DonutProcessor.from_pretrained(""naver-clova-ix/donut-base-finetuned-cord-v2"")
model = VisionEncoderDecoderModel.from_pretrained(""naver-clova-ix/donut-base-finetuned-cord-v2"")
device = ""cuda"" if torch.cuda.is_available() else ""cpu""
model.to(device)  # doctest: +IGNORE_RESULT
load document image
dataset = load_dataset(""hf-internal-testing/example-documents"", split=""test"")
image = dataset[2][""image""]
prepare decoder inputs
task_prompt = """"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors=""pt"").input_ids
pixel_values = processor(image, return_tensors=""pt"").pixel_values
outputs = model.generate(
     pixel_values.to(device),
     decoder_input_ids=decoder_input_ids.to(device),
     max_length=model.decoder.config.max_position_embeddings,
     early_stopping=True,
     pad_token_id=processor.tokenizer.pad_token_id,
     eos_token_id=processor.tokenizer.eos_token_id,
     use_cache=True,
     num_beams=1,
     bad_words_ids=[[processor.tokenizer.unk_token_id]],
     return_dict_in_generate=True,
 )
sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, """").replace(processor.tokenizer.pad_token, """")
sequence = re.sub(r""<.*?>"", """", sequence, count=1).strip()  # remove first task start token
print(processor.token2json(sequence))
{'menu': {'nm': 'CINNAMON SUGAR', 'unitprice': '17,000', 'cnt': '1 x', 'price': '17,000'}, 'sub_total': {'subtotal_price': '17,000'}, 'total': {'total_price': '17,000', 'cashprice': '20,000', 'changeprice': '3,000'}}

Step-by-step Document Visual Question Answering (DocVQA)

import re
from transformers import DonutProcessor, VisionEncoderDecoderModel
from datasets import load_dataset
import torch
processor = DonutProcessor.from_pretrained(""naver-clova-ix/donut-base-finetuned-docvqa"")
model = VisionEncoderDecoderModel.from_pretrained(""naver-clova-ix/donut-base-finetuned-docvqa"")
device = ""cuda"" if torch.cuda.is_available() else ""cpu""
model.to(device)  # doctest: +IGNORE_RESULT
load document image from the DocVQA dataset
dataset = load_dataset(""hf-internal-testing/example-documents"", split=""test"")
image = dataset[0][""image""]
prepare decoder inputs
task_prompt = ""{user_input}""
question = ""When is the coffee break?""
prompt = task_prompt.replace(""{user_input}"", question)
decoder_input_ids = processor.tokenizer(prompt, add_special_tokens=False, return_tensors=""pt"").input_ids
pixel_values = processor(image, return_tensors=""pt"").pixel_values
outputs = model.generate(
     pixel_values.to(device),
     decoder_input_ids=decoder_input_ids.to(device),
     max_length=model.decoder.config.max_position_embeddings,
     early_stopping=True,
     pad_token_id=processor.tokenizer.pad_token_id,
     eos_token_id=processor.tokenizer.eos_token_id,
     use_cache=True,
     num_beams=1,
     bad_words_ids=[[processor.tokenizer.unk_token_id]],
     return_dict_in_generate=True,
 )
sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, """").replace(processor.tokenizer.pad_token, """")
sequence = re.sub(r""<.*?>"", """", sequence, count=1).strip()  # remove first task start token
print(processor.token2json(sequence))
{'question': 'When is the coffee break?', 'answer': '11-14 to 11:39 a.m.'}

See the model hub to look for Donut checkpoints.
Training
We refer to the tutorial notebooks.
DonutSwinConfig
[[autodoc]] DonutSwinConfig
DonutImageProcessor
[[autodoc]] DonutImageProcessor
    - preprocess
DonutFeatureExtractor
[[autodoc]] DonutFeatureExtractor
    - call
DonutProcessor
[[autodoc]] DonutProcessor
    - call
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
DonutSwinModel
[[autodoc]] DonutSwinModel
    - forward"
"
Bark
Overview
Bark is a transformer-based text-to-speech model proposed by Suno AI in suno-ai/bark. 
Bark is made of 4 main models:

[BarkSemanticModel] (also referred to as the 'text' model): a causal auto-regressive transformer model that takes as input tokenized text, and predicts semantic text tokens that capture the meaning of the text.
[BarkCoarseModel] (also referred to as the 'coarse acoustics' model): a causal autoregressive transformer, that takes as input the results of the [BarkSemanticModel] model. It aims at predicting the first two audio codebooks necessary for EnCodec.
[BarkFineModel] (the 'fine acoustics' model), this time a non-causal autoencoder transformer, which iteratively predicts the last codebooks based on the sum of the previous codebooks embeddings.
having predicted all the codebook channels from the [EncodecModel], Bark uses it to decode the output audio array.

It should be noted that each of the first three modules can support conditional speaker embeddings to condition the output sound according to specific predefined voice.
Tips:
Suno offers a library of voice presets in a number of languages here.
These presets are also uploaded in the hub here or here.
thon

from transformers import AutoProcessor, BarkModel
processor = AutoProcessor.from_pretrained(""suno/bark"")
model = BarkModel.from_pretrained(""suno/bark"")
voice_preset = ""v2/en_speaker_6""
inputs = processor(""Hello, my dog is cute"", voice_preset=voice_preset)
audio_array = model.generate(**inputs)
audio_array = audio_array.cpu().numpy().squeeze()

Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. 
thon

Multilingual speech - simplified Chinese
inputs = processor(""惊人的！我会说中文"")
Multilingual speech - French - let's use a voice_preset as well
inputs = processor(""Incroyable! Je peux générer du son."", voice_preset=""fr_speaker_5"")
Bark can also generate music. You can help it out by adding music notes around your lyrics.
inputs = processor(""♪ Hello, my dog is cute ♪"")
audio_array = model.generate(**inputs)
audio_array = audio_array.cpu().numpy().squeeze()

The model can also produce nonverbal communications like laughing, sighing and crying.
thon

Adding non-speech cues to the input text
inputs = processor(""Hello uh  [clears throat], my dog is cute [laughter]"")
audio_array = model.generate(**inputs)
audio_array = audio_array.cpu().numpy().squeeze()

To save the audio, simply take the sample rate from the model config and some scipy utility:
thon

from scipy.io.wavfile import write as write_wav
save audio to disk, but first take the sample rate from the model config
sample_rate = model.generation_config.sample_rate
write_wav(""bark_generation.wav"", sample_rate, audio_array)

This model was contributed by Yoach Lacombe (ylacombe) and Sanchit Gandhi (sanchit-gandhi).
The original code can be found here.
BarkConfig
[[autodoc]] BarkConfig
    - all
BarkProcessor
[[autodoc]] BarkProcessor
    - all
    - call
BarkModel
[[autodoc]] BarkModel
    - generate
BarkSemanticModel
[[autodoc]] BarkSemanticModel
    - forward
BarkCoarseModel
[[autodoc]] BarkCoarseModel
    - forward
BarkFineModel
[[autodoc]] BarkFineModel
    - forward
BarkCausalModel
[[autodoc]] BarkCausalModel
    - forward
BarkCoarseConfig
[[autodoc]] BarkCoarseConfig
    - all
BarkFineConfig
[[autodoc]] BarkFineConfig
    - all
BarkSemanticConfig
[[autodoc]] BarkSemanticConfig
    - all"
"
SAM
Overview
SAM (Segment Anything Model) was proposed in Segment Anything by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
The model can be used to predict segmentation masks of any object of interest given an input image. 

The abstract from the paper is the following:
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision.
Tips:

The model predicts binary masks that states the presence or not of the object of interest given an image.
The model predicts much better results if input 2D points and/or input bounding boxes are provided
You can prompt multiple points for the same image, and predict a single mask. 
Fine-tuning the model is not supported yet
According to the paper, textual input should be also supported. However, at this time of writing this seems to be not supported according to the official repository. 

This model was contributed by ybelkada and ArthurZ.
The original code can be found here.
Below is an example on how to run mask generation given an image and a 2D point:
thon
import torch
from PIL import Image
import requests
from transformers import SamModel, SamProcessor
device = ""cuda"" if torch.cuda.is_available() else ""cpu""
model = SamModel.from_pretrained(""facebook/sam-vit-huge"").to(device)
processor = SamProcessor.from_pretrained(""facebook/sam-vit-huge"")
img_url = ""https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png""
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert(""RGB"")
input_points = [[[450, 600]]]  # 2D location of a window in the image
inputs = processor(raw_image, input_points=input_points, return_tensors=""pt"").to(device)
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(
    outputs.pred_masks.cpu(), inputs[""original_sizes""].cpu(), inputs[""reshaped_input_sizes""].cpu()
)
scores = outputs.iou_scores

Resources:

Demo notebook for using the model.
Demo notebook for using the automatic mask generation pipeline.
Demo notebook for inference with MedSAM, a fine-tuned version of SAM on the medical domain.
Demo notebook for fine-tuning the model on custom data.

SamConfig
[[autodoc]] SamConfig
SamVisionConfig
[[autodoc]] SamVisionConfig
SamMaskDecoderConfig
[[autodoc]] SamMaskDecoderConfig
SamPromptEncoderConfig
[[autodoc]] SamPromptEncoderConfig
SamProcessor
[[autodoc]] SamProcessor
SamImageProcessor
[[autodoc]] SamImageProcessor
SamModel
[[autodoc]] SamModel
    - forward
TFSamModel
[[autodoc]] TFSamModel
    - call"
"
EfficientFormer
Overview
The EfficientFormer model was proposed in EfficientFormer: Vision Transformers at MobileNet Speed 
by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.  EfficientFormer proposes a
dimension-consistent pure transformer that can be run on mobile devices for dense prediction tasks like image classification, object
detection and semantic segmentation.
The abstract from the paper is the following:
Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. 
However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally 
times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly 
challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation 
complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still 
unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance? 
To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs. 
Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm. 
Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer. 
Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices. 
Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on 
iPhone 12 (compiled with CoreML), which { runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1),} and our largest model, 
EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can 
reach extremely low latency on mobile devices while maintaining high performance.
This model was contributed by novice03 and Bearnardd.
The original code can be found here. The TensorFlow version of this model was added by D-Roberts.
Documentation resources

Image classification task guide

EfficientFormerConfig
[[autodoc]] EfficientFormerConfig
EfficientFormerImageProcessor
[[autodoc]] EfficientFormerImageProcessor
    - preprocess
EfficientFormerModel
[[autodoc]] EfficientFormerModel
    - forward
EfficientFormerForImageClassification
[[autodoc]] EfficientFormerForImageClassification
    - forward
EfficientFormerForImageClassificationWithTeacher
[[autodoc]] EfficientFormerForImageClassificationWithTeacher
    - forward
TFEfficientFormerModel
[[autodoc]] TFEfficientFormerModel
    - call
TFEfficientFormerForImageClassification
[[autodoc]] TFEfficientFormerForImageClassification
    - call
TFEfficientFormerForImageClassificationWithTeacher
[[autodoc]] TFEfficientFormerForImageClassificationWithTeacher
    - call"
"
RWKV
Overview
The RWKV model was proposed in this repo
It suggests a tweak in the traditional Transformer attention to make it linear. This way, the model can be used as recurrent network: passing inputs for timestamp 0 and timestamp 1 together is the same as passing inputs at timestamp 0, then inputs at timestamp 1 along with the state of timestamp 0 (see example below).
This can be more efficient than a regular Transformer and can deal with sentence of any length (even if the model uses a fixed context length for training).
This model was contributed by sgugger.
The original code can be found here.
Example of use as an RNN:

import torch
from transformers import AutoTokenizer, RwkvConfig, RwkvModel
model = RwkvModel.from_pretrained(""sgugger/rwkv-430M-pile"")
tokenizer = AutoTokenizer.from_pretrained(""sgugger/rwkv-430M-pile"")
inputs = tokenizer(""This is an example."", return_tensors=""pt"")
Feed everything to the model
outputs = model(inputs[""input_ids""])
output_whole = outputs.last_hidden_state
outputs = model(inputs[""input_ids""][:, :2])
output_one = outputs.last_hidden_state
Using the state computed on the first inputs, we will get the same output
outputs = model(inputs[""input_ids""][:, 2:], state=outputs.state)
output_two = outputs.last_hidden_state
torch.allclose(torch.cat([output_one, output_two], dim=1), output_whole, atol=1e-5)

RwkvConfig
[[autodoc]] RwkvConfig
RwkvModel
[[autodoc]] RwkvModel
    - forward
RwkvLMHeadModel
[[autodoc]] RwkvForCausalLM
    - forward
Rwkv attention and the recurrent formulas
In a traditional auto-regressive Transformer, attention is written as
$$O = \hbox{softmax}(QK^{T} / \sqrt{d}) V$$
with \(Q\), \(K\) and \(V\) are matrices of shape seq_len x hidden_size named query, key and value (they are actually bigger matrices with a batch dimension and an attention head dimension but we're only interested in the last two, which is where the matrix product is taken, so for the sake of simplicity we only consider those two). The product \(QK^{T}\) then has shape seq_len x seq_len and we can take the maxtrix product with \(V\) to get the output \(O\) of the same shape as the others.  
Replacing the softmax by its value gives:
$$O_{i} = \frac{\sum_{j=1}^{i} e^{Q_{i} K_{j}^{T} / \sqrt{d}} V_{j}}{\sum_{j=1}^{i} e^{Q_{i} K_{j}^{T} / \sqrt{d}}}$$
Note that the entries in \(QK^{T}\) corresponding to \(j > i\) are masked (the sum stops at j) because the attention is not allowed to look at future tokens (only past ones).
In comparison, the RWKV attention is given by
$$O_{i} = \sigma(R_{i}) \frac{\sum_{j=1}^{i} e^{W_{i-j} + K_{j}} V_{j}}{\sum_{j=1}^{i} e^{W_{i-j} + K_{j}}}$$
where \(R\) is a new matrix called receptance by the author, \(K\) and \(V\) are still the key and value (\(\sigma\) here is the sigmoid function). \(W\) is a new vector that represents the position of the token and is given by
$$W_{0} = u \hbox{  and  } W_{k} = (k-1)w \hbox{ for } k \geq 1$$
with \(u\) and \(w\) learnable parameters called in the code time_first and time_decay respectively. The numerator and denominator can both be expressed recursively. Naming them \(N_{i}\) and \(D_{i}\) we have:
$$N_{i} = e^{u + K_{i}} V_{i} + \hat{N}{i} \hbox{  where  } \hat{N}{i} = e^{K_{i-1}} V_{i-1} + e^{w + K_{i-2}} V_{i-2} \cdots + e^{(i-2)w + K_{1}} V_{1}$$
so \(\hat{N}_{i}\) (called numerator_state in the code) satistfies
$$\hat{N}{0} = 0 \hbox{  and  } \hat{N}{j+1} = e^{K_{j}} V_{j} + e^{w} \hat{N}_{j}$$
and
$$D_{i} = e^{u + K_{i}} + \hat{D}{i} \hbox{  where  } \hat{D}{i} = e^{K_{i-1}} + e^{w + K_{i-2}} \cdots + e^{(i-2)w + K_{1}}$$
so \(\hat{D}_{i}\) (called denominator_state in the code) satistfies
$$\hat{D}{0} = 0 \hbox{  and  } \hat{D}{j+1} = e^{K_{j}} + e^{w} \hat{D}_{j}$$
The actual recurrent formula used are a tiny bit more complex, as for numerical stability we don't want to compute exponentials of big numbers. Usually the softmax is not computed as is, but the exponential of the maximum term is divided of the numerator and denominator:
$$\frac{e^{x_{i}}}{\sum_{j=1}^{n} e^{x_{j}}} = \frac{e^{x_{i} - M}}{\sum_{j=1}^{n} e^{x_{j} - M}}$$
with \(M\) the maximum of all \(x_{j}\). So here on top of saving the numerator state (\(\hat{N}\)) and the denominator state (\(\hat{D}\)) we also keep track of the maximum of all terms encountered in the exponentials. So we actually use
$$\tilde{N}{i} = e^{-M{i}} \hat{N}{i} \hbox{  and  } \tilde{D}{i} = e^{-M_{i}} \hat{D}_{i}$$
defined by the following recurrent formulas:
$$\tilde{N}{0} = 0 \hbox{  and  } \tilde{N}{j+1} = e^{K_{j} - q} V_{j} + e^{w + M_{j} - q} \tilde{N}{j} \hbox{  where  } q = \max(K{j}, w + M_{j})$$
and
$$\tilde{D}{0} = 0 \hbox{  and  } \tilde{D}{j+1} = e^{K_{j} - q} + e^{w + M_{j} - q} \tilde{D}{j} \hbox{  where  } q = \max(K{j}, w + M_{j})$$
and \(M_{j+1} = q\). With those, we can then compute
$$N_{i} = e^{u + K_{i} - q} V_{i} + e^{M_{i}} \tilde{N}{i} \hbox{  where  } q = \max(u + K{i}, M_{i})$$
and
$$D_{i} = e^{u + K_{i} - q} + e^{M_{i}} \tilde{D}{i} \hbox{  where  } q = \max(u + K{i}, M_{i})$$
which finally gives us
$$O_{i} = \sigma(R_{i}) \frac{N_{i}}{D_{i}}$$"
"
ALBERT

Overview
The ALBERT model was proposed in ALBERT: A Lite BERT for Self-supervised Learning of Language Representations by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,
Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training
speed of BERT:

Splitting the embedding matrix into two smaller matrices.
Using repeating layers split among groups.

The abstract from the paper is the following:
Increasing model size when pretraining natural language representations often results in improved performance on
downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations,
longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction
techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows
that our proposed methods lead to models that scale much better compared to the original BERT. We also use a
self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks
with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and
SQuAD benchmarks while having fewer parameters compared to BERT-large.
Tips:

ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
  than the left.
ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains
  similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
  number of (repeating) layers.
Embedding size E is different from hidden size H justified because the embeddings are context independent (one embedding vector represents one token), whereas hidden states are context dependent (one hidden state represents a sequence of tokens) so it's more logical to have H >> E. Also, the embedding matrix is large since it's V x E (V being the vocab size). If E < H, it has less parameters.
Layers are split in groups that share parameters (to save memory).
Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A and B (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have been swapped or not.

This model was contributed by lysandre. This model jax version was contributed by
kamalkraj. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

AlbertConfig
[[autodoc]] AlbertConfig
AlbertTokenizer
[[autodoc]] AlbertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
AlbertTokenizerFast
[[autodoc]] AlbertTokenizerFast
Albert specific outputs
[[autodoc]] models.albert.modeling_albert.AlbertForPreTrainingOutput
[[autodoc]] models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput
AlbertModel
[[autodoc]] AlbertModel
    - forward
AlbertForPreTraining
[[autodoc]] AlbertForPreTraining
    - forward
AlbertForMaskedLM
[[autodoc]] AlbertForMaskedLM
    - forward
AlbertForSequenceClassification
[[autodoc]] AlbertForSequenceClassification
    - forward
AlbertForMultipleChoice
[[autodoc]] AlbertForMultipleChoice
AlbertForTokenClassification
[[autodoc]] AlbertForTokenClassification
    - forward
AlbertForQuestionAnswering
[[autodoc]] AlbertForQuestionAnswering
    - forward
TFAlbertModel
[[autodoc]] TFAlbertModel
    - call
TFAlbertForPreTraining
[[autodoc]] TFAlbertForPreTraining
    - call
TFAlbertForMaskedLM
[[autodoc]] TFAlbertForMaskedLM
    - call
TFAlbertForSequenceClassification
[[autodoc]] TFAlbertForSequenceClassification
    - call
TFAlbertForMultipleChoice
[[autodoc]] TFAlbertForMultipleChoice
    - call
TFAlbertForTokenClassification
[[autodoc]] TFAlbertForTokenClassification
    - call
TFAlbertForQuestionAnswering
[[autodoc]] TFAlbertForQuestionAnswering
    - call
FlaxAlbertModel
[[autodoc]] FlaxAlbertModel
    - call
FlaxAlbertForPreTraining
[[autodoc]] FlaxAlbertForPreTraining
    - call
FlaxAlbertForMaskedLM
[[autodoc]] FlaxAlbertForMaskedLM
    - call
FlaxAlbertForSequenceClassification
[[autodoc]] FlaxAlbertForSequenceClassification
    - call
FlaxAlbertForMultipleChoice
[[autodoc]] FlaxAlbertForMultipleChoice
    - call
FlaxAlbertForTokenClassification
[[autodoc]] FlaxAlbertForTokenClassification
    - call
FlaxAlbertForQuestionAnswering
[[autodoc]] FlaxAlbertForQuestionAnswering
    - call"
"
EnCodec
Overview
The EnCodec neural codec model was proposed in High Fidelity Neural Audio Compression by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
The abstract from the paper is the following:
We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary that efficiently reduces artifacts and produce high-quality samples. We introduce a novel loss balancer mechanism to stabilize training: the weight of a loss now defines the fraction of the overall gradient it should represent, thus decoupling the choice of this hyper-parameter from the typical scale of the loss. Finally, we study how lightweight Transformer models can be used to further compress the obtained representation by up to 40%, while staying faster than real time. We provide a detailed description of the key design choices of the proposed model including: training objective, architectural changes and a study of various perceptual loss functions. We present an extensive subjective evaluation (MUSHRA tests) together with an ablation study for a range of bandwidths and audio domains, including speech, noisy-reverberant speech, and music. Our approach is superior to the baselines methods across all evaluated settings, considering both 24 kHz monophonic and 48 kHz stereophonic audio.
This model was contributed by Matthijs, Patrick Von Platen and Arthur Zucker. 
The original code can be found here.
Here is a quick example of how to encode and decode an audio using this model:
thon 

from datasets import load_dataset, Audio
from transformers import EncodecModel, AutoProcessor
librispeech_dummy = load_dataset(""hf-internal-testing/librispeech_asr_dummy"", ""clean"", split=""validation"")
model = EncodecModel.from_pretrained(""facebook/encodec_24khz"")
processor = AutoProcessor.from_pretrained(""facebook/encodec_24khz"")
librispeech_dummy = librispeech_dummy.cast_column(""audio"", Audio(sampling_rate=processor.sampling_rate))
audio_sample = librispeech_dummy[-1][""audio""][""array""]
inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors=""pt"")
encoder_outputs = model.encode(inputs[""input_values""], inputs[""padding_mask""])
audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, inputs[""padding_mask""])[0]
or the equivalent with a forward pass
audio_values = model(inputs[""input_values""], inputs[""padding_mask""]).audio_values

EncodecConfig
[[autodoc]] EncodecConfig
EncodecFeatureExtractor
[[autodoc]] EncodecFeatureExtractor
    - call
EncodecModel
[[autodoc]] EncodecModel
    - decode
    - encode
    - forward"
"
UniSpeech-SAT
Overview
The UniSpeech-SAT model was proposed in UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware
Pre-Training by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen,
Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu .
The abstract from the paper is the following:
Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled
data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in
speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In
this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are
introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to
the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function.
Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where
additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed
methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves
state-of-the-art performance in universal representation learning, especially for speaker identification oriented
tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training
dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.
Tips:

UniSpeechSat is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
  Please use [Wav2Vec2Processor] for the feature extraction.
UniSpeechSat model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
  decoded using [Wav2Vec2CTCTokenizer].
UniSpeechSat performs especially well on speaker verification, speaker identification, and speaker diarization tasks.

This model was contributed by patrickvonplaten. The Authors' code can be
found here.
Documentation resources

Audio classification task guide
Automatic speech recognition task guide

UniSpeechSatConfig
[[autodoc]] UniSpeechSatConfig
UniSpeechSat specific outputs
[[autodoc]] models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput
UniSpeechSatModel
[[autodoc]] UniSpeechSatModel
    - forward
UniSpeechSatForCTC
[[autodoc]] UniSpeechSatForCTC
    - forward
UniSpeechSatForSequenceClassification
[[autodoc]] UniSpeechSatForSequenceClassification
    - forward
UniSpeechSatForAudioFrameClassification
[[autodoc]] UniSpeechSatForAudioFrameClassification
    - forward
UniSpeechSatForXVector
[[autodoc]] UniSpeechSatForXVector
    - forward
UniSpeechSatForPreTraining
[[autodoc]] UniSpeechSatForPreTraining
    - forward"
"
UniSpeech
Overview
The UniSpeech model was proposed in UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael
Zeng, Xuedong Huang .
The abstract from the paper is the following:
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both
unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive
self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture
information more correlated with phonetic structures and improve the generalization across languages and domains. We
evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The
results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech
recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all
testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task,
i.e., a relative word error rate reduction of 6% against the previous approach.
Tips:

UniSpeech is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please
  use [Wav2Vec2Processor] for the feature extraction.
UniSpeech model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
  decoded using [Wav2Vec2CTCTokenizer].

This model was contributed by patrickvonplaten. The Authors' code can be
found here.
Documentation resources

Audio classification task guide
Automatic speech recognition task guide

UniSpeechConfig
[[autodoc]] UniSpeechConfig
UniSpeech specific outputs
[[autodoc]] models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
UniSpeechModel
[[autodoc]] UniSpeechModel
    - forward
UniSpeechForCTC
[[autodoc]] UniSpeechForCTC
    - forward
UniSpeechForSequenceClassification
[[autodoc]] UniSpeechForSequenceClassification
    - forward
UniSpeechForPreTraining
[[autodoc]] UniSpeechForPreTraining
    - forward"
"
BioGPT
Overview
The BioGPT model was proposed in BioGPT: generative pre-trained transformer for biomedical text generation and mining
 by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu. BioGPT is a domain-specific generative pre-trained Transformer language model for biomedical text generation and mining. BioGPT follows the Transformer language model backbone, and is pre-trained on 15M PubMed abstracts from scratch.
The abstract from the paper is the following:
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
Tips:

BioGPT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.
BioGPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence. Leveraging this feature allows BioGPT to generate syntactically coherent text as it can be observed in the run_generation.py example script.
The model can take the past_key_values (for PyTorch) as input, which is the previously computed key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the BioGptForCausalLM.forward() method for more information on its usage.

This model was contributed by kamalkraj. The original code can be found here.
Documentation resources

Causal language modeling task guide

BioGptConfig
[[autodoc]] BioGptConfig
BioGptTokenizer
[[autodoc]] BioGptTokenizer
    - save_vocabulary
BioGptModel
[[autodoc]] BioGptModel
    - forward
BioGptForCausalLM
[[autodoc]] BioGptForCausalLM
    - forward
BioGptForTokenClassification
[[autodoc]] BioGptForTokenClassification
    - forward
BioGptForSequenceClassification
[[autodoc]] BioGptForSequenceClassification
    - forward"
"
CANINE
Overview
The CANINE model was proposed in CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language
Representation by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. It's
among the first papers that trains a Transformer without using an explicit tokenization step (such as Byte Pair
Encoding (BPE), WordPiece or SentencePiece). Instead, the model is trained directly at a Unicode character-level.
Training at a character-level inevitably comes with a longer sequence length, which CANINE solves with an efficient
downsampling strategy, before applying a deep Transformer encoder.
The abstract from the paper is the following:
Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models
still require an explicit tokenization step. While recent tokenization approaches based on data-derived subword
lexicons are less brittle than manually engineered tokenizers, these techniques are not equally suited to all
languages, and the use of any fixed vocabulary may limit a model's ability to adapt. In this paper, we present CANINE,
a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a
pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias.
To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input
sequence length, with a deep transformer stack, which encodes context. CANINE outperforms a comparable mBERT model by
2.8 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28% fewer model parameters.
Tips:

CANINE uses no less than 3 Transformer encoders internally: 2 ""shallow"" encoders (which only consist of a single
  layer) and 1 ""deep"" encoder (which is a regular BERT encoder). First, a ""shallow"" encoder is used to contextualize
  the character embeddings, using local attention. Next, after downsampling, a ""deep"" encoder is applied. Finally,
  after upsampling, a ""shallow"" encoder is used to create the final character embeddings. Details regarding up- and
  downsampling can be found in the paper.
CANINE uses a max sequence length of 2048 characters by default. One can use [CanineTokenizer]
  to prepare text for the model.
Classification can be done by placing a linear layer on top of the final hidden state of the special [CLS] token
  (which has a predefined Unicode code point). For token classification tasks however, the downsampled sequence of
  tokens needs to be upsampled again to match the length of the original character sequence (which is 2048). The
  details for this can be found in the paper.

Models:

google/canine-c: Pre-trained with autoregressive character loss,
    12-layer, 768-hidden, 12-heads, 121M parameters (size ~500 MB).

google/canine-s: Pre-trained with subword loss, 12-layer,
    768-hidden, 12-heads, 121M parameters (size ~500 MB).

This model was contributed by nielsr. The original code can be found here.
Example
CANINE works on raw characters, so it can be used without a tokenizer:
thon

from transformers import CanineModel
import torch
model = CanineModel.from_pretrained(""google/canine-c"")  # model pre-trained with autoregressive character loss
text = ""hello world""
use Python's built-in ord() function to turn each character into its unicode code point id
input_ids = torch.tensor([[ord(char) for char in text]])
outputs = model(input_ids)  # forward pass
pooled_output = outputs.pooler_output
sequence_output = outputs.last_hidden_state

For batched inference and training, it is however recommended to make use of the tokenizer (to pad/truncate all
sequences to the same length):
thon

from transformers import CanineTokenizer, CanineModel
model = CanineModel.from_pretrained(""google/canine-c"")
tokenizer = CanineTokenizer.from_pretrained(""google/canine-c"")
inputs = [""Life is like a box of chocolates."", ""You never know what you gonna get.""]
encoding = tokenizer(inputs, padding=""longest"", truncation=True, return_tensors=""pt"")
outputs = model(**encoding)  # forward pass
pooled_output = outputs.pooler_output
sequence_output = outputs.last_hidden_state

Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Multiple choice task guide

CANINE specific outputs
[[autodoc]] models.canine.modeling_canine.CanineModelOutputWithPooling
CanineConfig
[[autodoc]] CanineConfig
CanineTokenizer
[[autodoc]] CanineTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
CanineModel
[[autodoc]] CanineModel
    - forward
CanineForSequenceClassification
[[autodoc]] CanineForSequenceClassification
    - forward
CanineForMultipleChoice
[[autodoc]] CanineForMultipleChoice
    - forward
CanineForTokenClassification
[[autodoc]] CanineForTokenClassification
    - forward
CanineForQuestionAnswering
[[autodoc]] CanineForQuestionAnswering
    - forward"
"
Nyströmformer
Overview
The Nyströmformer model was proposed in Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn
Fung, Yin Li, and Vikas Singh.
The abstract from the paper is the following:
Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component
that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or
dependence of other tokens on each specific token. While beneficial, the quadratic complexity of self-attention on the
input sequence length has limited its application to longer sequences -- a topic being actively studied in the
community. To address this limitation, we propose Nyströmformer -- a model that exhibits favorable scalability as a
function of sequence length. Our idea is based on adapting the Nyström method to approximate standard self-attention
with O(n) complexity. The scalability of Nyströmformer enables application to longer sequences with thousands of
tokens. We perform evaluations on multiple downstream tasks on the GLUE benchmark and IMDB reviews with standard
sequence length, and find that our Nyströmformer performs comparably, or in a few cases, even slightly better, than
standard self-attention. On longer sequence tasks in the Long Range Arena (LRA) benchmark, Nyströmformer performs
favorably relative to other efficient self-attention methods. Our code is available at this https URL.
This model was contributed by novice03. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

NystromformerConfig
[[autodoc]] NystromformerConfig
NystromformerModel
[[autodoc]] NystromformerModel
    - forward
NystromformerForMaskedLM
[[autodoc]] NystromformerForMaskedLM
    - forward
NystromformerForSequenceClassification
[[autodoc]] NystromformerForSequenceClassification
    - forward
NystromformerForMultipleChoice
[[autodoc]] NystromformerForMultipleChoice
    - forward
NystromformerForTokenClassification
[[autodoc]] NystromformerForTokenClassification
    - forward
NystromformerForQuestionAnswering
[[autodoc]] NystromformerForQuestionAnswering
    - forward"
"
Nezha
Overview
The Nezha model was proposed in NEZHA: Neural Contextualized Representation for Chinese Language Understanding by Junqiu Wei et al.
The abstract from the paper is the following:
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks
due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
In this technical report, we present our practice of pre-training language models named NEZHA (NEural contextualiZed
representation for CHinese lAnguage understanding) on Chinese corpora and finetuning for the Chinese NLU tasks. 
The current version of NEZHA is based on BERT with a collection of proven improvements, which include Functional 
Relative Positional Encoding as an effective positional encoding scheme, Whole Word Masking strategy,
Mixed Precision Training and the LAMB Optimizer in training the models. The experimental results show that NEZHA
achieves the state-of-the-art performances when finetuned on several representative Chinese tasks, including
named entity recognition (People's Daily NER), sentence matching (LCQMC), Chinese sentiment classification (ChnSenti)
and natural language inference (XNLI).
This model was contributed by sijunhe. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

NezhaConfig
[[autodoc]] NezhaConfig
NezhaModel
[[autodoc]] NezhaModel
    - forward
NezhaForPreTraining
[[autodoc]] NezhaForPreTraining
    - forward
NezhaForMaskedLM
[[autodoc]] NezhaForMaskedLM
    - forward
NezhaForNextSentencePrediction
[[autodoc]] NezhaForNextSentencePrediction
    - forward
NezhaForSequenceClassification
[[autodoc]] NezhaForSequenceClassification
    - forward
NezhaForMultipleChoice
[[autodoc]] NezhaForMultipleChoice
    - forward
NezhaForTokenClassification
[[autodoc]] NezhaForTokenClassification
    - forward
NezhaForQuestionAnswering
[[autodoc]] NezhaForQuestionAnswering
    - forward"
"
Video Vision Transformer (ViViT)
Overview
The Vivit model was proposed in ViViT: A Video Vision Transformer by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid.
The paper proposes one of the first successful pure-transformer based set of models for video understanding.
The abstract from the paper is the following:
We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efficient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classification benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks.
This model was contributed by jegormeister. The original code (written in JAX) can be found here.
VivitConfig
[[autodoc]] VivitConfig
VivitImageProcessor
[[autodoc]] VivitImageProcessor
    - preprocess
VivitModel
[[autodoc]] VivitModel
    - forward
VivitForVideoClassification
[[autodoc]] transformers.VivitForVideoClassification
    - forward"
"
Swin2SR
Overview
The Swin2SR model was proposed in Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
Swin2R improves the SwinIR model by incorporating Swin Transformer v2 layers which mitigates issues such as training instability, resolution gaps between pre-training
and fine-tuning, and hunger on data.
The abstract from the paper is the following:
Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual quality. For these reasons, quality enhancement of compressed images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks.
In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the ""AIM 2022 Challenge on Super-Resolution of Compressed Image and Video"".

 Swin2SR architecture. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
Demo notebooks for Swin2SR can be found here.
A demo Space for image super-resolution with SwinSR can be found here.
Swin2SRImageProcessor
[[autodoc]] Swin2SRImageProcessor
    - preprocess
Swin2SRConfig
[[autodoc]] Swin2SRConfig
Swin2SRModel
[[autodoc]] Swin2SRModel
    - forward
Swin2SRForImageSuperResolution
[[autodoc]] Swin2SRForImageSuperResolution
    - forward"
"
MobileNet V1
Overview
The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
The abstract from the paper is the following:
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
Tips:

The checkpoints are named mobilenet_v1_depth_size, for example mobilenet_v1_1.0_224, where 1.0 is the depth multiplier (sometimes also referred to as ""alpha"" or the width multiplier) and 224 is the resolution of the input images the model was trained on.

Even though the checkpoint is trained on images of specific size, the model will work on images of any size. The smallest supported image size is 32x32.

One can use [MobileNetV1ImageProcessor] to prepare images for the model.

The available image classification checkpoints are pre-trained on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes). However, the model predicts 1001 classes: the 1000 classes from ImageNet plus an extra “background” class (index 0).

The original TensorFlow checkpoints use different padding rules than PyTorch, requiring the model to determine the padding amount at inference time, since this depends on the input image size. To use native PyTorch padding behavior, create a [MobileNetV1Config] with tf_padding = False.

Unsupported features:

The [MobileNetV1Model] outputs a globally pooled version of the last hidden state. In the original model it is possible to use a 7x7 average pooling layer with stride 2 instead of global pooling. For larger inputs, this gives a pooled output that is larger than 1x1 pixel. The HuggingFace implementation does not support this.

It is currently not possible to specify an output_stride. For smaller output strides, the original model invokes dilated convolution to prevent the spatial resolution from being reduced further. The output stride of the HuggingFace model is always 32.

The original TensorFlow checkpoints include quantized models. We do not support these models as they include additional ""FakeQuantization"" operations to unquantize the weights.

It's common to extract the output from the pointwise layers at indices 5, 11, 12, 13 for downstream purposes. Using output_hidden_states=True returns the output from all intermediate layers. There is currently no way to limit this to specific layers.

This model was contributed by matthijs. The original code and weights can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileNetV1.

[MobileNetV1ForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
MobileNetV1Config
[[autodoc]] MobileNetV1Config
MobileNetV1FeatureExtractor
[[autodoc]] MobileNetV1FeatureExtractor
    - preprocess
MobileNetV1ImageProcessor
[[autodoc]] MobileNetV1ImageProcessor
    - preprocess
MobileNetV1Model
[[autodoc]] MobileNetV1Model
    - forward
MobileNetV1ForImageClassification
[[autodoc]] MobileNetV1ForImageClassification
    - forward"
"
BORT

This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: pip install -U transformers==4.30.0.

Overview
The BORT model was proposed in Optimal Subarchitecture Extraction for BERT by
Adrian de Wynter and Daniel J. Perry. It is an optimal subset of architectural parameters for the BERT, which the
authors refer to as ""Bort"".
The abstract from the paper is the following:
We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by
applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as
""Bort"", is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of 5.5% the
original BERT-large architecture, and 16% of the net size. Bort is also able to be pretrained in 288 GPU hours, which
is 1.2% of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large
(Liu et al., 2019), and about 33% of that of the world-record, in GPU hours, required to train BERT-large on the same
hardware. It is also 7.9x faster on a CPU, as well as being better performing than other compressed variants of the
architecture, and some of the non-compressed variants: it obtains performance improvements of between 0.3% and 31%,
absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks.
Tips:

BORT's model architecture is based on BERT, so one can refer to BERT's documentation page for the
  model's API as well as usage examples.
BORT uses the RoBERTa tokenizer instead of the BERT tokenizer, so one can refer to RoBERTa's documentation page for the tokenizer's API as well as usage examples.
BORT requires a specific fine-tuning algorithm, called Agora ,
  that is sadly not open-sourced yet. It would be very useful for the community, if someone tries to implement the
  algorithm to make BORT fine-tuning work.

This model was contributed by stefan-it. The original code can be found here."
"
GPT-Sw3
Overview
The GPT-Sw3 model was first proposed in
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish
by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman,
Fredrik Carlsson, Magnus Sahlgren.
Since that first paper the authors have extended their work and trained new models on their new 1.2TB corpora named The Nordic Pile.
GPT-Sw3 is a collection of large decoder-only pretrained transformer language models that were developed by AI Sweden
in collaboration with RISE and the WASP WARA for Media and Language. GPT-Sw3 has been trained on a dataset containing
320B tokens in Swedish, Norwegian, Danish, Icelandic, English, and programming code. The model was pretrained using a
causal language modeling (CLM) objective utilizing the NeMo Megatron GPT implementation.
This model was contributed by AI Sweden.
The implementation uses the GPT2Model coupled
with our GPTSw3Tokenizer. This means that AutoTokenizer and AutoModelForCausalLM map to our tokenizer
implementation and the corresponding GPT2 model implementation respectively.
Note that sentencepiece is required to use our tokenizer and can be installed with: pip install transformers[sentencepiece] or pip install sentencepiece
Example usage:
thon

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(""AI-Sweden/gpt-sw3-356m"")
model = AutoModelForCausalLM.from_pretrained(""AI-Sweden/gpt-sw3-356m"")
input_ids = tokenizer(""Träd är fina för att"", return_tensors=""pt"")[""input_ids""]
generated_token_ids = model.generate(inputs=input_ids, max_new_tokens=10, do_sample=True)[0]
print(tokenizer.decode(generated_token_ids))
Träd är fina för att de är färgstarka. Men ibland är det fint

Documentation resources

Text classification task guide
Token classification task guide
Causal language modeling task guide

GPTSw3Tokenizer
[[autodoc]] GPTSw3Tokenizer
    - save_vocabulary"
"
ViTMAE
Overview
The ViTMAE model was proposed in Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li,
Piotr Dollár, Ross Girshick. The paper shows that, by pre-training a Vision Transformer (ViT) to reconstruct pixel values for masked patches, one can get results after
fine-tuning that outperform supervised pre-training.
The abstract from the paper is the following:
This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the
input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates
only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask
tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs
enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity
models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream
tasks outperforms supervised pre-training and shows promising scaling behavior.
Tips:

MAE (masked auto encoding) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training objective is relatively simple:
by masking a large portion (75%) of the image patches, the model must reconstruct raw pixel values. One can use [ViTMAEForPreTraining] for this purpose.
After pre-training, one ""throws away"" the decoder used to reconstruct pixels, and one uses the encoder for fine-tuning/linear probing. This means that after
fine-tuning, one can directly plug in the weights into a [ViTForImageClassification].
One can use [ViTImageProcessor] to prepare images for the model. See the code examples for more info.
Note that the encoder of MAE is only used to encode the visual patches. The encoded patches are then concatenated with mask tokens, which the decoder (which also
consists of Transformer blocks) takes as input. Each mask token is a shared, learned vector that indicates the presence of a missing patch to be predicted. Fixed
sin/cos position embeddings are added both to the input of the encoder and the decoder.
For a visual understanding of how MAEs work you can check out this post.

 
 MAE architecture. Taken from the original paper. 
This model was contributed by nielsr. TensorFlow version of the model was contributed by sayakpaul and 
ariG23498 (equal contribution). The original code can be found here. 
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViTMAE.

[ViTMAEForPreTraining] is supported by this example script, allowing you to pre-train the model from scratch/further pre-train the model on custom data.
A notebook that illustrates how to visualize reconstructed pixel values with [ViTMAEForPreTraining] can be found here.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ViTMAEConfig
[[autodoc]] ViTMAEConfig
ViTMAEModel
[[autodoc]] ViTMAEModel
    - forward
ViTMAEForPreTraining
[[autodoc]] transformers.ViTMAEForPreTraining
    - forward
TFViTMAEModel
[[autodoc]] TFViTMAEModel
    - call
TFViTMAEForPreTraining
[[autodoc]] transformers.TFViTMAEForPreTraining
    - call"
"
RemBERT
Overview
The RemBERT model was proposed in Rethinking Embedding Coupling in Pre-trained Language Models by Hyung Won Chung, Thibault Févry, Henry Tsai, Melvin Johnson, Sebastian Ruder.
The abstract from the paper is the following:
We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art
pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to
significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By
reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on
standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that
allocating additional capacity to the output embedding provides benefits to the model that persist through the
fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger
output embeddings prevent the model's last layers from overspecializing to the pre-training task and encourage
Transformer representations to be more general and more transferable to other tasks and languages. Harnessing these
findings, we are able to train models that achieve strong performance on the XTREME benchmark without increasing the
number of parameters at the fine-tuning stage.
Tips:
For fine-tuning, RemBERT can be thought of as a bigger version of mBERT with an ALBERT-like factorization of the
embedding layer. The embeddings are not tied in pre-training, in contrast with BERT, which enables smaller input
embeddings (preserved during fine-tuning) and bigger output embeddings (discarded at fine-tuning). The tokenizer is
also similar to the Albert one rather than the BERT one.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

RemBertConfig
[[autodoc]] RemBertConfig
RemBertTokenizer
[[autodoc]] RemBertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
RemBertTokenizerFast
[[autodoc]] RemBertTokenizerFast
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
RemBertModel
[[autodoc]] RemBertModel
    - forward
RemBertForCausalLM
[[autodoc]] RemBertForCausalLM
    - forward
RemBertForMaskedLM
[[autodoc]] RemBertForMaskedLM
    - forward
RemBertForSequenceClassification
[[autodoc]] RemBertForSequenceClassification
    - forward
RemBertForMultipleChoice
[[autodoc]] RemBertForMultipleChoice
    - forward
RemBertForTokenClassification
[[autodoc]] RemBertForTokenClassification
    - forward
RemBertForQuestionAnswering
[[autodoc]] RemBertForQuestionAnswering
    - forward
TFRemBertModel
[[autodoc]] TFRemBertModel
    - call
TFRemBertForMaskedLM
[[autodoc]] TFRemBertForMaskedLM
    - call
TFRemBertForCausalLM
[[autodoc]] TFRemBertForCausalLM
    - call
TFRemBertForSequenceClassification
[[autodoc]] TFRemBertForSequenceClassification
    - call
TFRemBertForMultipleChoice
[[autodoc]] TFRemBertForMultipleChoice
    - call
TFRemBertForTokenClassification
[[autodoc]] TFRemBertForTokenClassification
    - call
TFRemBertForQuestionAnswering
[[autodoc]] TFRemBertForQuestionAnswering
    - call"
"
Informer
Overview
The Informer model was proposed in Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting  by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
This method introduces a Probabilistic Attention mechanism to select the ""active"" queries rather than the ""lazy"" queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.
The abstract from the paper is the following:
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder-decoder architecture. To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a ProbSparse self-attention mechanism, which achieves O(L logL) in time complexity and memory usage, and has comparable performance on sequences' dependency alignment. (ii) the self-attention distilling highlights dominating attention by halving cascading layer input, and efficiently handles extreme long input sequences. (iii) the generative style decoder, while conceptually simple, predicts the long time-series sequences at one forward operation rather than a step-by-step way, which drastically improves the inference speed of long-sequence predictions. Extensive experiments on four large-scale datasets demonstrate that Informer significantly outperforms existing methods and provides a new solution to the LSTF problem.
This model was contributed by elisim and kashif.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

Check out the Informer blog-post in HuggingFace blog: Multivariate Probabilistic Time Series Forecasting with Informer

InformerConfig
[[autodoc]] InformerConfig
InformerModel
[[autodoc]] InformerModel
    - forward
InformerForPrediction
[[autodoc]] InformerForPrediction
    - forward"
"
BEiT
Overview
The BEiT model was proposed in BEiT: BERT Pre-Training of Image Transformers by
Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of
Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class
of an image (as done in the original ViT paper), BEiT models are pre-trained to
predict visual tokens from the codebook of OpenAI's DALL-E model given masked
patches.
The abstract from the paper is the following:
We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation
from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image
modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image
patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first ""tokenize"" the original image into
visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training
objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we
directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder.
Experimental results on image classification and semantic segmentation show that our model achieves competitive results
with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K,
significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains
86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%).
Tips:

BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They
  outperform both the original model (ViT) as well as Data-efficient Image Transformers (DeiT) when fine-tuned on ImageNet-1K and CIFAR-100. You can check out demo notebooks regarding inference as well as
  fine-tuning on custom data here (you can just replace
  [ViTFeatureExtractor] by [BeitImageProcessor] and
  [ViTForImageClassification] by [BeitForImageClassification]).
There's also a demo notebook available which showcases how to combine DALL-E's image tokenizer with BEiT for
  performing masked image modeling. You can find it here.
As the BEiT models expect each image to be of the same size (resolution), one can use
  [BeitImageProcessor] to resize (or rescale) and normalize images for the model.
Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of
  each checkpoint. For example, microsoft/beit-base-patch16-224 refers to a base-sized architecture with patch
  resolution of 16x16 and fine-tuning resolution of 224x224. All checkpoints can be found on the hub.
The available checkpoints are either (1) pre-trained on ImageNet-22k (a collection of
  14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million
  images and 1,000 classes).
BEiT uses relative position embeddings, inspired by the T5 model. During pre-training, the authors shared the
  relative position bias among the several self-attention layers. During fine-tuning, each layer's relative position
  bias is initialized with the shared relative position bias obtained after pre-training. Note that, if one wants to
  pre-train a model from scratch, one needs to either set the use_relative_position_bias or the
  use_relative_position_bias attribute of [BeitConfig] to True in order to add
  position embeddings.

 BEiT pre-training. Taken from the original paper. 
This model was contributed by nielsr. The JAX/FLAX version of this model was
contributed by kamalkraj. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BEiT.

[BeitForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

Semantic segmentation
- Semantic segmentation task guide
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
BEiT specific outputs
[[autodoc]] models.beit.modeling_beit.BeitModelOutputWithPooling
[[autodoc]] models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling
BeitConfig
[[autodoc]] BeitConfig
BeitFeatureExtractor
[[autodoc]] BeitFeatureExtractor
    - call
    - post_process_semantic_segmentation
BeitImageProcessor
[[autodoc]] BeitImageProcessor
    - preprocess
    - post_process_semantic_segmentation
BeitModel
[[autodoc]] BeitModel
    - forward
BeitForMaskedImageModeling
[[autodoc]] BeitForMaskedImageModeling
    - forward
BeitForImageClassification
[[autodoc]] BeitForImageClassification
    - forward
BeitForSemanticSegmentation
[[autodoc]] BeitForSemanticSegmentation
    - forward
FlaxBeitModel
[[autodoc]] FlaxBeitModel
    - call
FlaxBeitForMaskedImageModeling
[[autodoc]] FlaxBeitForMaskedImageModeling
    - call
FlaxBeitForImageClassification
[[autodoc]] FlaxBeitForImageClassification
    - call"
"
WavLM
Overview
The WavLM model was proposed in WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen,
Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu,
Michael Zeng, Furu Wei.
The abstract from the paper is the following:
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been
attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker
identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is
challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks.
WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity
preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on
recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where
additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up
the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB
benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.
Tips:

WavLM is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please use
  [Wav2Vec2Processor] for the feature extraction.
WavLM model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
  using [Wav2Vec2CTCTokenizer].
WavLM performs especially well on speaker verification, speaker identification, and speaker diarization tasks.

Relevant checkpoints can be found under https://huggingface.co/models?other=wavlm.
This model was contributed by patrickvonplaten. The Authors' code can be
found here.
Documentation resources

Audio classification task guide
Automatic speech recognition task guide

WavLMConfig
[[autodoc]] WavLMConfig
WavLMModel
[[autodoc]] WavLMModel
    - forward
WavLMForCTC
[[autodoc]] WavLMForCTC
    - forward
WavLMForSequenceClassification
[[autodoc]] WavLMForSequenceClassification
    - forward
WavLMForAudioFrameClassification
[[autodoc]] WavLMForAudioFrameClassification
    - forward
WavLMForXVector
[[autodoc]] WavLMForXVector
    - forward"
"
Reformer

DISCLAIMER: This model is still a work in progress, if you see something strange, file a Github Issue.
Overview
The Reformer model was proposed in the paper Reformer: The Efficient Transformer by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
The abstract from the paper is the following:
Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can
be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of
Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its
complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual
layers instead of the standard residuals, which allows storing activations only once in the training process instead of
N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models
while being much more memory-efficient and much faster on long sequences.
This model was contributed by patrickvonplaten. The Authors' code can be
found here.
Tips:

Reformer does not work with torch.nn.DataParallel due to a bug in PyTorch, see issue #36035.
Use Axial position encoding (see below for more details). It’s a mechanism to avoid having a huge positional encoding matrix (when the sequence length is very big) by factorizing it into smaller matrices.
Replace traditional attention by LSH (local-sensitive hashing) attention (see below for more details). It’s a technique to avoid computing the full product query-key in the attention layers.
Avoid storing the intermediate results of each layer by using reversible transformer layers to obtain them during the backward pass (subtracting the residuals from the input of the next layer gives them back) or recomputing them for results inside a given layer (less efficient than storing them but saves memory).
Compute the feedforward operations by chunks and not on the whole batch.

Axial Positional Encodings
Axial Positional Encodings were first implemented in Google's trax library
and developed by the authors of this model's paper. In models that are treating very long input sequences, the
conventional position id encodings store an embedings vector of size \(d\) being the config.hidden_size for
every position \(i, \ldots, n_s\), with \(n_s\) being config.max_embedding_size. This means that having
a sequence length of \(n_s = 2^{19} \approx 0.5M\) and a config.hidden_size of \(d = 2^{10} \approx 1000\)
would result in a position encoding matrix:
$$X_{i,j}, \text{ with } i \in \left[1,\ldots, d\right] \text{ and } j \in \left[1,\ldots, n_s\right]$$
which alone has over 500M parameters to store. Axial positional encodings factorize \(X_{i,j}\) into two matrices:
$$X^{1}_{i,j}, \text{ with } i \in \left[1,\ldots, d^1\right] \text{ and } j \in \left[1,\ldots, n_s^1\right]$$
and
$$X^{2}_{i,j}, \text{ with } i \in \left[1,\ldots, d^2\right] \text{ and } j \in \left[1,\ldots, n_s^2\right]$$
with:
$$d = d^1 + d^2 \text{ and } n_s = n_s^1 \times n_s^2 .$$
Therefore the following holds:
$$X_{i,j} = \begin{cases}
X^{1}{i, k}, & \text{if }\ i < d^1 \text{ with } k = j \mod n_s^1 \
X^{2}{i - d^1, l}, & \text{if } i \ge d^1 \text{ with } l = \lfloor\frac{j}{n_s^1}\rfloor
\end{cases}$$
Intuitively, this means that a position embedding vector \(x_j \in \mathbb{R}^{d}\) is now the composition of two
factorized embedding vectors: \(x^1_{k, l} + x^2_{l, k}\), where as the config.max_embedding_size dimension
\(j\) is factorized into \(k \text{ and } l\). This design ensures that each position embedding vector
\(x_j\) is unique.
Using the above example again, axial position encoding with \(d^1 = 2^9, d^2 = 2^9, n_s^1 = 2^9, n_s^2 = 2^{10}\)
can drastically reduced the number of parameters from 500 000 000 to \(2^{18} + 2^{19} \approx 780 000\) parameters, this means 85% less memory usage.
In practice, the parameter config.axial_pos_embds_dim is set to a tuple \((d^1, d^2)\) which sum has to be
equal to config.hidden_size and config.axial_pos_shape is set to a tuple \((n_s^1, n_s^2)\) which
product has to be equal to config.max_embedding_size, which during training has to be equal to the sequence
length of the input_ids.
LSH Self Attention
In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key
query embedding vectors are also tied. LSH self attention uses the locality sensitive hashing mechanism proposed in
Practical and Optimal LSH for Angular Distance to assign each of the tied key
query embedding vectors to one of config.num_buckets possible buckets. The premise is that the more ""similar""
key query embedding vectors (in terms of cosine similarity) are to each other, the more likely they are assigned to
the same bucket.
The accuracy of the LSH mechanism can be improved by increasing config.num_hashes or directly the argument
num_hashes of the forward function so that the output of the LSH self attention better approximates the output
of the ""normal"" full self attention. The buckets are then sorted and chunked into query key embedding vector chunks
each of length config.lsh_chunk_length. For each chunk, the query embedding vectors attend to its key vectors
(which are tied to themselves) and to the key embedding vectors of config.lsh_num_chunks_before previous
neighboring chunks and config.lsh_num_chunks_after following neighboring chunks.
For more information, see the original Paper or this great blog post.
Note that config.num_buckets can also be factorized into a list \((n_{\text{buckets}}^1,
n_{\text{buckets}}^2)\). This way instead of assigning the query key embedding vectors to one of \((1,\ldots,
n_{\text{buckets}})\) they are assigned to one of \((1-1,\ldots, n_{\text{buckets}}^1-1, \ldots,
1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)\). This is crucial for very long sequences to
save memory.
When training a model from scratch, it is recommended to leave config.num_buckets=None, so that depending on the
sequence length a good value for num_buckets is calculated on the fly. This value will then automatically be
saved in the config and should be reused for inference.
Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from
\(\mathcal{O}(n_s \times n_s)\) to \(\mathcal{O}(n_s \times \log(n_s))\), which usually represents the memory
and time bottleneck in a transformer model, with \(n_s\) being the sequence length.
Local Self Attention
Local self attention is essentially a ""normal"" self attention layer with key, query and value projections, but is
chunked so that in each chunk of length config.local_chunk_length the query embedding vectors only attends to
the key embedding vectors in its chunk and to the key embedding vectors of config.local_num_chunks_before
previous neighboring chunks and config.local_num_chunks_after following neighboring chunks.
Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from
\(\mathcal{O}(n_s \times n_s)\) to \(\mathcal{O}(n_s \times \log(n_s))\), which usually represents the memory
and time bottleneck in a transformer model, with \(n_s\) being the sequence length.
Training
During training, we must ensure that the sequence length is set to a value that can be divided by the least common
multiple of config.lsh_chunk_length and config.local_chunk_length and that the parameters of the Axial
Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can
easily be trained on sequences as long as 64000 tokens.
For training, the [ReformerModelWithLMHead] should be used as follows:
python
input_ids = tokenizer.encode(""This is a sentence from the training data"", return_tensors=""pt"")
loss = model(input_ids, labels=input_ids)[0]
Documentation resources

Text classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide

ReformerConfig
[[autodoc]] ReformerConfig
ReformerTokenizer
[[autodoc]] ReformerTokenizer
    - save_vocabulary
ReformerTokenizerFast
[[autodoc]] ReformerTokenizerFast
ReformerModel
[[autodoc]] ReformerModel
    - forward
ReformerModelWithLMHead
[[autodoc]] ReformerModelWithLMHead
    - forward
ReformerForMaskedLM
[[autodoc]] ReformerForMaskedLM
    - forward
ReformerForSequenceClassification
[[autodoc]] ReformerForSequenceClassification
    - forward
ReformerForQuestionAnswering
[[autodoc]] ReformerForQuestionAnswering
    - forward"
"
DeBERTa
Overview
The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
RoBERTa.
The abstract from the paper is the following:
Recent progress in pre-trained neural language models has significantly improved the performance of many natural
language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with
disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the
disentangled attention mechanism, where each word is represented using two vectors that encode its content and
position, respectively, and the attention weights among words are computed using disentangled matrices on their
contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.
This model was contributed by DeBERTa. This model TF 2.0 implementation was
contributed by kamalkraj . The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DeBERTa. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog post on how to Accelerate Large Model Training using DeepSpeed with DeBERTa.
A blog post on Supercharged Customer Service with Machine Learning with DeBERTa.
[DebertaForSequenceClassification] is supported by this example script and notebook.
[TFDebertaForSequenceClassification] is supported by this example script and notebook.
Text classification task guide

[DebertaForTokenClassification] is supported by this example script and notebook.
[TFDebertaForTokenClassification] is supported by this example script and notebook.
Token classification chapter of the 🤗 Hugging Face Course.
Byte-Pair Encoding tokenization chapter of the 🤗 Hugging Face Course.
Token classification task guide

[DebertaForMaskedLM] is supported by this example script and notebook.
[TFDebertaForMaskedLM] is supported by this example script and notebook.
Masked language modeling chapter of the 🤗 Hugging Face Course.
Masked language modeling task guide

[DebertaForQuestionAnswering] is supported by this example script and notebook.
[TFDebertaForQuestionAnswering] is supported by this example script and notebook.
Question answering chapter of the 🤗 Hugging Face Course.
Question answering task guide

DebertaConfig
[[autodoc]] DebertaConfig
DebertaTokenizer
[[autodoc]] DebertaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
DebertaTokenizerFast
[[autodoc]] DebertaTokenizerFast
    - build_inputs_with_special_tokens
    - create_token_type_ids_from_sequences
DebertaModel
[[autodoc]] DebertaModel
    - forward
DebertaPreTrainedModel
[[autodoc]] DebertaPreTrainedModel
DebertaForMaskedLM
[[autodoc]] DebertaForMaskedLM
    - forward
DebertaForSequenceClassification
[[autodoc]] DebertaForSequenceClassification
    - forward
DebertaForTokenClassification
[[autodoc]] DebertaForTokenClassification
    - forward
DebertaForQuestionAnswering
[[autodoc]] DebertaForQuestionAnswering
    - forward
TFDebertaModel
[[autodoc]] TFDebertaModel
    - call
TFDebertaPreTrainedModel
[[autodoc]] TFDebertaPreTrainedModel
    - call
TFDebertaForMaskedLM
[[autodoc]] TFDebertaForMaskedLM
    - call
TFDebertaForSequenceClassification
[[autodoc]] TFDebertaForSequenceClassification
    - call
TFDebertaForTokenClassification
[[autodoc]] TFDebertaForTokenClassification
    - call
TFDebertaForQuestionAnswering
[[autodoc]] TFDebertaForQuestionAnswering
    - call"
"
MobileNet V2
Overview
The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
The abstract from the paper is the following:
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3.
The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters.
Tips:

The checkpoints are named mobilenet_v2_depth_size, for example mobilenet_v2_1.0_224, where 1.0 is the depth multiplier (sometimes also referred to as ""alpha"" or the width multiplier) and 224 is the resolution of the input images the model was trained on.

Even though the checkpoint is trained on images of specific size, the model will work on images of any size. The smallest supported image size is 32x32.

One can use [MobileNetV2ImageProcessor] to prepare images for the model.

The available image classification checkpoints are pre-trained on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes). However, the model predicts 1001 classes: the 1000 classes from ImageNet plus an extra “background” class (index 0).

The segmentation model uses a DeepLabV3+ head. The available semantic segmentation checkpoints are pre-trained on PASCAL VOC.

The original TensorFlow checkpoints use different padding rules than PyTorch, requiring the model to determine the padding amount at inference time, since this depends on the input image size. To use native PyTorch padding behavior, create a [MobileNetV2Config] with tf_padding = False.

Unsupported features:

The [MobileNetV2Model] outputs a globally pooled version of the last hidden state. In the original model it is possible to use an average pooling layer with a fixed 7x7 window and stride 1 instead of global pooling. For inputs that are larger than the recommended image size, this gives a pooled output that is larger than 1x1. The Hugging Face implementation does not support this.

The original TensorFlow checkpoints include quantized models. We do not support these models as they include additional ""FakeQuantization"" operations to unquantize the weights.

It's common to extract the output from the expansion layers at indices 10 and 13, as well as the output from the final 1x1 convolution layer, for downstream purposes. Using output_hidden_states=True returns the output from all intermediate layers. There is currently no way to limit this to specific layers.

The DeepLabV3+ segmentation head does not use the final convolution layer from the backbone, but this layer gets computed anyway. There is currently no way to tell [MobileNetV2Model] up to which layer it should run.

This model was contributed by matthijs. The original code and weights can be found here for the main model and here for DeepLabV3+.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileNetV2.

[MobileNetV2ForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

Semantic segmentation
- Semantic segmentation task guide
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
MobileNetV2Config
[[autodoc]] MobileNetV2Config
MobileNetV2FeatureExtractor
[[autodoc]] MobileNetV2FeatureExtractor
    - preprocess
    - post_process_semantic_segmentation
MobileNetV2ImageProcessor
[[autodoc]] MobileNetV2ImageProcessor
    - preprocess
    - post_process_semantic_segmentation
MobileNetV2Model
[[autodoc]] MobileNetV2Model
    - forward
MobileNetV2ForImageClassification
[[autodoc]] MobileNetV2ForImageClassification
    - forward
MobileNetV2ForSemanticSegmentation
[[autodoc]] MobileNetV2ForSemanticSegmentation
    - forward"
"
RegNet
Overview
The RegNet model was proposed in Designing Network Design Spaces by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
The abstract from the paper is the following:
In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.
Tips:

One can use [AutoImageProcessor] to prepare images for the model.
The huge 10B model from Self-supervised Pretraining of Visual Features in the Wild, trained on one billion Instagram images, is available on the hub

This model was contributed by Francesco. The TensorFlow version of the model
was contributed by sayakpaul and ariG23498.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with RegNet.

[RegNetForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
RegNetConfig
[[autodoc]] RegNetConfig
RegNetModel
[[autodoc]] RegNetModel
    - forward
RegNetForImageClassification
[[autodoc]] RegNetForImageClassification
    - forward
TFRegNetModel
[[autodoc]] TFRegNetModel
    - call
TFRegNetForImageClassification
[[autodoc]] TFRegNetForImageClassification
    - call
FlaxRegNetModel
[[autodoc]] FlaxRegNetModel
    - call
FlaxRegNetForImageClassification
[[autodoc]] FlaxRegNetForImageClassification
    - call"
"
MobileBERT
Overview
The MobileBERT model was proposed in MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny
Zhou. It's a bidirectional transformer based on the BERT model, which is compressed and accelerated using several
approaches.
The abstract from the paper is the following:
Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds
of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot
be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating
the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to
various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while
equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE
model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is
4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known benchmarks. On the
natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6 lower than BERT_BASE), and 62 ms
latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of
90.0/79.2 (1.5/2.1 higher than BERT_BASE).
Tips:

MobileBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
  than the left.
MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
  with a causal language modeling (CLM) objective are better in that regard.

This model was contributed by vshampor. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

MobileBertConfig
[[autodoc]] MobileBertConfig
MobileBertTokenizer
[[autodoc]] MobileBertTokenizer
MobileBertTokenizerFast
[[autodoc]] MobileBertTokenizerFast
MobileBert specific outputs
[[autodoc]] models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput
[[autodoc]] models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput
MobileBertModel
[[autodoc]] MobileBertModel
    - forward
MobileBertForPreTraining
[[autodoc]] MobileBertForPreTraining
    - forward
MobileBertForMaskedLM
[[autodoc]] MobileBertForMaskedLM
    - forward
MobileBertForNextSentencePrediction
[[autodoc]] MobileBertForNextSentencePrediction
    - forward
MobileBertForSequenceClassification
[[autodoc]] MobileBertForSequenceClassification
    - forward
MobileBertForMultipleChoice
[[autodoc]] MobileBertForMultipleChoice
    - forward
MobileBertForTokenClassification
[[autodoc]] MobileBertForTokenClassification
    - forward
MobileBertForQuestionAnswering
[[autodoc]] MobileBertForQuestionAnswering
    - forward
TFMobileBertModel
[[autodoc]] TFMobileBertModel
    - call
TFMobileBertForPreTraining
[[autodoc]] TFMobileBertForPreTraining
    - call
TFMobileBertForMaskedLM
[[autodoc]] TFMobileBertForMaskedLM
    - call
TFMobileBertForNextSentencePrediction
[[autodoc]] TFMobileBertForNextSentencePrediction
    - call
TFMobileBertForSequenceClassification
[[autodoc]] TFMobileBertForSequenceClassification
    - call
TFMobileBertForMultipleChoice
[[autodoc]] TFMobileBertForMultipleChoice
    - call
TFMobileBertForTokenClassification
[[autodoc]] TFMobileBertForTokenClassification
    - call
TFMobileBertForQuestionAnswering
[[autodoc]] TFMobileBertForQuestionAnswering
    - call"
"
DETR
Overview
The DETR model was proposed in End-to-End Object Detection with Transformers by
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR
consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for
object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use
things like region proposals, non-maximum suppression procedure and anchor generation. Moreover, DETR can also be
naturally extended to perform panoptic segmentation, by simply adding a mask head on top of the decoder outputs.
The abstract from the paper is the following:
We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the
detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression
procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the
new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via
bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries,
DETR reasons about the relations of the objects and the global image context to directly output the final set of
predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many
other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and
highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily
generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive
baselines.
This model was contributed by nielsr. The original code can be found here.
Here's a TLDR explaining how [~transformers.DetrForObjectDetection] works:
First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
ResNet-50/ResNet-101). Let's assume we also add a batch dimension. This means that the input to the backbone is a
tensor of shape (batch_size, 3, height, width), assuming the image has 3 color channels (RGB). The CNN backbone
outputs a new lower-resolution feature map, typically of shape (batch_size, 2048, height/32, width/32). This is
then projected to match the hidden dimension of the Transformer of DETR, which is 256 by default, using a
nn.Conv2D layer. So now, we have a tensor of shape (batch_size, 256, height/32, width/32). Next, the
feature map is flattened and transposed to obtain a tensor of shape (batch_size, seq_len, d_model) =
(batch_size, width/32*height/32, 256). So a difference with NLP models is that the sequence length is actually
longer than usual, but with a smaller d_model (which in NLP is typically 768 or higher).
Next, this is sent through the encoder, outputting encoder_hidden_states of the same shape (you can consider
these as image features). Next, so-called object queries are sent through the decoder. This is a tensor of shape
(batch_size, num_queries, d_model), with num_queries typically set to 100 and initialized with zeros.
These input embeddings are learnt positional encodings that the authors refer to as object queries, and similarly to
the encoder, they are added to the input of each attention layer. Each object query will look for a particular object
in the image. The decoder updates these embeddings through multiple self-attention and encoder-decoder attention layers
to output decoder_hidden_states of the same shape: (batch_size, num_queries, d_model). Next, two heads
are added on top for object detection: a linear layer for classifying each object query into one of the objects or ""no
object"", and a MLP to predict bounding boxes for each query.
The model is trained using a bipartite matching loss: so what we actually do is compare the predicted classes +
bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N
(so if an image only contains 4 objects, 96 annotations will just have a ""no object"" as class and ""no bounding box"" as
bounding box). The Hungarian matching algorithm is used to find
an optimal one-to-one mapping of each of the N queries to each of the N annotations. Next, standard cross-entropy (for
the classes) and a linear combination of the L1 and generalized IoU loss (for the
bounding boxes) are used to optimize the parameters of the model.
DETR can be naturally extended to perform panoptic segmentation (which unifies semantic segmentation and instance
segmentation). [~transformers.DetrForSegmentation] adds a segmentation mask head on top of
[~transformers.DetrForObjectDetection]. The mask head can be trained either jointly, or in a two steps process,
where one first trains a [~transformers.DetrForObjectDetection] model to detect bounding boxes around both
""things"" (instances) and ""stuff"" (background things like trees, roads, sky), then freeze all the weights and train only
the mask head for 25 epochs. Experimentally, these two approaches give similar results. Note that predicting boxes is
required for the training to be possible, since the Hungarian matching is computed using distances between boxes.
Tips:

DETR uses so-called object queries to detect objects in an image. The number of queries determines the maximum
  number of objects that can be detected in a single image, and is set to 100 by default (see parameter
  num_queries of [~transformers.DetrConfig]). Note that it's good to have some slack (in COCO, the
  authors used 100, while the maximum number of objects in a COCO image is ~70).
The decoder of DETR updates the query embeddings in parallel. This is different from language models like GPT-2,
  which use autoregressive decoding instead of parallel. Hence, no causal attention mask is used.
DETR adds position embeddings to the hidden states at each self-attention and cross-attention layer before projecting
  to queries and keys. For the position embeddings of the image, one can choose between fixed sinusoidal or learned
  absolute position embeddings. By default, the parameter position_embedding_type of
  [~transformers.DetrConfig] is set to ""sine"".
During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help
  the model output the correct number of objects of each class. If you set the parameter auxiliary_loss of
  [~transformers.DetrConfig] to True, then prediction feedforward neural networks and Hungarian losses
  are added after each decoder layer (with the FFNs sharing parameters).
If you want to train the model in a distributed environment across multiple nodes, then one should update the
  num_boxes variable in the DetrLoss class of modeling_detr.py. When training on multiple nodes, this should be
  set to the average number of target boxes across all nodes, as can be seen in the original implementation here.
[~transformers.DetrForObjectDetection] and [~transformers.DetrForSegmentation] can be initialized with
  any convolutional backbone available in the timm library.
  Initializing with a MobileNet backbone for example can be done by setting the backbone attribute of
  [~transformers.DetrConfig] to ""tf_mobilenetv3_small_075"", and then initializing the model with that
  config.
DETR resizes the input images such that the shortest side is at least a certain amount of pixels while the longest is
  at most 1333 pixels. At training time, scale augmentation is used such that the shortest side is randomly set to at
  least 480 and at most 800 pixels. At inference time, the shortest side is set to 800. One can use
  [~transformers.DetrImageProcessor] to prepare images (and optional annotations in COCO format) for the
  model. Due to this resizing, images in a batch can have different sizes. DETR solves this by padding images up to the
  largest size in a batch, and by creating a pixel mask that indicates which pixels are real/which are padding.
  Alternatively, one can also define a custom collate_fn in order to batch images together, using
  [~transformers.DetrImageProcessor.pad_and_create_pixel_mask].
The size of the images will determine the amount of memory being used, and will thus determine the batch_size.
  It is advised to use a batch size of 2 per GPU. See this Github thread for more info.

There are three ways to instantiate a DETR model (depending on what you prefer):
Option 1: Instantiate DETR with pre-trained weights for entire model

from transformers import DetrForObjectDetection
model = DetrForObjectDetection.from_pretrained(""facebook/detr-resnet-50"")

Option 2: Instantiate DETR with randomly initialized weights for Transformer, but pre-trained weights for backbone

from transformers import DetrConfig, DetrForObjectDetection
config = DetrConfig()
model = DetrForObjectDetection(config)
Option 3: Instantiate DETR with randomly initialized weights for backbone + Transformerpy
config = DetrConfig(use_pretrained_backbone=False)
model = DetrForObjectDetection(config)

As a summary, consider the following table:
| Task | Object detection | Instance segmentation | Panoptic segmentation |
|------|------------------|-----------------------|-----------------------|
| Description | Predicting bounding boxes and class labels around objects in an image | Predicting masks around objects (i.e. instances) in an image | Predicting masks around both objects (i.e. instances) as well as ""stuff"" (i.e. background things like trees and roads) in an image |
| Model | [~transformers.DetrForObjectDetection] | [~transformers.DetrForSegmentation] | [~transformers.DetrForSegmentation] |
| Example dataset | COCO detection | COCO detection, COCO panoptic | COCO panoptic  |                                                                        |
| Format of annotations to provide to  [~transformers.DetrImageProcessor] | {'image_id': int, 'annotations': List[Dict]} each Dict being a COCO object annotation  | {'image_id': int, 'annotations': List[Dict]}  (in case of COCO detection) or {'file_name': str, 'image_id': int, 'segments_info': List[Dict]} (in case of COCO panoptic) | {'file_name': str, 'image_id': int, 'segments_info': List[Dict]} and masks_path (path to directory containing PNG files of the masks) |
| Postprocessing (i.e. converting the output of the model to COCO API) | [~transformers.DetrImageProcessor.post_process] | [~transformers.DetrImageProcessor.post_process_segmentation] | [~transformers.DetrImageProcessor.post_process_segmentation], [~transformers.DetrImageProcessor.post_process_panoptic] |
| evaluators | CocoEvaluator with iou_types=""bbox"" | CocoEvaluator with iou_types=""bbox"" or ""segm"" | CocoEvaluator with iou_tupes=""bbox"" or ""segm"", PanopticEvaluator |
In short, one should prepare the data either in COCO detection or COCO panoptic format, then use
[~transformers.DetrImageProcessor] to create pixel_values, pixel_mask and optional
labels, which can then be used to train (or fine-tune) a model. For evaluation, one should first convert the
outputs of the model using one of the postprocessing methods of [~transformers.DetrImageProcessor]. These can
be be provided to either CocoEvaluator or PanopticEvaluator, which allow you to calculate metrics like
mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the original repository. See the example notebooks for more info regarding evaluation.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DETR.

All example notebooks illustrating fine-tuning [DetrForObjectDetection] and [DetrForSegmentation] on a custom dataset an be found here.
See also: Object detection task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
DETR specific outputs
[[autodoc]] models.detr.modeling_detr.DetrModelOutput
[[autodoc]] models.detr.modeling_detr.DetrObjectDetectionOutput
[[autodoc]] models.detr.modeling_detr.DetrSegmentationOutput
DetrConfig
[[autodoc]] DetrConfig
DetrImageProcessor
[[autodoc]] DetrImageProcessor
    - preprocess
    - post_process_object_detection
    - post_process_semantic_segmentation
    - post_process_instance_segmentation
    - post_process_panoptic_segmentation
DetrFeatureExtractor
[[autodoc]] DetrFeatureExtractor
    - call
    - post_process_object_detection
    - post_process_semantic_segmentation
    - post_process_instance_segmentation
    - post_process_panoptic_segmentation
DetrModel
[[autodoc]] DetrModel
    - forward
DetrForObjectDetection
[[autodoc]] DetrForObjectDetection
    - forward
DetrForSegmentation
[[autodoc]] DetrForSegmentation
    - forward"
"
NLLB-MOE
Overview
The NLLB model was presented in No Language Left Behind: Scaling Human-Centered Machine Translation by Marta R. Costa-jussà, James Cross, Onur Çelebi,
Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula,
Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews,
Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers,
Safiyyah Saleem, Holger Schwenk, and Jeff Wang.
The abstract of the paper is the following:
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the
200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by
first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed
at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of
Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training
improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using
a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety.
Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.
Tips:

M2M100ForConditionalGeneration is the base model for both NLLB and NLLB MoE
The NLLB-MoE is very similar to the NLLB model, but it's feed forward layer is based on the implementation of SwitchTransformers.
The tokenizer is the same as the NLLB models.

This model was contributed by Arthur Zucker.
The original code can be found here.
Implementation differences with SwitchTransformers
The biggest difference is the way the tokens are routed. NLLB-MoE uses a top-2-gate which means that for each input, only the top two experts are selected based on the 
highest predicted probabilities from the gating network, and the remaining experts are ignored. In SwitchTransformers, only the top-1 probabilities are computed, 
which means that tokens have less probability of being forwarded. Moreover, if a token is not routed to any expert, SwitchTransformers still adds its unmodified hidden 
states (kind of like a residual connection) while they are masked in NLLB's top-2 routing mechanism. 
Generating with NLLB-MoE
The avalable checkpoints requires around 350GB of storage. Make sure to use accelerate if you do not have enough RAM on your machine.
While generating the target text set the forced_bos_token_id to the target language id. The following
example shows how to translate English to French using the facebook/nllb-200-distilled-600M model.
Note that we're using the BCP-47 code for French fra_Latn. See here
for the list of all BCP-47 in the Flores 200 dataset.
thon

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""facebook/nllb-moe-54b"")
model = AutoModelForSeq2SeqLM.from_pretrained(""facebook/nllb-moe-54b"")
article = ""Previously, Ring's CEO, Jamie Siminoff, remarked the company started when his doorbell wasn't audible from his shop in his garage.""
inputs = tokenizer(article, return_tensors=""pt"")
translated_tokens = model.generate(
     **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[""fra_Latn""], max_length=50
 )
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
""Auparavant, le PDG de Ring, Jamie Siminoff, a fait remarquer que la société avait commencé lorsque sa sonnette n'était pas audible depuis son magasin dans son garage.""

Generating from any other language than English
English (eng_Latn) is set as the default language from which to translate. In order to specify that you'd like to translate from a different language,
you should specify the BCP-47 code in the src_lang keyword argument of the tokenizer initialization.
See example below for a translation from romanian to german:
thon

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""facebook/nllb-moe-54b"", src_lang=""ron_Latn"")
model = AutoModelForSeq2SeqLM.from_pretrained(""facebook/nllb-moe-54b"")
article = ""Şeful ONU spune că nu există o soluţie militară în Siria""
inputs = tokenizer(article, return_tensors=""pt"")
translated_tokens = model.generate(
     **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[""deu_Latn""], max_length=30
 )
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

Documentation resources

Translation task guide
Summarization task guide

NllbMoeConfig
[[autodoc]] NllbMoeConfig
NllbMoeTop2Router
[[autodoc]] NllbMoeTop2Router
    - route_tokens
    - forward
NllbMoeSparseMLP
[[autodoc]] NllbMoeSparseMLP
    - forward
NllbMoeModel
[[autodoc]] NllbMoeModel
    - forward
NllbMoeForConditionalGeneration
[[autodoc]] NllbMoeForConditionalGeneration
    - forward"
"
Trajectory Transformer

This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: pip install -U transformers==4.30.0.

Overview
The Trajectory Transformer model was proposed in Offline Reinforcement Learning as One Big Sequence Modeling Problem  by Michael Janner, Qiyang Li, Sergey Levine.
The abstract from the paper is the following:
Reinforcement learning (RL) is typically concerned with estimating stationary policies or single-step models,
leveraging the Markov property to factorize problems in time. However, we can also view RL as a generic sequence
modeling problem, with the goal being to produce a sequence of actions that leads to a sequence of high rewards.
Viewed in this way, it is tempting to consider whether high-capacity sequence prediction models that work well
in other domains, such as natural-language processing, can also provide effective solutions to the RL problem.
To this end, we explore how RL can be tackled with the tools of sequence modeling, using a Transformer architecture
to model distributions over trajectories and repurposing beam search as a planning algorithm. Framing RL as sequence
modeling problem simplifies a range of design decisions, allowing us to dispense with many of the components common
in offline RL algorithms. We demonstrate the flexibility of this approach across long-horizon dynamics prediction,
imitation learning, goal-conditioned RL, and offline RL. Further, we show that this approach can be combined with
existing model-free algorithms to yield a state-of-the-art planner in sparse-reward, long-horizon tasks.
Tips:
This Transformer is used for deep reinforcement learning. To use it, you need to create sequences from
actions, states and rewards from all previous timesteps. This model will treat all these elements together
as one big sequence (a trajectory).
This model was contributed by CarlCochet. The original code can be found here.
TrajectoryTransformerConfig
[[autodoc]] TrajectoryTransformerConfig
TrajectoryTransformerModel
[[autodoc]] TrajectoryTransformerModel
    - forward"
"
GPT Neo
Overview
The GPTNeo model was released in the EleutherAI/gpt-neo repository by Sid
Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like causal language model trained on the
Pile dataset.
The architecture is similar to GPT2 except that GPT Neo uses local attention in every other layer with a window size of
256 tokens.
This model was contributed by valhalla.
Generation
The generate() method can be used to generate text using GPT Neo model.
thon

from transformers import GPTNeoForCausalLM, GPT2Tokenizer
model = GPTNeoForCausalLM.from_pretrained(""EleutherAI/gpt-neo-1.3B"")
tokenizer = GPT2Tokenizer.from_pretrained(""EleutherAI/gpt-neo-1.3B"")
prompt = (
     ""In a shocking finding, scientists discovered a herd of unicorns living in a remote, ""
     ""previously unexplored valley, in the Andes Mountains. Even more surprising to the ""
     ""researchers was the fact that the unicorns spoke perfect English.""
 )
input_ids = tokenizer(prompt, return_tensors=""pt"").input_ids
gen_tokens = model.generate(
     input_ids,
     do_sample=True,
     temperature=0.9,
     max_length=100,
 )
gen_text = tokenizer.batch_decode(gen_tokens)[0]

Documentation resources

Text classification task guide
Causal language modeling task guide

GPTNeoConfig
[[autodoc]] GPTNeoConfig
GPTNeoModel
[[autodoc]] GPTNeoModel
    - forward
GPTNeoForCausalLM
[[autodoc]] GPTNeoForCausalLM
    - forward
GPTNeoForQuestionAnswering
[[autodoc]] GPTNeoForQuestionAnswering
    - forward
GPTNeoForSequenceClassification
[[autodoc]] GPTNeoForSequenceClassification
    - forward
GPTNeoForTokenClassification
[[autodoc]] GPTNeoForTokenClassification
    - forward
FlaxGPTNeoModel
[[autodoc]] FlaxGPTNeoModel
    - call
FlaxGPTNeoForCausalLM
[[autodoc]] FlaxGPTNeoForCausalLM
    - call"
"
T5v1.1
Overview
T5v1.1 was released in the google-research/text-to-text-transfer-transformer
repository by Colin Raffel et al. It's an improved version of the original T5 model.
One can directly plug in the weights of T5v1.1 into a T5 model, like so:
thon

from transformers import T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained(""google/t5-v1_1-base"")

T5 Version 1.1 includes the following improvements compared to the original T5 model:

GEGLU activation in the feed-forward hidden layer, rather than ReLU. See this paper.

Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.

Pre-trained on C4 only without mixing in the downstream tasks.

No parameter sharing between the embedding and classifier layer.

""xl"" and ""xxl"" replace ""3B"" and ""11B"". The model shapes are a bit different - larger d_model and smaller
  num_heads and d_ff.

Note: T5 Version 1.1 was only pre-trained on C4 excluding any supervised
training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, unlike the original T5
model. Since t5v1.1 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.
Google has released the following variants:

google/t5-v1_1-small

google/t5-v1_1-base

google/t5-v1_1-large

google/t5-v1_1-xl

google/t5-v1_1-xxl.

One can refer to T5's documentation page for all tips, code examples and notebooks.
This model was contributed by patrickvonplaten. The original code can be
found here."
"
LayoutLMv3
Overview
The LayoutLMv3 model was proposed in LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
LayoutLMv3 simplifies LayoutLMv2 by using patch embeddings (as in ViT) instead of leveraging a CNN backbone, and pre-trains the model on 3 objectives: masked language modeling (MLM), masked image modeling (MIM)
and word-patch alignment (WPA).
The abstract from the paper is the following:
Self-supervised pre-training techniques have achieved remarkable progress in Document AI. Most multimodal pre-trained models use a masked language modeling objective to learn bidirectional representations on the text modality, but they differ in pre-training objectives for the image modality. This discrepancy adds difficulty to multimodal representation learning. In this paper, we propose LayoutLMv3 to pre-train multimodal Transformers for Document AI with unified text and image masking. Additionally, LayoutLMv3 is pre-trained with a word-patch alignment objective to learn cross-modal alignment by predicting whether the corresponding image patch of a text word is masked. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model for both text-centric and image-centric Document AI tasks. Experimental results show that LayoutLMv3 achieves state-of-the-art performance not only in text-centric tasks, including form understanding, receipt understanding, and document visual question answering, but also in image-centric tasks such as document image classification and document layout analysis.
Tips:

In terms of data processing, LayoutLMv3 is identical to its predecessor LayoutLMv2, except that:
images need to be resized and normalized with channels in regular RGB format. LayoutLMv2 on the other hand normalizes the images internally and expects the channels in BGR format.
text is tokenized using byte-pair encoding (BPE), as opposed to WordPiece.
  Due to these differences in data preprocessing, one can use [LayoutLMv3Processor] which internally combines a [LayoutLMv3ImageProcessor] (for the image modality) and a [LayoutLMv3Tokenizer]/[LayoutLMv3TokenizerFast] (for the text modality) to prepare all data for the model.

Regarding usage of [LayoutLMv3Processor], we refer to the usage guide of its predecessor.
Demo notebooks for LayoutLMv3 can be found here.
Demo scripts can be found here.

 LayoutLMv3 architecture. Taken from the original paper. 
This model was contributed by nielsr. The TensorFlow version of this model was added by chriskoo, tokec, and lre. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLMv3. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

LayoutLMv3 is nearly identical to LayoutLMv2, so we've also included LayoutLMv2 resources you can adapt for LayoutLMv3 tasks. For these notebooks, take care to use [LayoutLMv2Processor] instead when preparing data for the model!

[LayoutLMv2ForSequenceClassification] is supported by this notebook.
Text classification task guide

[LayoutLMv3ForTokenClassification] is supported by this example script and notebook.
A notebook for how to perform inference with [LayoutLMv2ForTokenClassification] and a notebook for how to perform inference when no labels are available with [LayoutLMv2ForTokenClassification].
A notebook for how to finetune [LayoutLMv2ForTokenClassification] with the 🤗 Trainer.
Token classification task guide

[LayoutLMv2ForQuestionAnswering] is supported by this notebook.
Question answering task guide

Document question answering
- Document question answering task guide
LayoutLMv3Config
[[autodoc]] LayoutLMv3Config
LayoutLMv3FeatureExtractor
[[autodoc]] LayoutLMv3FeatureExtractor
    - call
LayoutLMv3ImageProcessor
[[autodoc]] LayoutLMv3ImageProcessor
    - preprocess
LayoutLMv3Tokenizer
[[autodoc]] LayoutLMv3Tokenizer
    - call
    - save_vocabulary
LayoutLMv3TokenizerFast
[[autodoc]] LayoutLMv3TokenizerFast
    - call
LayoutLMv3Processor
[[autodoc]] LayoutLMv3Processor
    - call
LayoutLMv3Model
[[autodoc]] LayoutLMv3Model
    - forward
LayoutLMv3ForSequenceClassification
[[autodoc]] LayoutLMv3ForSequenceClassification
    - forward
LayoutLMv3ForTokenClassification
[[autodoc]] LayoutLMv3ForTokenClassification
    - forward
LayoutLMv3ForQuestionAnswering
[[autodoc]] LayoutLMv3ForQuestionAnswering
    - forward
TFLayoutLMv3Model
[[autodoc]] TFLayoutLMv3Model
    - call
TFLayoutLMv3ForSequenceClassification
[[autodoc]] TFLayoutLMv3ForSequenceClassification
    - call
TFLayoutLMv3ForTokenClassification
[[autodoc]] TFLayoutLMv3ForTokenClassification
    - call
TFLayoutLMv3ForQuestionAnswering
[[autodoc]] TFLayoutLMv3ForQuestionAnswering
    - call"
"
LXMERT
Overview
The LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from Transformers by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders
(one for the vision modality, one for the language modality, and then one to fuse both modalities) pretrained using a
combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked
visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. The pretraining
consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.
The abstract from the paper is the following:
Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly,
the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality
Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we
build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language
encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language
semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative
pretraining tasks: masked language modeling, masked object prediction (feature regression and label classification),
cross-modality matching, and image question answering. These tasks help in learning both intra-modality and
cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art
results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our
pretrained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR, and improve the previous
best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel
model components and pretraining strategies significantly contribute to our strong results; and also present several
attention visualizations for the different encoders
Tips:

Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features
  will work.
Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the
  cross-modality layer, so they contain information from both modalities. To access a modality that only attends to
  itself, select the vision/language hidden states from the first input in the tuple.
The bidirectional cross-modality encoder attention only returns attention values when the language modality is used
  as the input and the vision modality is used as the context vector. Further, while the cross-modality encoder
  contains self-attention for each respective modality and cross-attention, only the cross attention is returned and
  both self attention outputs are disregarded.

This model was contributed by eltoto1219. The original code can be found here.
Documentation resources

Question answering task guide

LxmertConfig
[[autodoc]] LxmertConfig
LxmertTokenizer
[[autodoc]] LxmertTokenizer
LxmertTokenizerFast
[[autodoc]] LxmertTokenizerFast
Lxmert specific outputs
[[autodoc]] models.lxmert.modeling_lxmert.LxmertModelOutput
[[autodoc]] models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput
[[autodoc]] models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput
[[autodoc]] models.lxmert.modeling_tf_lxmert.TFLxmertModelOutput
[[autodoc]] models.lxmert.modeling_tf_lxmert.TFLxmertForPreTrainingOutput
LxmertModel
[[autodoc]] LxmertModel
    - forward
LxmertForPreTraining
[[autodoc]] LxmertForPreTraining
    - forward
LxmertForQuestionAnswering
[[autodoc]] LxmertForQuestionAnswering
    - forward
TFLxmertModel
[[autodoc]] TFLxmertModel
    - call
TFLxmertForPreTraining
[[autodoc]] TFLxmertForPreTraining
    - call"
"
LayoutLMV2
Overview
The LayoutLMV2 model was proposed in LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu,
Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves LayoutLM to obtain
state-of-the-art results across several document image understanding benchmarks:

information extraction from scanned documents: the FUNSD dataset (a
  collection of 199 annotated forms comprising more than 30,000 words), the CORD
  dataset (a collection of 800 receipts for training, 100 for validation and 100 for testing), the SROIE dataset (a collection of 626 receipts for training and 347 receipts for testing)
  and the Kleister-NDA dataset (a collection of non-disclosure
  agreements from the EDGAR database, including 254 documents for training, 83 documents for validation, and 203
  documents for testing).
document image classification: the RVL-CDIP dataset (a collection of
  400,000 images belonging to one of 16 classes).
document visual question answering: the DocVQA dataset (a collection of 50,000
  questions defined on 12,000+ document images).

The abstract from the paper is the following:
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to
its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. In this
paper, we present LayoutLMv2 by pre-training text, layout and image in a multi-modal framework, where new model
architectures and pre-training tasks are leveraged. Specifically, LayoutLMv2 not only uses the existing masked
visual-language modeling task but also the new text-image alignment and text-image matching tasks in the pre-training
stage, where cross-modality interaction is better learned. Meanwhile, it also integrates a spatial-aware self-attention
mechanism into the Transformer architecture, so that the model can fully understand the relative positional
relationship among different text blocks. Experiment results show that LayoutLMv2 outperforms strong baselines and
achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks,
including FUNSD (0.7895 -> 0.8420), CORD (0.9493 -> 0.9601), SROIE (0.9524 -> 0.9781), Kleister-NDA (0.834 -> 0.852),
RVL-CDIP (0.9443 -> 0.9564), and DocVQA (0.7295 -> 0.8672). The pre-trained LayoutLMv2 model is publicly available at
this https URL.
LayoutLMv2 depends on detectron2, torchvision and tesseract. Run the
following to install them:
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
python -m pip install torchvision tesseract
(If you are developing for LayoutLMv2, note that passing the doctests also requires the installation of these packages.)
Tips:

The main difference between LayoutLMv1 and LayoutLMv2 is that the latter incorporates visual embeddings during
  pre-training (while LayoutLMv1 only adds visual embeddings during fine-tuning).
LayoutLMv2 adds both a relative 1D attention bias as well as a spatial 2D attention bias to the attention scores in
  the self-attention layers. Details can be found on page 5 of the paper.
Demo notebooks on how to use the LayoutLMv2 model on RVL-CDIP, FUNSD, DocVQA, CORD can be found here.
LayoutLMv2 uses Facebook AI's Detectron2 package for its visual
  backbone. See this link for installation
  instructions.
In addition to input_ids, [~LayoutLMv2Model.forward] expects 2 additional inputs, namely
  image and bbox. The image input corresponds to the original document image in which the text
  tokens occur. The model expects each document image to be of size 224x224. This means that if you have a batch of
  document images, image should be a tensor of shape (batch_size, 3, 224, 224). This can be either a
  torch.Tensor or a Detectron2.structures.ImageList. You don't need to normalize the channels, as this is
  done by the model. Important to note is that the visual backbone expects BGR channels instead of RGB, as all models
  in Detectron2 are pre-trained using the BGR format. The bbox input are the bounding boxes (i.e. 2D-positions)
  of the input text tokens. This is identical to [LayoutLMModel]. These can be obtained using an
  external OCR engine such as Google's Tesseract (there's a Python
  wrapper available). Each bounding box should be in (x0, y0, x1, y1)
  format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1)
  represents the position of the lower right corner. Note that one first needs to normalize the bounding boxes to be on
  a 0-1000 scale. To normalize, you can use the following function:

python
def normalize_bbox(bbox, width, height):
    return [
        int(1000 * (bbox[0] / width)),
        int(1000 * (bbox[1] / height)),
        int(1000 * (bbox[2] / width)),
        int(1000 * (bbox[3] / height)),
    ]
Here, width and height correspond to the width and height of the original document in which the token
occurs (before resizing the image). Those can be obtained using the Python Image Library (PIL) library for example, as
follows:
thon
from PIL import Image
image = Image.open(
    ""name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images).""
)
width, height = image.size

However, this model includes a brand new [~transformers.LayoutLMv2Processor] which can be used to directly
prepare data for the model (including applying OCR under the hood). More information can be found in the ""Usage""
section below.

Internally, [~transformers.LayoutLMv2Model] will send the image input through its visual backbone to
  obtain a lower-resolution feature map, whose shape is equal to the image_feature_pool_shape attribute of
  [~transformers.LayoutLMv2Config]. This feature map is then flattened to obtain a sequence of image tokens. As
  the size of the feature map is 7x7 by default, one obtains 49 image tokens. These are then concatenated with the text
  tokens, and send through the Transformer encoder. This means that the last hidden states of the model will have a
  length of 512 + 49 = 561, if you pad the text tokens up to the max length. More generally, the last hidden states
  will have a shape of seq_length + image_feature_pool_shape[0] *
  config.image_feature_pool_shape[1].
When calling [~transformers.LayoutLMv2Model.from_pretrained], a warning will be printed with a long list of
  parameter names that are not initialized. This is not a problem, as these parameters are batch normalization
  statistics, which are going to have values when fine-tuning on a custom dataset.
If you want to train the model in a distributed environment, make sure to call [synchronize_batch_norm] on the
  model in order to properly synchronize the batch normalization layers of the visual backbone.

In addition, there's LayoutXLM, which is a multilingual version of LayoutLMv2. More information can be found on
LayoutXLM's documentation page.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLMv2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A notebook on how to finetune LayoutLMv2 for text-classification on RVL-CDIP dataset.
See also: Text classification task guide

A notebook on how to finetune LayoutLMv2 for question-answering on DocVQA dataset.
See also: Question answering task guide
See also: Document question answering task guide

A notebook on how to finetune LayoutLMv2 for token-classification on CORD dataset.
A notebook on how to finetune LayoutLMv2 for token-classification on FUNSD dataset.
See also: Token classification task guide

Usage: LayoutLMv2Processor
The easiest way to prepare data for the model is to use [LayoutLMv2Processor], which internally
combines a image processor ([LayoutLMv2ImageProcessor]) and a tokenizer
([LayoutLMv2Tokenizer] or [LayoutLMv2TokenizerFast]). The image processor
handles the image modality, while the tokenizer handles the text modality. A processor combines both, which is ideal
for a multi-modal model like LayoutLMv2. Note that you can still use both separately, if you only want to handle one
modality.
thon
from transformers import LayoutLMv2ImageProcessor, LayoutLMv2TokenizerFast, LayoutLMv2Processor
image_processor = LayoutLMv2ImageProcessor()  # apply_ocr is set to True by default
tokenizer = LayoutLMv2TokenizerFast.from_pretrained(""microsoft/layoutlmv2-base-uncased"")
processor = LayoutLMv2Processor(image_processor, tokenizer)

In short, one can provide a document image (and possibly additional data) to [LayoutLMv2Processor],
and it will create the inputs expected by the model. Internally, the processor first uses
[LayoutLMv2ImageProcessor] to apply OCR on the image to get a list of words and normalized
bounding boxes, as well to resize the image to a given size in order to get the image input. The words and
normalized bounding boxes are then provided to [LayoutLMv2Tokenizer] or
[LayoutLMv2TokenizerFast], which converts them to token-level input_ids,
attention_mask, token_type_ids, bbox. Optionally, one can provide word labels to the processor,
which are turned into token-level labels.
[LayoutLMv2Processor] uses PyTesseract, a Python
wrapper around Google's Tesseract OCR engine, under the hood. Note that you can still use your own OCR engine of
choice, and provide the words and normalized boxes yourself. This requires initializing
[LayoutLMv2ImageProcessor] with apply_ocr set to False.
In total, there are 5 use cases that are supported by the processor. Below, we list them all. Note that each of these
use cases work for both batched and non-batched inputs (we illustrate them for non-batched inputs).
Use case 1: document image classification (training, inference) + token classification (inference), apply_ocr =
True
This is the simplest case, in which the processor (actually the image processor) will perform OCR on the image to get
the words and normalized bounding boxes.
thon
from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained(""microsoft/layoutlmv2-base-uncased"")
image = Image.open(
    ""name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images).""
).convert(""RGB"")
encoding = processor(
    image, return_tensors=""pt""
)  # you can also add all tokenizer parameters here such as padding, truncation
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])

Use case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False
In case one wants to do OCR themselves, one can initialize the image processor with apply_ocr set to
False. In that case, one should provide the words and corresponding (normalized) bounding boxes themselves to
the processor.
thon
from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained(""microsoft/layoutlmv2-base-uncased"", revision=""no_ocr"")
image = Image.open(
    ""name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images).""
).convert(""RGB"")
words = [""hello"", ""world""]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
encoding = processor(image, words, boxes=boxes, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])

Use case 3: token classification (training), apply_ocr=False
For token classification tasks (such as FUNSD, CORD, SROIE, Kleister-NDA), one can also provide the corresponding word
labels in order to train a model. The processor will then convert these into token-level labels. By default, it
will only label the first wordpiece of a word, and label the remaining wordpieces with -100, which is the
ignore_index of PyTorch's CrossEntropyLoss. In case you want all wordpieces of a word to be labeled, you can
initialize the tokenizer with only_label_first_subword set to False.
thon
from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained(""microsoft/layoutlmv2-base-uncased"", revision=""no_ocr"")
image = Image.open(
    ""name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images).""
).convert(""RGB"")
words = [""hello"", ""world""]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
word_labels = [1, 2]
encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'labels', 'image'])

Use case 4: visual question answering (inference), apply_ocr=True
For visual question answering tasks (such as DocVQA), you can provide a question to the processor. By default, the
processor will apply OCR on the image, and create [CLS] question tokens [SEP] word tokens [SEP].
thon
from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained(""microsoft/layoutlmv2-base-uncased"")
image = Image.open(
    ""name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images).""
).convert(""RGB"")
question = ""What's his name?""
encoding = processor(image, question, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])

Use case 5: visual question answering (inference), apply_ocr=False
For visual question answering tasks (such as DocVQA), you can provide a question to the processor. If you want to
perform OCR yourself, you can provide your own words and (normalized) bounding boxes to the processor.
thon
from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained(""microsoft/layoutlmv2-base-uncased"", revision=""no_ocr"")
image = Image.open(
    ""name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images).""
).convert(""RGB"")
question = ""What's his name?""
words = [""hello"", ""world""]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
encoding = processor(image, question, words, boxes=boxes, return_tensors=""pt"")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])

LayoutLMv2Config
[[autodoc]] LayoutLMv2Config
LayoutLMv2FeatureExtractor
[[autodoc]] LayoutLMv2FeatureExtractor
    - call
LayoutLMv2ImageProcessor
[[autodoc]] LayoutLMv2ImageProcessor
    - preprocess
LayoutLMv2Tokenizer
[[autodoc]] LayoutLMv2Tokenizer
    - call
    - save_vocabulary
LayoutLMv2TokenizerFast
[[autodoc]] LayoutLMv2TokenizerFast
    - call
LayoutLMv2Processor
[[autodoc]] LayoutLMv2Processor
    - call
LayoutLMv2Model
[[autodoc]] LayoutLMv2Model
    - forward
LayoutLMv2ForSequenceClassification
[[autodoc]] LayoutLMv2ForSequenceClassification
LayoutLMv2ForTokenClassification
[[autodoc]] LayoutLMv2ForTokenClassification
LayoutLMv2ForQuestionAnswering
[[autodoc]] LayoutLMv2ForQuestionAnswering"
"
GIT
Overview
The GIT model was proposed in GIT: A Generative Image-to-text Transformer for Vision and Language by
Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang. GIT is a decoder-only Transformer
that leverages CLIP's vision encoder to condition the model on vision inputs besides text. The model obtains state-of-the-art results on
image captioning and visual question answering benchmarks.
The abstract from the paper is the following:
In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and depends on external modules such as object detectors/taggers and optical character recognition (OCR). In GIT, we simplify the architecture as one image encoder and one text decoder under a single language modeling task. We also scale up the pre-training data and the model size to boost the model performance. Without bells and whistles, our GIT establishes new state of the arts on 12 challenging benchmarks with a large margin. For instance, our model surpasses the human performance for the first time on TextCaps (138.2 vs. 125.5 in CIDEr). Furthermore, we present a new scheme of generation-based image classification and scene text recognition, achieving decent performance on standard benchmarks.
Tips:

GIT is implemented in a very similar way to GPT-2, the only difference being that the model is also conditioned on pixel_values.
One can use [GitProcessor] to prepare images for the model, and the generate method for autoregressive generation.

 GIT architecture. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GIT.

Demo notebooks regarding inference + fine-tuning GIT on custom data can be found here.
See also: Causal language modeling task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.
GitVisionConfig
[[autodoc]] GitVisionConfig
GitVisionModel
[[autodoc]] GitVisionModel
    - forward
GitConfig
[[autodoc]] GitConfig
    - all
GitProcessor
[[autodoc]] GitProcessor
    - call
GitModel
[[autodoc]] GitModel
    - forward
GitForCausalLM
[[autodoc]] GitForCausalLM
    - forward"
"
FNet
Overview
The FNet model was proposed in FNet: Mixing Tokens with Fourier Transforms by
James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon. The model replaces the self-attention layer in a BERT
model with a fourier transform which returns only the real parts of the transform. The model is significantly faster
than the BERT model because it has fewer parameters and is more memory efficient. The model achieves about 92-97%
accuracy of BERT counterparts on GLUE benchmark, and trains much faster than the BERT model. The abstract from the
paper is the following:
We show that Transformer encoder architectures can be sped up, with limited accuracy costs, by replacing the
self-attention sublayers with simple linear transformations that ""mix"" input tokens. These linear mixers, along with
standard nonlinearities in feed-forward layers, prove competent at modeling semantic relationships in several text
classification tasks. Most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder
with a standard, unparameterized Fourier Transform achieves 92-97% of the accuracy of BERT counterparts on the GLUE
benchmark, but trains 80% faster on GPUs and 70% faster on TPUs at standard 512 input lengths. At longer input lengths,
our FNet model is significantly faster: when compared to the ""efficient"" Transformers on the Long Range Arena
benchmark, FNet matches the accuracy of the most accurate models, while outpacing the fastest models across all
sequence lengths on GPUs (and across relatively shorter lengths on TPUs). Finally, FNet has a light memory footprint
and is particularly efficient at smaller model sizes; for a fixed speed and accuracy budget, small FNet models
outperform Transformer counterparts.
Tips on usage:

The model was trained without an attention mask as it is based on Fourier Transform. The model was trained with
  maximum sequence length 512 which includes pad tokens. Hence, it is highly recommended to use the same maximum
  sequence length for fine-tuning and inference.

This model was contributed by gchhablani. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

FNetConfig
[[autodoc]] FNetConfig
FNetTokenizer
[[autodoc]] FNetTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
FNetTokenizerFast
[[autodoc]] FNetTokenizerFast
FNetModel
[[autodoc]] FNetModel
    - forward
FNetForPreTraining
[[autodoc]] FNetForPreTraining
    - forward
FNetForMaskedLM
[[autodoc]] FNetForMaskedLM
    - forward
FNetForNextSentencePrediction
[[autodoc]] FNetForNextSentencePrediction
    - forward
FNetForSequenceClassification
[[autodoc]] FNetForSequenceClassification
    - forward
FNetForMultipleChoice
[[autodoc]] FNetForMultipleChoice
    - forward
FNetForTokenClassification
[[autodoc]] FNetForTokenClassification
    - forward
FNetForQuestionAnswering
[[autodoc]] FNetForQuestionAnswering
    - forward"
"
ByT5
Overview
The ByT5 model was presented in ByT5: Towards a token-free future with pre-trained byte-to-byte models by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir
Kale, Adam Roberts, Colin Raffel.
The abstract from the paper is the following:
Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units.
Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from
the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they
can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by
removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token
sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of
operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with
minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count,
training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level
counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on
tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of
pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our
experiments.
This model was contributed by patrickvonplaten. The original code can be
found here.
ByT5's architecture is based on the T5v1.1 model, so one can refer to T5v1.1's documentation page. They
only differ in how inputs should be prepared for the model, see the code examples below.
Since ByT5 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.
Example
ByT5 works on raw UTF-8 bytes, so it can be used without a tokenizer:
thon

from transformers import T5ForConditionalGeneration
import torch
model = T5ForConditionalGeneration.from_pretrained(""google/byt5-small"")
num_special_tokens = 3
Model has 3 special tokens which take up the input ids 0,1,2 of ByT5.
=> Need to shift utf-8 character encodings by 3 before passing ids to model.
input_ids = torch.tensor([list(""Life is like a box of chocolates."".encode(""utf-8""))]) + num_special_tokens
labels = torch.tensor([list(""La vie est comme une boîte de chocolat."".encode(""utf-8""))]) + num_special_tokens
loss = model(input_ids, labels=labels).loss
loss.item()
2.66

For batched inference and training it is however recommended to make use of the tokenizer:
thon

from transformers import T5ForConditionalGeneration, AutoTokenizer
model = T5ForConditionalGeneration.from_pretrained(""google/byt5-small"")
tokenizer = AutoTokenizer.from_pretrained(""google/byt5-small"")
model_inputs = tokenizer(
     [""Life is like a box of chocolates."", ""Today is Monday.""], padding=""longest"", return_tensors=""pt""
 )
labels_dict = tokenizer(
     [""La vie est comme une boîte de chocolat."", ""Aujourd'hui c'est lundi.""], padding=""longest"", return_tensors=""pt""
 )
labels = labels_dict.input_ids
loss = model(**model_inputs, labels=labels).loss
loss.item()
17.9

Similar to T5, ByT5 was trained on the span-mask denoising task. However, 
since the model works directly on characters, the pretraining task is a bit 
different. Let's corrupt some characters of the 
input sentence ""The dog chases a ball in the park."" and ask ByT5 to predict them 
for us.
thon

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
tokenizer = AutoTokenizer.from_pretrained(""google/byt5-base"")
model = AutoModelForSeq2SeqLM.from_pretrained(""google/byt5-base"")
input_ids_prompt = ""The dog chases a ball in the park.""
input_ids = tokenizer(input_ids_prompt).input_ids
Note that we cannot add ""{extra_id_}"" to the string directly
as the Byte tokenizer would incorrectly merge the tokens
For ByT5, we need to work directly on the character level
Contrary to T5, ByT5 does not use sentinel tokens for masking, but instead
uses final utf character ids.
UTF-8 is represented by 8 bits and ByT5 has 3 special tokens.
=> There are 2**8+2 = 259 input ids and mask tokens count down from index 258.
=> mask to ""The dog [258]a ball [257]park.""
input_ids = torch.tensor([input_ids[:8] + [258] + input_ids[14:21] + [257] + input_ids[28:]])
input_ids
tensor([[ 87, 107, 104,  35, 103, 114, 106,  35, 258,  35, 100,  35, 101, 100, 111, 111, 257,  35, 115, 100, 117, 110,  49,   1]])
ByT5 produces only one char at a time so we need to produce many more output characters here -> set max_length=100.
output_ids = model.generate(input_ids, max_length=100)[0].tolist()
output_ids
[0, 258, 108, 118,  35, 119, 107, 104,  35, 114, 113, 104,  35, 122, 107, 114,  35, 103, 114, 104, 118, 257,  35, 108, 113,  35, 119, 107, 104,  35, 103, 108, 118, 102, 114, 256, 108, 113,  35, 119, 107, 104, 35, 115, 100, 117, 110,  49,  35,  87, 107, 104,  35, 103, 114, 106, 35, 108, 118,  35, 119, 107, 104,  35, 114, 113, 104,  35, 122, 107, 114,  35, 103, 114, 104, 118,  35, 100,  35, 101, 100, 111, 111,  35, 108, 113, 255,  35, 108, 113,  35, 119, 107, 104,  35, 115, 100, 117, 110,  49]
^- Note how 258 descends to 257, 256, 255
Now we need to split on the sentinel tokens, let's write a short loop for this
output_ids_list = []
start_token = 0
sentinel_token = 258
while sentinel_token in output_ids:
     split_idx = output_ids.index(sentinel_token)
     output_ids_list.append(output_ids[start_token:split_idx])
     start_token = split_idx
     sentinel_token -= 1
output_ids_list.append(output_ids[start_token:])
output_string = tokenizer.batch_decode(output_ids_list)
output_string
['', 'is the one who does', ' in the disco', 'in the park. The dog is the one who does a ball in', ' in the park.']

ByT5Tokenizer
[[autodoc]] ByT5Tokenizer
See [ByT5Tokenizer] for all details."
"
ResNet
Overview
The ResNet model was proposed in Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. Our implementation follows the small changes made by Nvidia, we apply the stride=2 for downsampling in bottleneck's 3x3 conv and not in the first 1x1. This is generally known as ""ResNet v1.5"".
ResNet introduced residual connections, they allow to train networks with an unseen number of layers (up to 1000). ResNet won the 2015 ILSVRC & COCO competition, one important milestone in deep computer vision.
The abstract from the paper is the following:
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.
The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Tips:

One can use [AutoImageProcessor] to prepare images for the model.

The figure below illustrates the architecture of ResNet. Taken from the original paper.

This model was contributed by Francesco. The TensorFlow version of this model was added by amyeroberts. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ResNet.

[ResNetForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ResNetConfig
[[autodoc]] ResNetConfig
ResNetModel
[[autodoc]] ResNetModel
    - forward
ResNetForImageClassification
[[autodoc]] ResNetForImageClassification
    - forward
TFResNetModel
[[autodoc]] TFResNetModel
    - call
TFResNetForImageClassification
[[autodoc]] TFResNetForImageClassification
    - call
FlaxResNetModel
[[autodoc]] FlaxResNetModel
    - call
FlaxResNetForImageClassification
[[autodoc]] FlaxResNetForImageClassification
    - call"
"
FLAN-UL2
Overview
Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the UL2 model released earlier last year. 
It was fine tuned using the ""Flan"" prompt tuning and dataset collection. Similiar to Flan-T5,  one can directly use FLAN-UL2 weights without finetuning the model:
According ot the original blog here are the notable improvements:

The original UL2 model was only trained with receptive field of 512, which made it non-ideal for N-shot prompting where N is large.
The Flan-UL2 checkpoint uses a receptive field of 2048 which makes it more usable for few-shot in-context learning.
The original UL2 model also had mode switch tokens that was rather mandatory to get good performance. However, they were a little cumbersome as this requires often some changes during inference or finetuning. In this update/change, we continue training UL2 20B for an additional 100k steps (with small batch) to forget “mode tokens” before applying Flan instruction tuning. This Flan-UL2 checkpoint does not require mode tokens anymore.
Google has released the following variants:

One can refer to T5's documentation page for all tips, code examples and notebooks. As well as the FLAN-T5 model card for more details regarding training and evaluation of the model.
The original checkpoints can be found here.
Running on low resource devices
The model is pretty heavy (~40GB in half precision) so if you just want to run the model, make sure you load your model in 8bit, and use device_map=""auto"" to make sure  you don't have any OOM issue!
thon

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(""google/flan-ul2"", load_in_8bit=True, device_map=""auto"")
tokenizer = AutoTokenizer.from_pretrained(""google/flan-ul2"")
inputs = tokenizer(""A step by step recipe to make bolognese pasta:"", return_tensors=""pt"")
outputs = model.generate(**inputs)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['In a large skillet, brown the ground beef and onion over medium heat. Add the garlic']

Inference
The inference protocol is exaclty the same as any T5 model, please have a look at the T5's documentation page for more details."
"
RoCBert
Overview
The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining  by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.
The abstract from the paper is the following:
Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown
vulnerable to adversarial attacks especially for logographic languages like Chinese. In this work, we propose
ROCBERT: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation,
synonyms, typos, etc. It is pretrained with the contrastive learning objective which maximizes the label consistency
under different synthesized adversarial examples. The model takes as input multimodal information including the
semantic, phonetic and visual features. We show all these features are important to the model robustness since the
attack can be performed in all the three forms. Across 5 Chinese NLU tasks, ROCBERT outperforms strong baselines under
three blackbox adversarial algorithms without sacrificing the performance on clean testset. It also performs the best
in the toxic content detection task under human-made attacks.
This model was contributed by weiweishi.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

RoCBertConfig
[[autodoc]] RoCBertConfig
    - all
RoCBertTokenizer
[[autodoc]] RoCBertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
RoCBertModel
[[autodoc]] RoCBertModel
    - forward
RoCBertForPreTraining
[[autodoc]] RoCBertForPreTraining
    - forward
RoCBertForCausalLM
[[autodoc]] RoCBertForCausalLM
    - forward
RoCBertForMaskedLM
[[autodoc]] RoCBertForMaskedLM
    - forward
RoCBertForSequenceClassification
[[autodoc]] transformers.RoCBertForSequenceClassification
    - forward
RoCBertForMultipleChoice
[[autodoc]] transformers.RoCBertForMultipleChoice
    - forward
RoCBertForTokenClassification
[[autodoc]] transformers.RoCBertForTokenClassification
    - forward
RoCBertForQuestionAnswering
[[autodoc]] RoCBertForQuestionAnswering
    - forward"
"
BART

DISCLAIMER: If you see something strange, file a Github Issue and assign
@patrickvonplaten
Overview
The Bart model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation,
Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan
Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.
According to the abstract,

Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a
  left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme,
  where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It
  matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new
  state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains
  of up to 6 ROUGE.

Tips:

BART is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.

Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). A composition of the following transformations are applied on the pretraining tasks for the encoder:

mask random tokens (like in BERT)

delete random tokens
mask a span of k tokens with a single mask token (a span of 0 tokens is an insertion of a mask token)
permute sentences
rotate the document to make it start at a specific token

This model was contributed by sshleifer. The Authors' code can be found here.
Examples

Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in
  examples/pytorch/summarization/.
An example of how to train [BartForConditionalGeneration] with a Hugging Face datasets
  object can be found in this forum discussion.
Distilled checkpoints are described in this paper.

Implementation Notes

Bart doesn't use token_type_ids for sequence classification. Use [BartTokenizer] or
  [~BartTokenizer.encode] to get the proper splitting.
The forward pass of [BartModel] will create the decoder_input_ids if they are not passed.
  This is different than some other modeling APIs. A typical use case of this feature is mask filling.
Model predictions are intended to be identical to the original implementation when
  forced_bos_token_id=0. This only works, however, if the string you pass to
  [fairseq.encode] starts with a space.
[~generation.GenerationMixin.generate] should be used for conditional generation tasks like
  summarization, see the example in that docstrings.
Models that load the facebook/bart-large-cnn weights will not have a mask_token_id, or be able to perform
  mask-filling tasks.

Mask Filling
The facebook/bart-base and facebook/bart-large checkpoints can be used to fill multi-token masks.
thon
from transformers import BartForConditionalGeneration, BartTokenizer
model = BartForConditionalGeneration.from_pretrained(""facebook/bart-large"", forced_bos_token_id=0)
tok = BartTokenizer.from_pretrained(""facebook/bart-large"")
example_english_phrase = ""UN Chief Says There Is No  in Syria""
batch = tok(example_english_phrase, return_tensors=""pt"")
generated_ids = model.generate(batch[""input_ids""])
assert tok.batch_decode(generated_ids, skip_special_tokens=True) == [
    ""UN Chief Says There Is No Plan to Stop Chemical Weapons in Syria""
]

Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BART. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog post on Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker.
A notebook on how to finetune BART for summarization with fastai using blurr. 🌎
A notebook on how to finetune BART for summarization in two languages with Trainer class. 🌎
[BartForConditionalGeneration] is supported by this example script and notebook.
[TFBartForConditionalGeneration] is supported by this example script and notebook.
[FlaxBartForConditionalGeneration] is supported by this example script.
Summarization chapter of the 🤗 Hugging Face course.
Summarization task guide

[BartForConditionalGeneration] is supported by this example script and notebook.
[TFBartForConditionalGeneration] is supported by this example script and notebook.
[FlaxBartForConditionalGeneration] is supported by this example script and notebook.
Masked language modeling chapter of the 🤗 Hugging Face Course.
Masked language modeling task guide

A notebook on how to finetune mBART using Seq2SeqTrainer for Hindi to English translation. 🌎
[BartForConditionalGeneration] is supported by this example script and notebook.
[TFBartForConditionalGeneration] is supported by this example script and notebook.
Translation task guide

See also:
- Text classification task guide
- Question answering task guide
- Causal language modeling task guide
BartConfig
[[autodoc]] BartConfig
    - all
BartTokenizer
[[autodoc]] BartTokenizer
    - all
BartTokenizerFast
[[autodoc]] BartTokenizerFast
    - all
BartModel
[[autodoc]] BartModel
    - forward
BartForConditionalGeneration
[[autodoc]] BartForConditionalGeneration
    - forward
BartForSequenceClassification
[[autodoc]] BartForSequenceClassification
    - forward
BartForQuestionAnswering
[[autodoc]] BartForQuestionAnswering
    - forward
BartForCausalLM
[[autodoc]] BartForCausalLM
    - forward
TFBartModel
[[autodoc]] TFBartModel
    - call
TFBartForConditionalGeneration
[[autodoc]] TFBartForConditionalGeneration
    - call
TFBartForSequenceClassification
[[autodoc]] TFBartForSequenceClassification
    - call
FlaxBartModel
[[autodoc]] FlaxBartModel
    - call
    - encode
    - decode
FlaxBartForConditionalGeneration
[[autodoc]] FlaxBartForConditionalGeneration
    - call
    - encode
    - decode
FlaxBartForSequenceClassification
[[autodoc]] FlaxBartForSequenceClassification
    - call
    - encode
    - decode
FlaxBartForQuestionAnswering
[[autodoc]] FlaxBartForQuestionAnswering
    - call
    - encode
    - decode
FlaxBartForCausalLM
[[autodoc]] FlaxBartForCausalLM
    - call"
"
UL2
Overview
The T5 model was presented in Unifying Language Learning Paradigms by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler.
The abstract from the paper is the following:
Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setups. We begin by disentangling architectural archetypes with pre-training objectives -- two concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. We furthermore introduce a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. We conduct extensive ablative experiments to compare multiple pre-training objectives and find that our method pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across multiple diverse setups. Finally, by scaling our model up to 20B parameters, we achieve SOTA performance on 50 well-established supervised NLP tasks ranging from language generation (with automated and human evaluation), language understanding, text classification, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
Tips:

UL2 is an encoder-decoder model pre-trained on a mixture of denoising functions as well as fine-tuned on an array of downstream tasks.
UL2 has the same architecture as T5v1.1 but uses the Gated-SiLU activation function instead of Gated-GELU.
The authors release checkpoints of one architecture which can be seen here

The original code can be found here.
This model was contributed by DanielHesslow."
"
ESM
Overview
This page provides code and pre-trained weights for Transformer protein language models from Meta AI's Fundamental 
AI Research Team, providing the state-of-the-art ESMFold and ESM-2, and the previously released ESM-1b and ESM-1v.
Transformer protein language models were introduced in the paper Biological structure and function emerge from scaling
unsupervised learning to 250 million protein sequences by 
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, 
C. Lawrence Zitnick, Jerry Ma, and Rob Fergus.
The first version of this paper was preprinted in 2019.
ESM-2 outperforms all tested single-sequence protein language models across a range of structure prediction tasks,
and enables atomic resolution structure prediction.
It was released with the paper Language models of protein sequences at the scale of evolution enable accurate
structure prediction by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie,
Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido and Alexander Rives.
Also introduced in this paper was ESMFold. It uses an ESM-2 stem with a head that can predict folded protein
structures with state-of-the-art accuracy. Unlike AlphaFold2,
it relies on the token embeddings from the large pre-trained protein language model stem and does not perform a multiple
sequence alignment (MSA) step at inference time, which means that ESMFold checkpoints are fully ""standalone"" -
they do not require a database of known protein sequences and structures with associated external query tools
to make predictions, and are much faster as a result.
The abstract from 
""Biological structure and function emerge from scaling unsupervised learning to 250 
million protein sequences"" is
In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised
learning has led to major advances in representation learning and statistical generation. In the life sciences, the
anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling
at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To
this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250
million protein sequences spanning evolutionary diversity. The resulting model contains information about biological
properties in its representations. The representations are learned from sequence data alone. The learned representation
space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to
remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and
can be identified by linear projections. Representation learning produces features that generalize across a range of
applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and
improving state-of-the-art features for long-range contact prediction.
The abstract from
""Language models of protein sequences at the scale of evolution enable accurate structure prediction"" is
Large language models have recently been shown to develop emergent capabilities with scale, going beyond
simple pattern matching to perform higher level reasoning and generate lifelike images and text. While
language models trained on protein sequences have been studied at a smaller scale, little is known about
what they learn about biology as they are scaled up. In this work we train models up to 15 billion parameters,
the largest language models of proteins to be evaluated to date. We find that as models are scaled they learn
information enabling the prediction of the three-dimensional structure of a protein at the resolution of
individual atoms. We present ESMFold for high accuracy end-to-end atomic level structure prediction directly
from the individual sequence of a protein. ESMFold has similar accuracy to AlphaFold2 and RoseTTAFold for
sequences with low perplexity that are well understood by the language model. ESMFold inference is an
order of magnitude faster than AlphaFold2, enabling exploration of the structural space of metagenomic
proteins in practical timescales.
Tips:

ESM models are trained with a masked language modeling (MLM) objective.

The original code can be found here and was
was developed by the Fundamental AI Research team at Meta AI.
ESM-1b, ESM-1v and ESM-2 were contributed to huggingface by jasonliu
and Matt.
ESMFold was contributed to huggingface by Matt and
Sylvain, with a big thank you to Nikita Smetanin, Roshan Rao and Tom Sercu for their
help throughout the process!
The HuggingFace port of ESMFold uses portions of the openfold library.
The openfold library is licensed under the Apache License 2.0.
Documentation resources

Text classification task guide
Token classification task guide
Masked language modeling task guide

EsmConfig
[[autodoc]] EsmConfig
    - all
EsmTokenizer
[[autodoc]] EsmTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
EsmModel
[[autodoc]] EsmModel
    - forward
EsmForMaskedLM
[[autodoc]] EsmForMaskedLM
    - forward
EsmForSequenceClassification
[[autodoc]] EsmForSequenceClassification
    - forward
EsmForTokenClassification
[[autodoc]] EsmForTokenClassification
    - forward
EsmForProteinFolding
[[autodoc]] EsmForProteinFolding
    - forward
TFEsmModel
[[autodoc]] TFEsmModel
    - call
TFEsmForMaskedLM
[[autodoc]] TFEsmForMaskedLM
    - call
TFEsmForSequenceClassification
[[autodoc]] TFEsmForSequenceClassification
    - call
TFEsmForTokenClassification
[[autodoc]] TFEsmForTokenClassification
    - call"
"
Neighborhood Attention Transformer
Overview
NAT was proposed in Neighborhood Attention Transformer
by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern.
The abstract from the paper is the following:
*We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision.
NA is a pixel-wise operation, localizing self attention (SA) to the nearest neighboring pixels, and therefore enjoys a
linear time and space complexity compared to the quadratic complexity of SA. The sliding-window pattern allows NA's
receptive field to grow without needing extra pixel shifts, and preserves translational equivariance, unlike
Swin Transformer's Window Self Attention (WSA). We develop NATTEN (Neighborhood Attention Extension), a Python package
with efficient C++ and CUDA kernels, which allows NA to run up to 40% faster than Swin's WSA while using up to 25% less
memory. We further present Neighborhood Attention Transformer (NAT), a new hierarchical transformer design based on NA
that boosts image classification and downstream vision performance. Experimental results on NAT are competitive;
NAT-Tiny reaches 83.2% top-1 accuracy on ImageNet, 51.4% mAP on MS-COCO and 48.4% mIoU on ADE20K, which is 1.9%
ImageNet accuracy, 1.0% COCO mAP, and 2.6% ADE20K mIoU improvement over a Swin model with similar size. *
Tips:
- One can use the [AutoImageProcessor] API to prepare images for the model.
- NAT can be used as a backbone. When output_hidden_states = True,
it will output both hidden_states and reshaped_hidden_states.
The reshaped_hidden_states have a shape of (batch, num_channels, height, width) rather than
(batch_size, height, width, num_channels).
Notes:
- NAT depends on NATTEN's implementation of Neighborhood Attention.
You can install it with pre-built wheels for Linux by referring to shi-labs.com/natten,
or build on your system by running pip install natten.
Note that the latter will likely take time to compile. NATTEN does not support Windows devices yet.
- Patch size of 4 is only supported at the moment.

 Neighborhood Attention compared to other attention patterns.
Taken from the original paper.
This model was contributed by Ali Hassani.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with NAT.

[NatForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
NatConfig
[[autodoc]] NatConfig
NatModel
[[autodoc]] NatModel
    - forward
NatForImageClassification
[[autodoc]] NatForImageClassification
    - forward"
"
PoolFormer
Overview
The PoolFormer model was proposed in MetaFormer is Actually What You Need for Vision  by Sea AI Labs. Instead of designing complicated token mixer to achieve SOTA performance, the target of this work is to demonstrate the competence of transformer models largely stem from the general architecture MetaFormer.
The abstract from the paper is the following:
Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of ""MetaFormer"", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design.
The figure below illustrates the architecture of PoolFormer. Taken from the original paper.

Tips:

PoolFormer has a hierarchical architecture, where instead of Attention, a simple Average Pooling layer is present. All checkpoints of the model can be found on the hub.
One can use [PoolFormerImageProcessor] to prepare images for the model.
As most models, PoolFormer comes in different sizes, the details of which can be found in the table below.

| Model variant | Depths    | Hidden sizes    | Params (M) | ImageNet-1k Top 1 |
| :---------------: | ------------- | ------------------- | :------------: | :-------------------: |
| s12               | [2, 2, 6, 2]  | [64, 128, 320, 512] | 12             | 77.2                  |
| s24               | [4, 4, 12, 4] | [64, 128, 320, 512] | 21             | 80.3                  |
| s36               | [6, 6, 18, 6] | [64, 128, 320, 512] | 31             | 81.4                  |
| m36               | [6, 6, 18, 6] | [96, 192, 384, 768] | 56             | 82.1                  |
| m48               | [8, 8, 24, 8] | [96, 192, 384, 768] | 73             | 82.5                  |
This model was contributed by heytanay. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with PoolFormer.

[PoolFormerForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
PoolFormerConfig
[[autodoc]] PoolFormerConfig
PoolFormerFeatureExtractor
[[autodoc]] PoolFormerFeatureExtractor
    - call
PoolFormerImageProcessor
[[autodoc]] PoolFormerImageProcessor
    - preprocess
PoolFormerModel
[[autodoc]] PoolFormerModel
    - forward
PoolFormerForImageClassification
[[autodoc]] PoolFormerForImageClassification
    - forward"
"
UMT5

Overview
The UMT5 model was proposed in UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.
The abstract from the paper is the following:
Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.
Tips: 

UMT5 was only pre-trained on mC4 excluding any supervised training.
Therefore, this model has to be fine-tuned before it is usable on a downstream task, unlike the original T5 model.
Since umT5 was pre-trained in an unsupervise manner, there's no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.

Google has released the following variants:

google/umt5-small
google/umt5-base
google/umt5-xl
google/umt5-xxl.

This model was contributed by agemagician and stefan-it. The original code can be
found here.
One can refer to T5's documentation page for more tips, code examples and notebooks.
Differences with mT5?
UmT5 is based on mT5, with a non-shared relative positional bias that is computed for each layer. This means that the model set has_relative_bias for each layer.
The conversion script is also different because the model was saved in t5x's latest checkpointing format.
Sample usage
thon

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(""google/umt5-small"")
tokenizer = AutoTokenizer.from_pretrained(""google/umt5-small"")
inputs = tokenizer(
     ""A  walks into a bar and orders a  with  pinch of ."",
     return_tensors=""pt"",
 )
outputs = model.generate(**inputs)
print(tokenizer.batch_decode(outputs))
['nyone who drink a alcohol A A. This I']

UMT5Config
[[autodoc]] UMT5Config
UMT5Model
[[autodoc]] UMT5Model
    - forward
UMT5ForConditionalGeneration
[[autodoc]] UMT5ForConditionalGeneration
    - forward
UMT5EncoderModel
[[autodoc]] UMT5EncoderModel
    - forward
UMT5ForQuestionAnswering
[[autodoc]] UMT5ForQuestionAnswering
    - forward"
"
M2M100
Overview
The M2M100 model was proposed in Beyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky,
Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy
Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
The abstract from the paper is the following:
Existing work in translation demonstrated the potential of massively multilingual machine translation by training a
single model able to translate between any pair of languages. However, much of this work is English-Centric by training
only on data which was translated from or to English. While this is supported by large sources of training data, it
does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation
model that can translate directly between any pair of 100 languages. We build and open source a training dataset that
covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how
to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters
to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly
translating between non-English directions while performing competitively to the best single systems of WMT. We
open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model.
This model was contributed by valhalla.
Training and Generation
M2M100 is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation tasks. As the model is
multilingual it expects the sequences in a certain format: A special language id token is used as prefix in both the
source and target text. The source text format is [lang_code] X [eos], where lang_code is source language
id for source text and target language id for target text, with X being the source or target text.
The [M2M100Tokenizer] depends on sentencepiece so be sure to install it before running the
examples. To install sentencepiece run pip install sentencepiece.

Supervised Training

thon
from transformers import M2M100Config, M2M100ForConditionalGeneration, M2M100Tokenizer
model = M2M100ForConditionalGeneration.from_pretrained(""facebook/m2m100_418M"")
tokenizer = M2M100Tokenizer.from_pretrained(""facebook/m2m100_418M"", src_lang=""en"", tgt_lang=""fr"")
src_text = ""Life is like a box of chocolates.""
tgt_text = ""La vie est comme une boîte de chocolat.""
model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors=""pt"")
loss = model(**model_inputs).loss  # forward pass

Generation

M2M100 uses the eos_token_id as the decoder_start_token_id for generation with the target language id
  being forced as the first generated token. To force the target language id as the first generated token, pass the
  forced_bos_token_id parameter to the generate method. The following example shows how to translate between
  Hindi to French and Chinese to English using the facebook/m2m100_418M checkpoint.
thon

from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
hi_text = ""जीवन एक चॉकलेट बॉक्स की तरह है।""
chinese_text = ""生活就像一盒巧克力。""
model = M2M100ForConditionalGeneration.from_pretrained(""facebook/m2m100_418M"")
tokenizer = M2M100Tokenizer.from_pretrained(""facebook/m2m100_418M"")
translate Hindi to French
tokenizer.src_lang = ""hi""
encoded_hi = tokenizer(hi_text, return_tensors=""pt"")
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id(""fr""))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
""La vie est comme une boîte de chocolat.""
translate Chinese to English
tokenizer.src_lang = ""zh""
encoded_zh = tokenizer(chinese_text, return_tensors=""pt"")
generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id(""en""))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
""Life is like a box of chocolate.""

Documentation resources

Translation task guide
Summarization task guide

M2M100Config
[[autodoc]] M2M100Config
M2M100Tokenizer
[[autodoc]] M2M100Tokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
M2M100Model
[[autodoc]] M2M100Model
    - forward
M2M100ForConditionalGeneration
[[autodoc]] M2M100ForConditionalGeneration
    - forward"
"
SegFormer
Overview
The SegFormer model was proposed in SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping
Luo. The model consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great
results on image segmentation benchmarks such as ADE20K and Cityscapes.
The abstract from the paper is the following:
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with
lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel
hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding,
thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution
differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from
different layers, and thus combining both local attention and global attention to render powerful representations. We
show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our
approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance
and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters,
being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on
Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C.
The figure below illustrates the architecture of SegFormer. Taken from the original paper.

This model was contributed by nielsr. The TensorFlow version
of the model was contributed by sayakpaul. The original code can be found here.
Tips:

SegFormer consists of a hierarchical Transformer encoder, and a lightweight all-MLP decoder head.
  [SegformerModel] is the hierarchical Transformer encoder (which in the paper is also referred to
  as Mix Transformer or MiT). [SegformerForSemanticSegmentation] adds the all-MLP decoder head on
  top to perform semantic segmentation of images. In addition, there's
  [SegformerForImageClassification] which can be used to - you guessed it - classify images. The
  authors of SegFormer first pre-trained the Transformer encoder on ImageNet-1k to classify images. Next, they throw
  away the classification head, and replace it by the all-MLP decode head. Next, they fine-tune the model altogether on
  ADE20K, Cityscapes and COCO-stuff, which are important benchmarks for semantic segmentation. All checkpoints can be
  found on the hub.
The quickest way to get started with SegFormer is by checking the example notebooks (which showcase both inference and
  fine-tuning on custom data). One can also check out the blog post introducing SegFormer and illustrating how it can be fine-tuned on custom data.
TensorFlow users should refer to this repository that shows off-the-shelf inference and fine-tuning.
One can also check out this interactive demo on Hugging Face Spaces
  to try out a SegFormer model on custom images.
SegFormer works on any input size, as it pads the input to be divisible by config.patch_sizes.
One can use [SegformerImageProcessor] to prepare images and corresponding segmentation maps
  for the model. Note that this image processor is fairly basic and does not include all data augmentations used in
  the original paper. The original preprocessing pipelines (for the ADE20k dataset for instance) can be found here. The most
  important preprocessing step is that images and segmentation maps are randomly cropped and padded to the same size,
  such as 512x512 or 640x640, after which they are normalized.
One additional thing to keep in mind is that one can initialize [SegformerImageProcessor] with
  reduce_labels set to True or False. In some datasets (like ADE20k), the 0 index is used in the annotated
  segmentation maps for background. However, ADE20k doesn't include the ""background"" class in its 150 labels.
  Therefore, reduce_labels is used to reduce all labels by 1, and to make sure no loss is computed for the
  background class (i.e. it replaces 0 in the annotated maps by 255, which is the ignore_index of the loss function
  used by [SegformerForSemanticSegmentation]). However, other datasets use the 0 index as
  background class and include this class as part of all labels. In that case, reduce_labels should be set to
  False, as loss should also be computed for the background class.
As most models, SegFormer comes in different sizes, the details of which can be found in the table below
  (taken from Table 7 of the original paper).

| Model variant | Depths    | Hidden sizes    | Decoder hidden size | Params (M) | ImageNet-1k Top 1 |
| :---------------: | ------------- | ------------------- | :---------------------: | :------------: | :-------------------: |
| MiT-b0            | [2, 2, 2, 2]  | [32, 64, 160, 256]  | 256                     | 3.7            | 70.5                  |
| MiT-b1            | [2, 2, 2, 2]  | [64, 128, 320, 512] | 256                     | 14.0           | 78.7                  |
| MiT-b2            | [3, 4, 6, 3]  | [64, 128, 320, 512] | 768                     | 25.4           | 81.6                  |
| MiT-b3            | [3, 4, 18, 3] | [64, 128, 320, 512] | 768                     | 45.2           | 83.1                  |
| MiT-b4            | [3, 8, 27, 3] | [64, 128, 320, 512] | 768                     | 62.6           | 83.6                  |
| MiT-b5            | [3, 6, 40, 3] | [64, 128, 320, 512] | 768                     | 82.0           | 83.8                  |
Note that MiT in the above table refers to the Mix Transformer encoder backbone introduced in SegFormer. For
SegFormer's results on the segmentation datasets like ADE20k, refer to the paper.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SegFormer.

[SegformerForImageClassification] is supported by this example script and notebook.
Image classification task guide

Semantic segmentation:

[SegformerForSemanticSegmentation] is supported by this example script.
A blog on fine-tuning SegFormer on a custom dataset can be found here.
More demo notebooks on SegFormer (both inference + fine-tuning on a custom dataset) can be found here.
[TFSegformerForSemanticSegmentation] is supported by this example notebook.
Semantic segmentation task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
SegformerConfig
[[autodoc]] SegformerConfig
SegformerFeatureExtractor
[[autodoc]] SegformerFeatureExtractor
    - call
    - post_process_semantic_segmentation
SegformerImageProcessor
[[autodoc]] SegformerImageProcessor
    - preprocess
    - post_process_semantic_segmentation
SegformerModel
[[autodoc]] SegformerModel
    - forward
SegformerDecodeHead
[[autodoc]] SegformerDecodeHead
    - forward
SegformerForImageClassification
[[autodoc]] SegformerForImageClassification
    - forward
SegformerForSemanticSegmentation
[[autodoc]] SegformerForSemanticSegmentation
    - forward
TFSegformerDecodeHead
[[autodoc]] TFSegformerDecodeHead
    - call
TFSegformerModel
[[autodoc]] TFSegformerModel
    - call
TFSegformerForImageClassification
[[autodoc]] TFSegformerForImageClassification
    - call
TFSegformerForSemanticSegmentation
[[autodoc]] TFSegformerForSemanticSegmentation
    - call"
"
MRA
Overview
The MRA model was proposed in Multi Resolution Analysis (MRA) for Approximate Self-Attention by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, and Vikas Singh.
The abstract from the paper is the following:
Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix, a key module in a Transformer architecture. Effective ideas include various prespecified sparsity patterns, low-rank basis expansions and combinations thereof. In this paper, we revisit classical Multiresolution Analysis (MRA) concepts such as Wavelets, whose potential value in this setting remains underexplored thus far. We show that simple approximations based on empirical feedback and design choices informed by modern hardware and implementation challenges, eventually yield a MRA-based approach for self-attention with an excellent performance profile across most criteria of interest. We undertake an extensive set of experiments and demonstrate that this multi-resolution scheme outperforms most efficient self-attention proposals and is favorable for both short and long sequences. Code is available at https://github.com/mlpen/mra-attention.
This model was contributed by novice03.
The original code can be found here.
MraConfig
[[autodoc]] MraConfig
MraModel
[[autodoc]] MraModel
    - forward
MraForMaskedLM
[[autodoc]] MraForMaskedLM
    - forward
MraForSequenceClassification
[[autodoc]] MraForSequenceClassification
    - forward
MraForMultipleChoice
[[autodoc]] MraForMultipleChoice
    - forward
MraForTokenClassification
[[autodoc]] MraForTokenClassification
    - forward
MraForQuestionAnswering
[[autodoc]] MraForQuestionAnswering
    - forward"
"
SwiftFormer
Overview
The SwiftFormer model was proposed in SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
The SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.
The abstract from the paper is the following:
Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolution limits its use in real-time applications, especially for deployment on resource-constrained mobile devices. Although hybrid approaches have been proposed to combine the advantages of convolutions and self-attention for a better speed-accuracy trade-off, the expensive matrix multiplication operations in self-attention remain a bottleneck. In this work, we introduce a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations with linear element-wise multiplications. Our design shows that the key-value interaction can be replaced with a linear layer without sacrificing any accuracy. Unlike previous state-of-the-art methods, our efficient formulation of self-attention enables its usage at all stages of the network. Using our proposed efficient additive attention, we build a series of models called ""SwiftFormer"" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Our small variant achieves 78.5% top-1 ImageNet-1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2x faster compared to MobileViT-v2.
Tips:
    - One can use the [ViTImageProcessor] API to prepare images for the model.
This model was contributed by shehan97.
The original code can be found here.
SwiftFormerConfig
[[autodoc]] SwiftFormerConfig
SwiftFormerModel
[[autodoc]] SwiftFormerModel
    - forward
SwiftFormerForImageClassification
[[autodoc]] SwiftFormerForImageClassification
    - forward"
"
ImageGPT
Overview
The ImageGPT model was proposed in Generative Pretraining from Pixels by Mark
Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. ImageGPT (iGPT) is a GPT-2-like
model trained to predict the next pixel value, allowing for both unconditional and conditional image generation.
The abstract from the paper is the following:
Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models
can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels,
without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels,
we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and
low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide
ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pre-trained models. We are also
competitive with self-supervised benchmarks on ImageNet when substituting pixels for a VQVAE encoding, achieving 69.0%
top-1 accuracy on a linear probe of our features.

 Summary of the approach. Taken from the original paper. 
This model was contributed by nielsr, based on this issue. The original code can be found
here.
Tips:

ImageGPT is almost exactly the same as GPT-2, with the exception that a different activation
  function is used (namely ""quick gelu""), and the layer normalization layers don't mean center the inputs. ImageGPT
  also doesn't have tied input- and output embeddings.
As the time- and memory requirements of the attention mechanism of Transformers scales quadratically in the sequence
  length, the authors pre-trained ImageGPT on smaller input resolutions, such as 32x32 and 64x64. However, feeding a
  sequence of 32x32x3=3072 tokens from 0..255 into a Transformer is still prohibitively large. Therefore, the authors
  applied k-means clustering to the (R,G,B) pixel values with k=512. This way, we only have a 32*32 = 1024-long
  sequence, but now of integers in the range 0..511. So we are shrinking the sequence length at the cost of a bigger
  embedding matrix. In other words, the vocabulary size of ImageGPT is 512, + 1 for a special ""start of sentence"" (SOS)
  token, used at the beginning of every sequence. One can use [ImageGPTImageProcessor] to prepare
  images for the model.
Despite being pre-trained entirely unsupervised (i.e. without the use of any labels), ImageGPT produces fairly
  performant image features useful for downstream tasks, such as image classification. The authors showed that the
  features in the middle of the network are the most performant, and can be used as-is to train a linear model (such as
  a sklearn logistic regression model for example). This is also referred to as ""linear probing"". Features can be
  easily obtained by first forwarding the image through the model, then specifying output_hidden_states=True, and
  then average-pool the hidden states at whatever layer you like.
Alternatively, one can further fine-tune the entire model on a downstream dataset, similar to BERT. For this, you can
  use [ImageGPTForImageClassification].
ImageGPT comes in different sizes: there's ImageGPT-small, ImageGPT-medium and ImageGPT-large. The authors did also
  train an XL variant, which they didn't release. The differences in size are summarized in the following table:

| Model variant | Depths | Hidden sizes | Decoder hidden size | Params (M) | ImageNet-1k Top 1 |
|---|---|---|---|---|---|
| MiT-b0 | [2, 2, 2, 2] | [32, 64, 160, 256] | 256 | 3.7 | 70.5 |
| MiT-b1 | [2, 2, 2, 2] | [64, 128, 320, 512] | 256 | 14.0 | 78.7 |
| MiT-b2 | [3, 4, 6, 3] | [64, 128, 320, 512] | 768 | 25.4 | 81.6 |
| MiT-b3 | [3, 4, 18, 3] | [64, 128, 320, 512] | 768 | 45.2 | 83.1 |
| MiT-b4 | [3, 8, 27, 3] | [64, 128, 320, 512] | 768 | 62.6 | 83.6 |
| MiT-b5 | [3, 6, 40, 3] | [64, 128, 320, 512] | 768 | 82.0 | 83.8 |
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ImageGPT.

Demo notebooks for ImageGPT can be found here.
[ImageGPTForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ImageGPTConfig
[[autodoc]] ImageGPTConfig
ImageGPTFeatureExtractor
[[autodoc]] ImageGPTFeatureExtractor
- __call__

ImageGPTImageProcessor
[[autodoc]] ImageGPTImageProcessor
    - preprocess
ImageGPTModel
[[autodoc]] ImageGPTModel
- forward

ImageGPTForCausalImageModeling
[[autodoc]] ImageGPTForCausalImageModeling
- forward

ImageGPTForImageClassification
[[autodoc]] ImageGPTForImageClassification
- forward
"
"
Deformable DETR
Overview
The Deformable DETR model was proposed in Deformable DETR: Deformable Transformers for End-to-End Object Detection by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
Deformable DETR mitigates the slow convergence issues and limited feature spatial resolution of the original DETR by leveraging a new deformable attention module which only attends to a small set of key sampling points around a reference.
The abstract from the paper is the following:
DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach.
Tips:

One can use [DeformableDetrImageProcessor] to prepare images (and optional targets) for the model.
Training Deformable DETR is equivalent to training the original DETR model. See the resources section below for demo notebooks.

 Deformable DETR architecture. Taken from the original paper.
This model was contributed by nielsr. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Deformable DETR.

Demo notebooks regarding inference + fine-tuning on a custom dataset for [DeformableDetrForObjectDetection] can be found here.
See also: Object detection task guide.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
DeformableDetrImageProcessor
[[autodoc]] DeformableDetrImageProcessor
    - preprocess
    - post_process_object_detection
DeformableDetrFeatureExtractor
[[autodoc]] DeformableDetrFeatureExtractor
    - call
    - post_process_object_detection
DeformableDetrConfig
[[autodoc]] DeformableDetrConfig
DeformableDetrModel
[[autodoc]] DeformableDetrModel
    - forward
DeformableDetrForObjectDetection
[[autodoc]] DeformableDetrForObjectDetection
    - forward"
"
HerBERT
Overview
The HerBERT model was proposed in KLEJ: Comprehensive Benchmark for Polish Language Understanding by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and
Ireneusz Gawlik. It is a BERT-based Language Model trained on Polish Corpora using only MLM objective with dynamic
masking of whole words.
The abstract from the paper is the following:
In recent years, a series of Transformer-based models unlocked major improvements in general natural language
understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which
allow for a fair comparison of the proposed methods. However, such benchmarks are available only for a handful of
languages. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language
understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing
datasets for named entity recognition, question-answering, textual entailment, and others. We also introduce a new
sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and
promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and
applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language,
which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an
extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based
models.
Examples of use:
thon

from transformers import HerbertTokenizer, RobertaModel
tokenizer = HerbertTokenizer.from_pretrained(""allegro/herbert-klej-cased-tokenizer-v1"")
model = RobertaModel.from_pretrained(""allegro/herbert-klej-cased-v1"")
encoded_input = tokenizer.encode(""Kto ma lepszą sztukę, ma lepszy rząd – to jasne."", return_tensors=""pt"")
outputs = model(encoded_input)
HerBERT can also be loaded using AutoTokenizer and AutoModel:
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(""allegro/herbert-klej-cased-tokenizer-v1"")
model = AutoModel.from_pretrained(""allegro/herbert-klej-cased-v1"")

This model was contributed by rmroczkowski. The original code can be found
here.
HerbertTokenizer
[[autodoc]] HerbertTokenizer
HerbertTokenizerFast
[[autodoc]] HerbertTokenizerFast"
"
TAPEX

This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: pip install -U transformers==4.30.0.

Overview
The TAPEX model was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu,
Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. TAPEX pre-trains a BART model to solve synthetic SQL queries, after
which it can be fine-tuned to answer natural language questions related to tabular data, as well as performing table fact checking. 
TAPEX has been fine-tuned on several datasets: 
- SQA (Sequential Question Answering by Microsoft)
- WTQ (Wiki Table Questions by Stanford University)
- WikiSQL (by Salesforce)
- TabFact (by USCB NLP Lab).
The abstract from the paper is the following:
Recent progress in language model pre-training has achieved a great success via leveraging large-scale unstructured textual data. However, it is
still a challenge to apply pre-training on structured tabular data due to the absence of large-scale high-quality tabular data. In this paper, we
propose TAPEX to show that table pre-training can be achieved by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically
synthesizing executable SQL queries and their execution outputs. TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL
executor on the diverse, large-scale and high-quality synthetic corpus. We evaluate TAPEX on four benchmark datasets. Experimental results demonstrate that
TAPEX outperforms previous table pre-training approaches by a large margin and achieves new state-of-the-art results on all of them. This includes improvements
on the weakly-supervised WikiSQL denotation accuracy to 89.5% (+2.3%), the WikiTableQuestions denotation accuracy to 57.5% (+4.8%), the SQA denotation accuracy
to 74.5% (+3.5%), and the TabFact accuracy to 84.2% (+3.2%). To our knowledge, this is the first work to exploit table pre-training via synthetic executable programs
and to achieve new state-of-the-art results on various downstream tasks.
Tips:

TAPEX is a generative (seq2seq) model. One can directly plug in the weights of TAPEX into a BART model. 
TAPEX has checkpoints on the hub that are either pre-trained only, or fine-tuned on WTQ, SQA, WikiSQL and TabFact.
Sentences + tables are presented to the model as sentence + "" "" + linearized table. The linearized table has the following format: 
  col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : .
TAPEX has its own tokenizer, that allows to prepare all data for the model easily. One can pass Pandas DataFrames and strings to the tokenizer,
  and it will automatically create the input_ids and attention_mask (as shown in the usage examples below). 

Usage: inference
Below, we illustrate how to use TAPEX for table question answering. As one can see, one can directly plug in the weights of TAPEX into a BART model.
We use the Auto API, which will automatically instantiate the appropriate tokenizer ([TapexTokenizer]) and model ([BartForConditionalGeneration]) for us,
based on the configuration file of the checkpoint on the hub.
thon

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd
tokenizer = AutoTokenizer.from_pretrained(""microsoft/tapex-large-finetuned-wtq"")
model = AutoModelForSeq2SeqLM.from_pretrained(""microsoft/tapex-large-finetuned-wtq"")
prepare table + question
data = {""Actors"": [""Brad Pitt"", ""Leonardo Di Caprio"", ""George Clooney""], ""Number of movies"": [""87"", ""53"", ""69""]}
table = pd.DataFrame.from_dict(data)
question = ""how many movies does Leonardo Di Caprio have?""
encoding = tokenizer(table, question, return_tensors=""pt"")
let the model generate an answer autoregressively
outputs = model.generate(**encoding)
decode back to text
predicted_answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(predicted_answer)
53

Note that [TapexTokenizer] also supports batched inference. Hence, one can provide a batch of different tables/questions, or a batch of a single table
and multiple questions, or a batch of a single query and multiple tables. Let's illustrate this:
thon

prepare table + question
data = {""Actors"": [""Brad Pitt"", ""Leonardo Di Caprio"", ""George Clooney""], ""Number of movies"": [""87"", ""53"", ""69""]}
table = pd.DataFrame.from_dict(data)
questions = [
     ""how many movies does Leonardo Di Caprio have?"",
     ""which actor has 69 movies?"",
     ""what's the first name of the actor who has 87 movies?"",
 ]
encoding = tokenizer(table, questions, padding=True, return_tensors=""pt"")
let the model generate an answer autoregressively
outputs = model.generate(**encoding)
decode back to text
tokenizer.batch_decode(outputs, skip_special_tokens=True)
[' 53', ' george clooney', ' brad pitt']

In case one wants to do table verification (i.e. the task of determining whether a given sentence is supported or refuted by the contents
of a table), one can instantiate a [BartForSequenceClassification] model. TAPEX has checkpoints on the hub fine-tuned on TabFact, an important
benchmark for table fact checking (it achieves 84% accuracy). The code example below again leverages the Auto API.
thon

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(""microsoft/tapex-large-finetuned-tabfact"")
model = AutoModelForSequenceClassification.from_pretrained(""microsoft/tapex-large-finetuned-tabfact"")
prepare table + sentence
data = {""Actors"": [""Brad Pitt"", ""Leonardo Di Caprio"", ""George Clooney""], ""Number of movies"": [""87"", ""53"", ""69""]}
table = pd.DataFrame.from_dict(data)
sentence = ""George Clooney has 30 movies""
encoding = tokenizer(table, sentence, return_tensors=""pt"")
forward pass
outputs = model(**encoding)
print prediction
predicted_class_idx = outputs.logits[0].argmax(dim=0).item()
print(model.config.id2label[predicted_class_idx])
Refused

TapexTokenizer
[[autodoc]] TapexTokenizer
    - call
    - save_vocabulary"
"
MobileViT
Overview
The MobileViT model was proposed in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari. MobileViT introduces a new layer that replaces local processing in convolutions with global processing using transformers.
The abstract from the paper is the following:
Light-weight convolutional neural networks (CNNs) are the de-facto for mobile vision tasks. Their spatial inductive biases allow them to learn representations with fewer parameters across different vision tasks. However, these networks are spatially local. To learn global representations, self-attention-based vision trans-formers (ViTs) have been adopted. Unlike CNNs, ViTs are heavy-weight. In this paper, we ask the following question: is it possible to combine the strengths of CNNs and ViTs to build a light-weight and low latency network for mobile vision tasks? Towards this end, we introduce MobileViT, a light-weight and general-purpose vision transformer for mobile devices. MobileViT presents a different perspective for the global processing of information with transformers, i.e., transformers as convolutions. Our results show that MobileViT significantly outperforms CNN- and ViT-based networks across different tasks and datasets. On the ImageNet-1k dataset, MobileViT achieves top-1 accuracy of 78.4% with about 6 million parameters, which is 3.2% and 6.2% more accurate than MobileNetv3 (CNN-based) and DeIT (ViT-based) for a similar number of parameters. On the MS-COCO object detection task, MobileViT is 5.7% more accurate than MobileNetv3 for a similar number of parameters.
Tips:

MobileViT is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map. You can follow this tutorial for a lightweight introduction.
One can use [MobileViTImageProcessor] to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB).
The available image classification checkpoints are pre-trained on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
The segmentation model uses a DeepLabV3 head. The available semantic segmentation checkpoints are pre-trained on PASCAL VOC.
As the name suggests MobileViT was designed to be performant and efficient on mobile phones. The TensorFlow versions of the MobileViT models are fully compatible with TensorFlow Lite.

You can use the following code to convert a MobileViT checkpoint (be it image classification or semantic segmentation) to generate a
  TensorFlow Lite model:

from transformers import TFMobileViTForImageClassification
import tensorflow as tf
model_ckpt = ""apple/mobilevit-xx-small""
model = TFMobileViTForImageClassification.from_pretrained(model_ckpt)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
    tf.lite.OpsSet.TFLITE_BUILTINS,
    tf.lite.OpsSet.SELECT_TF_OPS,
]
tflite_model = converter.convert()
tflite_filename = model_ckpt.split(""/"")[-1] + "".tflite""
with open(tflite_filename, ""wb"") as f:
    f.write(tflite_model)

The resulting model will be just about an MB making it a good fit for mobile applications where resources and network
  bandwidth can be constrained.
This model was contributed by matthijs. The TensorFlow version of the model was contributed by sayakpaul. The original code and weights can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileViT.

[MobileViTForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

Semantic segmentation
- Semantic segmentation task guide
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
MobileViTConfig
[[autodoc]] MobileViTConfig
MobileViTFeatureExtractor
[[autodoc]] MobileViTFeatureExtractor
    - call
    - post_process_semantic_segmentation
MobileViTImageProcessor
[[autodoc]] MobileViTImageProcessor
    - preprocess
    - post_process_semantic_segmentation
MobileViTModel
[[autodoc]] MobileViTModel
    - forward
MobileViTForImageClassification
[[autodoc]] MobileViTForImageClassification
    - forward
MobileViTForSemanticSegmentation
[[autodoc]] MobileViTForSemanticSegmentation
    - forward
TFMobileViTModel
[[autodoc]] TFMobileViTModel
    - call
TFMobileViTForImageClassification
[[autodoc]] TFMobileViTForImageClassification
    - call
TFMobileViTForSemanticSegmentation
[[autodoc]] TFMobileViTForSemanticSegmentation
    - call"
"
SEW-D
Overview
SEW-D (Squeezed and Efficient Wav2Vec with Disentangled attention) was proposed in Performance-Efficiency Trade-offs
in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim,
Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
The abstract from the paper is the following:
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition
(ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance
and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a
pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a
variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x
inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference
time, SEW reduces word error rate by 25-50% across different model sizes.
Tips:

SEW-D is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
SEWDForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
  using [Wav2Vec2CTCTokenizer].

This model was contributed by anton-l.
Documentation resources

Audio classification task guide
Automatic speech recognition task guide

SEWDConfig
[[autodoc]] SEWDConfig
SEWDModel
[[autodoc]] SEWDModel
    - forward
SEWDForCTC
[[autodoc]] SEWDForCTC
    - forward
SEWDForSequenceClassification
[[autodoc]] SEWDForSequenceClassification
    - forward"
"
YOLOS
Overview
The YOLOS model was proposed in You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
YOLOS proposes to just leverage the plain Vision Transformer (ViT) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.
The abstract from the paper is the following:
Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS.
Tips:

One can use [YolosImageProcessor] for preparing images (and optional targets) for the model. Contrary to DETR, YOLOS doesn't require a pixel_mask to be created.

 YOLOS architecture. Taken from the original paper.
This model was contributed by nielsr. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with YOLOS.

All example notebooks illustrating inference + fine-tuning [YolosForObjectDetection] on a custom dataset can be found here.
See also: Object detection task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
YolosConfig
[[autodoc]] YolosConfig
YolosImageProcessor
[[autodoc]] YolosImageProcessor
    - preprocess
    - pad
    - post_process_object_detection
YolosFeatureExtractor
[[autodoc]] YolosFeatureExtractor
    - call
    - pad
    - post_process_object_detection
YolosModel
[[autodoc]] YolosModel
    - forward
YolosForObjectDetection
[[autodoc]] YolosForObjectDetection
    - forward"
"
PLBart
DISCLAIMER: If you see something strange, file a Github Issue and assign
@gchhablani.
Overview of PLBart
The PLBART model was proposed in Unified Pre-training for Program Understanding and Generation by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
This is a BART-like model which can be used to perform code-summarization, code-generation, and code-translation tasks. The pre-trained model plbart-base has been trained using multilingual denoising task
on Java, Python and English.
According to the abstract
Code summarization and generation empower conversion between programming language (PL) and natural language (NL),
while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, 
a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks.
PLBART is pre-trained on an extensive collection of Java and Python functions and associated NL text via denoising autoencoding.
Experiments on code summarization in the English language, code generation, and code translation in seven programming languages
show that PLBART outperforms or rivals state-of-the-art models. Moreover, experiments on discriminative tasks, e.g., program
repair, clone detection, and vulnerable code detection, demonstrate PLBART's effectiveness in program understanding.
Furthermore, analysis reveals that PLBART learns program syntax, style (e.g., identifier naming convention), logical flow
(e.g., if block inside an else block is equivalent to else if block) that are crucial to program semantics and thus excels
even with limited annotations.
This model was contributed by gchhablani. The Authors' code can be found here.
Training of PLBart
PLBart is a multilingual encoder-decoder (sequence-to-sequence) model primarily intended for code-to-text, text-to-code, code-to-code tasks. As the
model is multilingual it expects the sequences in a different format. A special language id token is added in both the
source and target text. The source text format is X [eos, src_lang_code] where X is the source text. The
target text format is [tgt_lang_code] X [eos]. bos is never used.
However, for fine-tuning, in some cases no language token is provided in cases where a single language is used. Please refer to the paper to learn more about this.
In cases where the language code is needed, the regular [~PLBartTokenizer.__call__] will encode source text format 
when you pass texts as the first argument or with the keyword argument text, and will encode target text format if
it's passed with the text_target keyword argument.

Supervised training

thon

from transformers import PLBartForConditionalGeneration, PLBartTokenizer
tokenizer = PLBartTokenizer.from_pretrained(""uclanlp/plbart-base"", src_lang=""en_XX"", tgt_lang=""python"")
example_python_phrase = ""def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""
expected_translation_english = ""Returns the maximum value of a b c.""
inputs = tokenizer(example_python_phrase, text_target=expected_translation_english, return_tensors=""pt"")
model(**inputs)

Generation

While generating the target text set the decoder_start_token_id to the target language id. The following
  example shows how to translate Python to English using the uclanlp/plbart-python-en_XX model.
thon

from transformers import PLBartForConditionalGeneration, PLBartTokenizer
tokenizer = PLBartTokenizer.from_pretrained(""uclanlp/plbart-python-en_XX"", src_lang=""python"", tgt_lang=""en_XX"")
example_python_phrase = ""def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""
inputs = tokenizer(example_python_phrase, return_tensors=""pt"")
model = PLBartForConditionalGeneration.from_pretrained(""uclanlp/plbart-python-en_XX"")
translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id[""en_XX""])
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
""Returns the maximum value of a b c.""

Documentation resources

Text classification task guide
Causal language modeling task guide
Translation task guide
Summarization task guide

PLBartConfig
[[autodoc]] PLBartConfig
PLBartTokenizer
[[autodoc]] PLBartTokenizer
    - build_inputs_with_special_tokens
PLBartModel
[[autodoc]] PLBartModel
    - forward
PLBartForConditionalGeneration
[[autodoc]] PLBartForConditionalGeneration
    - forward
PLBartForSequenceClassification
[[autodoc]] PLBartForSequenceClassification
    - forward
PLBartForCausalLM
[[autodoc]] PLBartForCausalLM
    - forward"
"
BERT

Overview
The BERT model was proposed in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a
bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence
prediction on a large corpus comprising the Toronto Book Corpus and Wikipedia.
The abstract from the paper is the following:
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations
from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional
representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result,
the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
for a wide range of tasks, such as question answering and language inference, without substantial task-specific
architecture modifications.
BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
Tips:

BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation.

Corrupts the inputs by using random masking, more precisely, during pretraining, a given percentage of tokens (usually 15%) is masked by:

a special mask token with probability 0.8
a random token different from the one masked with probability 0.1
the same token with probability 0.1

The model must predict the original sentence, but has a second objective: inputs are two sentences A and B (with a separation token in between). With probability 50%, the sentences are consecutive in the corpus, in the remaining 50% they are not related. The model has to predict if the sentences are consecutive or not.

This model was contributed by thomwolf. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BERT. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog post on BERT Text Classification in a different language.
A notebook for Finetuning BERT (and friends) for multi-label text classification.
A notebook on how to Finetune BERT for multi-label classification using PyTorch. 🌎
A notebook on how to warm-start an EncoderDecoder model with BERT for summarization.
[BertForSequenceClassification] is supported by this example script and notebook.
[TFBertForSequenceClassification] is supported by this example script and notebook.
[FlaxBertForSequenceClassification] is supported by this example script and notebook.
Text classification task guide

A blog post on how to use Hugging Face Transformers with Keras: Fine-tune a non-English BERT for Named Entity Recognition.
A notebook for Finetuning BERT for named-entity recognition using only the first wordpiece of each word in the word label during tokenization. To propagate the label of the word to all wordpieces, see this version of the notebook instead.
[BertForTokenClassification] is supported by this example script and notebook.
[TFBertForTokenClassification] is supported by this example script and notebook.
[FlaxBertForTokenClassification] is supported by this example script.
Token classification chapter of the 🤗 Hugging Face Course.
Token classification task guide

[BertForMaskedLM] is supported by this example script and notebook.
[TFBertForMaskedLM] is supported by this example script and notebook.
[FlaxBertForMaskedLM] is supported by this example script and notebook.
Masked language modeling chapter of the 🤗 Hugging Face Course.
Masked language modeling task guide

[BertForQuestionAnswering] is supported by this example script and notebook.
[TFBertForQuestionAnswering] is supported by this example script and notebook.
[FlaxBertForQuestionAnswering] is supported by this example script.
Question answering chapter of the 🤗 Hugging Face Course.
Question answering task guide

Multiple choice
- [BertForMultipleChoice] is supported by this example script and notebook.
- [TFBertForMultipleChoice] is supported by this example script and notebook.
- Multiple choice task guide
⚡️ Inference
- A blog post on how to Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia.
- A blog post on how to Accelerate BERT inference with DeepSpeed-Inference on GPUs.
⚙️ Pretraining
- A blog post on Pre-Training BERT with Hugging Face Transformers and Habana Gaudi.
🚀 Deploy
- A blog post on how to Convert Transformers to ONNX with Hugging Face Optimum.
- A blog post on how to Setup Deep Learning environment for Hugging Face Transformers with Habana Gaudi on AWS.
- A blog post on Autoscaling BERT with Hugging Face Transformers, Amazon SageMaker and Terraform module.
- A blog post on Serverless BERT with HuggingFace, AWS Lambda, and Docker.
- A blog post on Hugging Face Transformers BERT fine-tuning using Amazon SageMaker and Training Compiler.
- A blog post on Task-specific knowledge distillation for BERT using Transformers & Amazon SageMaker.
BertConfig
[[autodoc]] BertConfig
    - all
BertTokenizer
[[autodoc]] BertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
BertTokenizerFast
[[autodoc]] BertTokenizerFast
TFBertTokenizer
[[autodoc]] TFBertTokenizer
Bert specific outputs
[[autodoc]] models.bert.modeling_bert.BertForPreTrainingOutput
[[autodoc]] models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
[[autodoc]] models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
BertModel
[[autodoc]] BertModel
    - forward
BertForPreTraining
[[autodoc]] BertForPreTraining
    - forward
BertLMHeadModel
[[autodoc]] BertLMHeadModel
    - forward
BertForMaskedLM
[[autodoc]] BertForMaskedLM
    - forward
BertForNextSentencePrediction
[[autodoc]] BertForNextSentencePrediction
    - forward
BertForSequenceClassification
[[autodoc]] BertForSequenceClassification
    - forward
BertForMultipleChoice
[[autodoc]] BertForMultipleChoice
    - forward
BertForTokenClassification
[[autodoc]] BertForTokenClassification
    - forward
BertForQuestionAnswering
[[autodoc]] BertForQuestionAnswering
    - forward
TFBertModel
[[autodoc]] TFBertModel
    - call
TFBertForPreTraining
[[autodoc]] TFBertForPreTraining
    - call
TFBertModelLMHeadModel
[[autodoc]] TFBertLMHeadModel
    - call
TFBertForMaskedLM
[[autodoc]] TFBertForMaskedLM
    - call
TFBertForNextSentencePrediction
[[autodoc]] TFBertForNextSentencePrediction
    - call
TFBertForSequenceClassification
[[autodoc]] TFBertForSequenceClassification
    - call
TFBertForMultipleChoice
[[autodoc]] TFBertForMultipleChoice
    - call
TFBertForTokenClassification
[[autodoc]] TFBertForTokenClassification
    - call
TFBertForQuestionAnswering
[[autodoc]] TFBertForQuestionAnswering
    - call
FlaxBertModel
[[autodoc]] FlaxBertModel
    - call
FlaxBertForPreTraining
[[autodoc]] FlaxBertForPreTraining
    - call
FlaxBertForCausalLM
[[autodoc]] FlaxBertForCausalLM
    - call
FlaxBertForMaskedLM
[[autodoc]] FlaxBertForMaskedLM
    - call
FlaxBertForNextSentencePrediction
[[autodoc]] FlaxBertForNextSentencePrediction
    - call
FlaxBertForSequenceClassification
[[autodoc]] FlaxBertForSequenceClassification
    - call
FlaxBertForMultipleChoice
[[autodoc]] FlaxBertForMultipleChoice
    - call
FlaxBertForTokenClassification
[[autodoc]] FlaxBertForTokenClassification
    - call
FlaxBertForQuestionAnswering
[[autodoc]] FlaxBertForQuestionAnswering
    - call"
"
MGP-STR
Overview
The MGP-STR model was proposed in Multi-Granularity Prediction for Scene Text Recognition by Peng Wang, Cheng Da, and Cong Yao. MGP-STR is a conceptually simple yet powerful vision Scene Text Recognition (STR) model, which is built upon the Vision Transformer (ViT). To integrate linguistic knowledge, Multi-Granularity Prediction (MGP) strategy is proposed to inject information from the language modality into the model in an implicit way.
The abstract from the paper is the following:
Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet powerful vision STR model, which is built upon ViT and outperforms previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, i.e. , subword representations (BPE and WordPiece) widely-used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. The resultant algorithm (termed MGP-STR) is able to push the performance envelop of STR to an even higher level. Specifically, it achieves an average recognition accuracy of 93.35% on standard benchmarks.

 MGP-STR architecture. Taken from the original paper. 
Tips:

MGP-STR is trained on two synthetic datasets MJSynth (MJ) and SynthText(http://www.robots.ox.ac.uk/~vgg/data/scenetext/) (ST) without fine-tuning on other datasets. It achieves state-of-the-art results on six standard Latin scene text benchmarks, including 3 regular text datasets (IC13, SVT, IIIT) and 3 irregular ones (IC15, SVTP, CUTE).
This model was contributed by yuekun. The original code can be found here.

Inference
[MgpstrModel] accepts images as input and generates three types of predictions, which represent textual information at different granularities.
The three types of predictions are fused to give the final prediction result.
The [ViTImageProcessor] class is responsible for preprocessing the input image and
[MgpstrTokenizer] decodes the generated character tokens to the target string. The
[MgpstrProcessor] wraps [ViTImageProcessor] and [MgpstrTokenizer]
into a single instance to both extract the input features and decode the predicted token ids.

Step-by-step Optical Character Recognition (OCR)

``` py

from transformers import MgpstrProcessor, MgpstrForSceneTextRecognition
import requests
from PIL import Image
processor = MgpstrProcessor.from_pretrained('alibaba-damo/mgp-str-base')
model = MgpstrForSceneTextRecognition.from_pretrained('alibaba-damo/mgp-str-base')
load image from the IIIT-5k dataset
url = ""https://i.postimg.cc/ZKwLg2Gw/367-14.png""
image = Image.open(requests.get(url, stream=True).raw).convert(""RGB"")
pixel_values = processor(images=image, return_tensors=""pt"").pixel_values
outputs = model(pixel_values)
generated_text = processor.batch_decode(outputs.logits)['generated_text']

MgpstrConfig
[[autodoc]] MgpstrConfig
MgpstrTokenizer
[[autodoc]] MgpstrTokenizer
    - save_vocabulary
MgpstrProcessor
[[autodoc]] MgpstrProcessor
    - call
    - batch_decode
MgpstrModel
[[autodoc]] MgpstrModel
    - forward
MgpstrForSceneTextRecognition
[[autodoc]] MgpstrForSceneTextRecognition
    - forward"
"
QDQBERT
Overview
The QDQBERT model can be referenced in Integer Quantization for Deep Learning Inference: Principles and Empirical
Evaluation by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius
Micikevicius.
The abstract from the paper is the following:
Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by
taking advantage of high throughput integer instructions. In this paper we review the mathematical aspects of
quantization parameters and evaluate their choices on a wide range of neural network models for different application
domains, including vision, speech, and language. We focus on quantization techniques that are amenable to acceleration
by processors with high-throughput integer math pipelines. We also present a workflow for 8-bit quantization that is
able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are
more difficult to quantize, such as MobileNets and BERT-large.
Tips:

QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to (i) linear layer
  inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.

QDQBERT requires the dependency of Pytorch Quantization Toolkit. To install pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com

QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example bert-base-uncased), and
  perform Quantization Aware Training/Post Training Quantization.

A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for
  SQUAD task can be found at transformers/examples/research_projects/quantization-qdqbert/.

This model was contributed by shangz.
Set default quantizers
QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to BERT by
TensorQuantizer in Pytorch Quantization Toolkit. TensorQuantizer is the module
for quantizing tensors, with QuantDescriptor defining how the tensor should be quantized. Refer to Pytorch
Quantization Toolkit userguide for more details.
Before creating QDQBERT model, one has to set the default QuantDescriptor defining default tensor quantizers.
Example:
thon

import pytorch_quantization.nn as quant_nn
from pytorch_quantization.tensor_quant import QuantDescriptor
The default tensor quantizer is set to use Max calibration method
input_desc = QuantDescriptor(num_bits=8, calib_method=""max"")
The default tensor quantizer is set to be per-channel quantization for weights
weight_desc = QuantDescriptor(num_bits=8, axis=((0,)))
quant_nn.QuantLinear.set_default_quant_desc_input(input_desc)
quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc)

Calibration
Calibration is the terminology of passing data samples to the quantizer and deciding the best scaling factors for
tensors. After setting up the tensor quantizers, one can use the following example to calibrate the model:
thon

Find the TensorQuantizer and enable calibration
for name, module in model.named_modules():
     if name.endswith(""_input_quantizer""):
         module.enable_calib()
         module.disable_quant()  # Use full precision data to calibrate
Feeding data samples
model(x)

Finalize calibration
for name, module in model.named_modules():
     if name.endswith(""_input_quantizer""):
         module.load_calib_amax()
         module.enable_quant()
If running on GPU, it needs to call .cuda() again because new tensors will be created by calibration process
model.cuda()
Keep running the quantized model

Export to ONNX
The goal of exporting to ONNX is to deploy inference by TensorRT. Fake
quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. After setting static member of
TensorQuantizer to use Pytorch’s own fake quantization functions, fake quantized model can be exported to ONNX, follow
the instructions in torch.onnx. Example:
thon

from pytorch_quantization.nn import TensorQuantizer
TensorQuantizer.use_fb_fake_quant = True
Load the calibrated model

ONNX export
torch.onnx.export()

Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

QDQBertConfig
[[autodoc]] QDQBertConfig
QDQBertModel
[[autodoc]] QDQBertModel
    - forward
QDQBertLMHeadModel
[[autodoc]] QDQBertLMHeadModel
    - forward
QDQBertForMaskedLM
[[autodoc]] QDQBertForMaskedLM
    - forward
QDQBertForSequenceClassification
[[autodoc]] QDQBertForSequenceClassification
    - forward
QDQBertForNextSentencePrediction
[[autodoc]] QDQBertForNextSentencePrediction
    - forward
QDQBertForMultipleChoice
[[autodoc]] QDQBertForMultipleChoice
    - forward
QDQBertForTokenClassification
[[autodoc]] QDQBertForTokenClassification
    - forward
QDQBertForQuestionAnswering
[[autodoc]] QDQBertForQuestionAnswering
    - forward"
"
TimeSformer
Overview
The TimeSformer model was proposed in TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Facebook Research.
This work is a milestone in action-recognition field being the first video transformer. It inspired many transformer based video understanding and classification papers.
The abstract from the paper is the following:
We present a convolution-free approach to video classification built exclusively on self-attention over space and time. Our method, named ""TimeSformer,"" adapts the standard Transformer architecture to video by enabling spatiotemporal feature learning directly from a sequence of frame-level patches. Our experimental study compares different self-attention schemes and suggests that ""divided attention,"" where temporal attention and spatial attention are separately applied within each block, leads to the best video classification accuracy among the design choices considered. Despite the radically new design, TimeSformer achieves state-of-the-art results on several action recognition benchmarks, including the best reported accuracy on Kinetics-400 and Kinetics-600. Finally, compared to 3D convolutional networks, our model is faster to train, it can achieve dramatically higher test efficiency (at a small drop in accuracy), and it can also be applied to much longer video clips (over one minute long). Code and models are available at: this https URL.
Tips:
There are many pretrained variants. Select your pretrained model based on the dataset it is trained on. Moreover, the number of input frames per clip changes based on the model size so you should consider this parameter while selecting your pretrained model.
This model was contributed by fcakyon.
The original code can be found here.
Documentation resources

Video classification task guide

TimesformerConfig
[[autodoc]] TimesformerConfig
TimesformerModel
[[autodoc]] TimesformerModel
    - forward
TimesformerForVideoClassification
[[autodoc]] TimesformerForVideoClassification
    - forward"
"
XLM-RoBERTa-XL
Overview
The XLM-RoBERTa-XL model was proposed in Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau. 
The abstract from the paper is the following:
Recent work has demonstrated the effectiveness of cross-lingual language model pretraining for cross-lingual understanding. In this study, we present the results of two larger multilingual masked language models, with 3.5B and 10.7B parameters. Our two new models dubbed XLM-R XL and XLM-R XXL outperform XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on average while handling 99 more languages. This suggests pretrained models with larger capacity may obtain both strong performance on high-resource languages while greatly improving low-resource languages. We make our code and models publicly available.
Tips:

XLM-RoBERTa-XL is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
  not require lang tensors to understand which language is used, and should be able to determine the correct
  language from the input ids.

This model was contributed by Soonhwan-Kwon and stefan-it. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

XLMRobertaXLConfig
[[autodoc]] XLMRobertaXLConfig
XLMRobertaXLModel
[[autodoc]] XLMRobertaXLModel
    - forward
XLMRobertaXLForCausalLM
[[autodoc]] XLMRobertaXLForCausalLM
    - forward
XLMRobertaXLForMaskedLM
[[autodoc]] XLMRobertaXLForMaskedLM
    - forward
XLMRobertaXLForSequenceClassification
[[autodoc]] XLMRobertaXLForSequenceClassification
    - forward
XLMRobertaXLForMultipleChoice
[[autodoc]] XLMRobertaXLForMultipleChoice
    - forward
XLMRobertaXLForTokenClassification
[[autodoc]] XLMRobertaXLForTokenClassification
    - forward
XLMRobertaXLForQuestionAnswering
[[autodoc]] XLMRobertaXLForQuestionAnswering
    - forward"
"
RoBERTa

Overview
The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer
Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018.
It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with
much larger mini-batches and learning rates.
The abstract from the paper is the following:
Language model pretraining has led to significant performance gains but careful comparison between different
approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes,
and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication
study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and
training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every
model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results
highlight the importance of previously overlooked design choices, and raise questions about the source of recently
reported improvements. We release our models and code.
Tips:

This implementation is the same as [BertModel] with a tiny embeddings tweak as well as a setup
  for Roberta pretrained models.
RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
  different pretraining scheme.
RoBERTa doesn't have token_type_ids, you don't need to indicate which token belongs to which segment. Just
  separate your segments with the separation token tokenizer.sep_token (or </s>)

Same as BERT with better pretraining tricks:

dynamic masking: tokens are masked differently at each epoch, whereas BERT does it once and for all
together to reach 512 tokens (so the sentences are in an order than may span several documents)
train with larger batches
use BPE with bytes as a subunit and not characters (because of unicode characters)
CamemBERT is a wrapper around RoBERTa. Refer to this page for usage examples.

This model was contributed by julien-c. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with RoBERTa. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog on Getting Started with Sentiment Analysis on Twitter using RoBERTa and the Inference API.
A blog on Opinion Classification with Kili and Hugging Face AutoTrain using RoBERTa.
A notebook on how to finetune RoBERTa for sentiment analysis. 🌎
[RobertaForSequenceClassification] is supported by this example script and notebook.
[TFRobertaForSequenceClassification] is supported by this example script and notebook.
[FlaxRobertaForSequenceClassification] is supported by this example script and notebook.
Text classification task guide

[RobertaForTokenClassification] is supported by this example script and notebook.
[TFRobertaForTokenClassification] is supported by this example script and notebook.
[FlaxRobertaForTokenClassification] is supported by this example script.
Token classification chapter of the 🤗 Hugging Face Course.
Token classification task guide

A blog on How to train a new language model from scratch using Transformers and Tokenizers with RoBERTa.
[RobertaForMaskedLM] is supported by this example script and notebook.
[TFRobertaForMaskedLM] is supported by this example script and notebook.
[FlaxRobertaForMaskedLM] is supported by this example script and notebook.
Masked language modeling chapter of the 🤗 Hugging Face Course.
Masked language modeling task guide

A blog on Accelerated Inference with Optimum and Transformers Pipelines with RoBERTa for question answering.
[RobertaForQuestionAnswering] is supported by this example script and notebook.
[TFRobertaForQuestionAnswering] is supported by this example script and notebook.
[FlaxRobertaForQuestionAnswering] is supported by this example script.
Question answering chapter of the 🤗 Hugging Face Course.
Question answering task guide

Multiple choice
- [RobertaForMultipleChoice] is supported by this example script and notebook.
- [TFRobertaForMultipleChoice] is supported by this example script and notebook.
- Multiple choice task guide
RobertaConfig
[[autodoc]] RobertaConfig
RobertaTokenizer
[[autodoc]] RobertaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
RobertaTokenizerFast
[[autodoc]] RobertaTokenizerFast
    - build_inputs_with_special_tokens
RobertaModel
[[autodoc]] RobertaModel
    - forward
RobertaForCausalLM
[[autodoc]] RobertaForCausalLM
    - forward
RobertaForMaskedLM
[[autodoc]] RobertaForMaskedLM
    - forward
RobertaForSequenceClassification
[[autodoc]] RobertaForSequenceClassification
    - forward
RobertaForMultipleChoice
[[autodoc]] RobertaForMultipleChoice
    - forward
RobertaForTokenClassification
[[autodoc]] RobertaForTokenClassification
    - forward
RobertaForQuestionAnswering
[[autodoc]] RobertaForQuestionAnswering
    - forward
TFRobertaModel
[[autodoc]] TFRobertaModel
    - call
TFRobertaForCausalLM
[[autodoc]] TFRobertaForCausalLM
    - call
TFRobertaForMaskedLM
[[autodoc]] TFRobertaForMaskedLM
    - call
TFRobertaForSequenceClassification
[[autodoc]] TFRobertaForSequenceClassification
    - call
TFRobertaForMultipleChoice
[[autodoc]] TFRobertaForMultipleChoice
    - call
TFRobertaForTokenClassification
[[autodoc]] TFRobertaForTokenClassification
    - call
TFRobertaForQuestionAnswering
[[autodoc]] TFRobertaForQuestionAnswering
    - call
FlaxRobertaModel
[[autodoc]] FlaxRobertaModel
    - call
FlaxRobertaForCausalLM
[[autodoc]] FlaxRobertaForCausalLM
    - call
FlaxRobertaForMaskedLM
[[autodoc]] FlaxRobertaForMaskedLM
    - call
FlaxRobertaForSequenceClassification
[[autodoc]] FlaxRobertaForSequenceClassification
    - call
FlaxRobertaForMultipleChoice
[[autodoc]] FlaxRobertaForMultipleChoice
    - call
FlaxRobertaForTokenClassification
[[autodoc]] FlaxRobertaForTokenClassification
    - call
FlaxRobertaForQuestionAnswering
[[autodoc]] FlaxRobertaForQuestionAnswering
    - call"
"
BridgeTower
Overview
The BridgeTower model was proposed in BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The goal of this model is to build a
bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder thus achieving remarkable performance on various downstream tasks with almost negligible additional performance and computational costs.
This paper has been accepted to the AAAI'23 conference. 
The abstract from the paper is the following:
Vision-Language (VL) models with the TWO-TOWER architecture have dominated visual-language representation learning in recent years.
Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder.
Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BRIDGETOWER, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the crossmodal encoder.
This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BRIDGETOWER achieves state-of-the-art performance on various downstream vision-language tasks.
In particular, on the VQAv2 test-std set, BRIDGETOWER achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs.
Notably, when further scaling the model, BRIDGETOWER achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets.

 BridgeTower architecture. Taken from the original paper. 
Usage
BridgeTower consists of a visual encoder, a textual encoder and cross-modal encoder with multiple lightweight bridge layers.
The goal of this approach was to build a bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder.
In principle, one can apply any visual, textual or cross-modal encoder in the proposed architecture.
The [BridgeTowerProcessor] wraps [RobertaTokenizer] and [BridgeTowerImageProcessor] into a single instance to both
encode the text and prepare the images respectively.
The following example shows how to run contrastive learning using [BridgeTowerProcessor] and [BridgeTowerForContrastiveLearning].
thon

from transformers import BridgeTowerProcessor, BridgeTowerForContrastiveLearning
import requests
from PIL import Image
url = ""http://images.cocodataset.org/val2017/000000039769.jpg""
image = Image.open(requests.get(url, stream=True).raw)
texts = [""An image of two cats chilling on a couch"", ""A football player scoring a goal""]
processor = BridgeTowerProcessor.from_pretrained(""BridgeTower/bridgetower-large-itm-mlm-itc"")
model = BridgeTowerForContrastiveLearning.from_pretrained(""BridgeTower/bridgetower-large-itm-mlm-itc"")
forward pass
scores = dict()
for text in texts:
     # prepare inputs
     encoding = processor(image, text, return_tensors=""pt"")
     outputs = model(**encoding)
     scores[text] = outputs

The following example shows how to run image-text retrieval using [BridgeTowerProcessor] and [BridgeTowerForImageAndTextRetrieval].
thon

from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval
import requests
from PIL import Image
url = ""http://images.cocodataset.org/val2017/000000039769.jpg""
image = Image.open(requests.get(url, stream=True).raw)
texts = [""An image of two cats chilling on a couch"", ""A football player scoring a goal""]
processor = BridgeTowerProcessor.from_pretrained(""BridgeTower/bridgetower-base-itm-mlm"")
model = BridgeTowerForImageAndTextRetrieval.from_pretrained(""BridgeTower/bridgetower-base-itm-mlm"")
forward pass
scores = dict()
for text in texts:
     # prepare inputs
     encoding = processor(image, text, return_tensors=""pt"")
     outputs = model(**encoding)
     scores[text] = outputs.logits[0, 1].item()

The following example shows how to run masked language modeling using [BridgeTowerProcessor] and [BridgeTowerForMaskedLM].
thon

from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM
from PIL import Image
import requests
url = ""http://images.cocodataset.org/val2017/000000360943.jpg""
image = Image.open(requests.get(url, stream=True).raw).convert(""RGB"")
text = ""a  looking out of the window""
processor = BridgeTowerProcessor.from_pretrained(""BridgeTower/bridgetower-base-itm-mlm"")
model = BridgeTowerForMaskedLM.from_pretrained(""BridgeTower/bridgetower-base-itm-mlm"")
prepare inputs
encoding = processor(image, text, return_tensors=""pt"")
forward pass
outputs = model(**encoding)
results = processor.decode(outputs.logits.argmax(dim=-1).squeeze(0).tolist())
print(results)
.a cat looking out of the window.

This model was contributed by Anahita Bhiwandiwalla, Tiep Le and Shaoyen Tseng. The original code can be found here.
Tips:

This implementation of BridgeTower uses [RobertaTokenizer] to generate text embeddings and OpenAI's CLIP/ViT model to compute visual embeddings.
Checkpoints for pre-trained bridgeTower-base and bridgetower masked language modeling and image text matching are released.
Please refer to Table 5 for BridgeTower's performance on Image Retrieval and other down stream tasks.
The PyTorch version of this model is only available in torch 1.10 and higher.

BridgeTowerConfig
[[autodoc]] BridgeTowerConfig
BridgeTowerTextConfig
[[autodoc]] BridgeTowerTextConfig
BridgeTowerVisionConfig
[[autodoc]] BridgeTowerVisionConfig
BridgeTowerImageProcessor
[[autodoc]] BridgeTowerImageProcessor
    - preprocess
BridgeTowerProcessor
[[autodoc]] BridgeTowerProcessor
    - call
BridgeTowerModel
[[autodoc]] BridgeTowerModel
    - forward
BridgeTowerForContrastiveLearning
[[autodoc]] BridgeTowerForContrastiveLearning
    - forward
BridgeTowerForMaskedLM
[[autodoc]] BridgeTowerForMaskedLM
    - forward
BridgeTowerForImageAndTextRetrieval
[[autodoc]] BridgeTowerForImageAndTextRetrieval
    - forward"
"
OneFormer
Overview
The OneFormer model was proposed in OneFormer: One Transformer to Rule Universal Image Segmentation by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi. OneFormer is a universal image segmentation framework that can be trained on a single panoptic dataset to perform semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference.

The abstract from the paper is the following:
Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance. Ideally, a truly universal framework should be trained only once and achieve SOTA performance across all three image segmentation tasks. To that end, we propose OneFormer, a universal image segmentation framework that unifies segmentation with a multi-task train-once design. We first propose a task-conditioned joint training strategy that enables training on ground truths of each domain (semantic, instance, and panoptic segmentation) within a single multi-task training process. Secondly, we introduce a task token to condition our model on the task at hand, making our model task-dynamic to support multi-task training and inference. Thirdly, we propose using a query-text contrastive loss during training to establish better inter-task and inter-class distinctions. Notably, our single OneFormer model outperforms specialized Mask2Former models across all three segmentation tasks on ADE20k, CityScapes, and COCO, despite the latter being trained on each of the three tasks individually with three times the resources. With new ConvNeXt and DiNAT backbones, we observe even more performance improvement. We believe OneFormer is a significant step towards making image segmentation more universal and accessible.
Tips:
-  OneFormer requires two inputs during inference: image and task token. 
- During training, OneFormer only uses panoptic annotations.
- If you want to train the model in a distributed environment across multiple nodes, then one should update the
  get_num_masks function inside in the OneFormerLoss class of modeling_oneformer.py. When training on multiple nodes, this should be
  set to the average number of target masks across all nodes, as can be seen in the original implementation here.
- One can use [OneFormerProcessor] to prepare input images and task inputs for the model and optional targets for the model. [OneformerProcessor] wraps [OneFormerImageProcessor] and [CLIPTokenizer] into a single instance to both prepare the images and encode the task inputs.
- To get the final segmentation, depending on the task, you can call [~OneFormerProcessor.post_process_semantic_segmentation] or [~OneFormerImageProcessor.post_process_instance_segmentation] or [~OneFormerImageProcessor.post_process_panoptic_segmentation]. All three tasks can be solved using [OneFormerForUniversalSegmentation] output, panoptic segmentation accepts an optional label_ids_to_fuse argument to fuse instances of the target object/s (e.g. sky) together.
The figure below illustrates the architecture of OneFormer. Taken from the original paper.

This model was contributed by Jitesh Jain. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with OneFormer.

Demo notebooks regarding inference + fine-tuning on custom data can be found here.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.
OneFormer specific outputs
[[autodoc]] models.oneformer.modeling_oneformer.OneFormerModelOutput
[[autodoc]] models.oneformer.modeling_oneformer.OneFormerForUniversalSegmentationOutput
OneFormerConfig
[[autodoc]] OneFormerConfig
OneFormerImageProcessor
[[autodoc]] OneFormerImageProcessor
    - preprocess
    - encode_inputs
    - post_process_semantic_segmentation
    - post_process_instance_segmentation
    - post_process_panoptic_segmentation
OneFormerProcessor
[[autodoc]] OneFormerProcessor
OneFormerModel
[[autodoc]] OneFormerModel
    - forward
OneFormerForUniversalSegmentation
[[autodoc]] OneFormerForUniversalSegmentation
    - forward"
"
MaskFormer

This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a Github Issue.

Overview
The MaskFormer model was proposed in Per-Pixel Classification is Not All You Need for Semantic Segmentation by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov. MaskFormer addresses semantic segmentation with a mask classification paradigm instead of performing classic pixel-level classification.
The abstract from the paper is the following:
Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction. Overall, the proposed mask classification-based method simplifies the landscape of effective approaches to semantic and panoptic segmentation tasks and shows excellent empirical results. In particular, we observe that MaskFormer outperforms per-pixel classification baselines when the number of classes is large. Our mask classification-based method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.
Tips:
-  MaskFormer's Transformer decoder is identical to the decoder of DETR. During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help the model output the correct number of objects of each class. If you set the parameter use_auxilary_loss of [MaskFormerConfig] to True, then prediction feedforward neural networks and Hungarian losses are added after each decoder layer (with the FFNs sharing parameters).
- If you want to train the model in a distributed environment across multiple nodes, then one should update the
  get_num_masks function inside in the MaskFormerLoss class of modeling_maskformer.py. When training on multiple nodes, this should be
  set to the average number of target masks across all nodes, as can be seen in the original implementation here.
- One can use [MaskFormerImageProcessor] to prepare images for the model and optional targets for the model.
- To get the final segmentation, depending on the task, you can call [~MaskFormerImageProcessor.post_process_semantic_segmentation] or [~MaskFormerImageProcessor.post_process_panoptic_segmentation]. Both tasks can be solved using [MaskFormerForInstanceSegmentation] output, panoptic segmentation accepts an optional label_ids_to_fuse argument to fuse instances of the target object/s (e.g. sky) together.
The figure below illustrates the architecture of MaskFormer. Taken from the original paper.

This model was contributed by francesco. The original code can be found here.
Resources

All notebooks that illustrate inference as well as fine-tuning on custom data with MaskFormer can be found here.

MaskFormer specific outputs
[[autodoc]] models.maskformer.modeling_maskformer.MaskFormerModelOutput
[[autodoc]] models.maskformer.modeling_maskformer.MaskFormerForInstanceSegmentationOutput
MaskFormerConfig
[[autodoc]] MaskFormerConfig
MaskFormerImageProcessor
[[autodoc]] MaskFormerImageProcessor
    - preprocess
    - encode_inputs
    - post_process_semantic_segmentation
    - post_process_instance_segmentation
    - post_process_panoptic_segmentation
MaskFormerFeatureExtractor
[[autodoc]] MaskFormerFeatureExtractor
    - call
    - encode_inputs
    - post_process_semantic_segmentation
    - post_process_instance_segmentation
    - post_process_panoptic_segmentation
MaskFormerModel
[[autodoc]] MaskFormerModel
    - forward
MaskFormerForInstanceSegmentation
[[autodoc]] MaskFormerForInstanceSegmentation
    - forward"
"
Encoder Decoder Models
Overview
The [EncoderDecoderModel] can be used to initialize a sequence-to-sequence model with any
pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
was shown in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by
Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
After such an [EncoderDecoderModel] has been trained/fine-tuned, it can be saved/loaded just like
any other models (see the examples for more information).
An application of this architecture could be to leverage two pretrained [BertModel] as the encoder
and decoder for a summarization model as was shown in: Text Summarization with Pretrained Encoders by Yang Liu and Mirella Lapata.
Randomly initializing EncoderDecoderModel from model configurations.
[EncoderDecoderModel] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [BertModel] configuration for the encoder and the default [BertForCausalLM] configuration for the decoder.
thon

from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
config_encoder = BertConfig()
config_decoder = BertConfig()
config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
model = EncoderDecoderModel(config=config)

Initialising EncoderDecoderModel from a pretrained encoder and a pretrained decoder.
[EncoderDecoderModel] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained auto-encoding model, e.g. BERT, can serve as the encoder and both pretrained auto-encoding models, e.g. BERT, pretrained causal language models, e.g. GPT2, as well as the pretrained decoder part of sequence-to-sequence models, e.g. decoder of BART, can be used as the decoder.
Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized.
Initializing [EncoderDecoderModel] from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in the Warm-starting-encoder-decoder blog post.
To do so, the EncoderDecoderModel class provides a [EncoderDecoderModel.from_encoder_decoder_pretrained] method.
thon

from transformers import EncoderDecoderModel, BertTokenizer
tokenizer = BertTokenizer.from_pretrained(""bert-base-uncased"")
model = EncoderDecoderModel.from_encoder_decoder_pretrained(""bert-base-uncased"", ""bert-base-uncased"")

Loading an existing EncoderDecoderModel checkpoint and perform inference.
To load fine-tuned checkpoints of the EncoderDecoderModel class, [EncoderDecoderModel] provides the from_pretrained() method just like any other model architecture in Transformers.
To perform inference, one uses the [generate] method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling.
thon

from transformers import AutoTokenizer, EncoderDecoderModel
load a fine-tuned seq2seq model and corresponding tokenizer
model = EncoderDecoderModel.from_pretrained(""patrickvonplaten/bert2bert_cnn_daily_mail"")
tokenizer = AutoTokenizer.from_pretrained(""patrickvonplaten/bert2bert_cnn_daily_mail"")
let's perform inference on a long piece of text
ARTICLE_TO_SUMMARIZE = (
     ""PG&E stated it scheduled the blackouts in response to forecasts for high winds ""
     ""amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were ""
     ""scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""
 )
input_ids = tokenizer(ARTICLE_TO_SUMMARIZE, return_tensors=""pt"").input_ids
autoregressively generate summary (uses greedy decoding by default)
generated_ids = model.generate(input_ids)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
nearly 800 thousand customers were affected by the shutoffs. the aim is to reduce the risk of wildfires. nearly 800, 000 customers were expected to be affected by high winds amid dry conditions. pg & e said it scheduled the blackouts to last through at least midday tomorrow.

Loading a PyTorch checkpoint into TFEncoderDecoderModel.
[TFEncoderDecoderModel.from_pretrained] currently doesn't support initializing the model from a
pytorch checkpoint. Passing from_pt=True to this method will throw an exception. If there are only pytorch
checkpoints for a particular encoder-decoder model, a workaround is:
thon

a workaround to load from pytorch checkpoint
from transformers import EncoderDecoderModel, TFEncoderDecoderModel
_model = EncoderDecoderModel.from_pretrained(""patrickvonplaten/bert2bert-cnn_dailymail-fp16"")
_model.encoder.save_pretrained(""./encoder"")
_model.decoder.save_pretrained(""./decoder"")
model = TFEncoderDecoderModel.from_encoder_decoder_pretrained(
     ""./encoder"", ""./decoder"", encoder_from_pt=True, decoder_from_pt=True
 )
This is only for copying some specific attributes of this particular model.
model.config = _model.config

Training
Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model.
As you can see, only 2 inputs are required for the model in order to compute a loss: input_ids (which are the
input_ids of the encoded input sequence) and labels (which are the input_ids of the encoded
target sequence).
thon

from transformers import BertTokenizer, EncoderDecoderModel
tokenizer = BertTokenizer.from_pretrained(""bert-base-uncased"")
model = EncoderDecoderModel.from_encoder_decoder_pretrained(""bert-base-uncased"", ""bert-base-uncased"")
model.config.decoder_start_token_id = tokenizer.cls_token_id
model.config.pad_token_id = tokenizer.pad_token_id
input_ids = tokenizer(
     ""The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side.During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was  finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft).Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."",
     return_tensors=""pt"",
 ).input_ids
labels = tokenizer(
     ""the eiffel tower surpassed the washington monument to become the tallest structure in the world. it was the first structure to reach a height of 300 metres in paris in 1930. it is now taller than the chrysler building by 5. 2 metres ( 17 ft ) and is the second tallest free - standing structure in paris."",
     return_tensors=""pt"",
 ).input_ids
the forward function automatically creates the correct decoder_input_ids
loss = model(input_ids=input_ids, labels=labels).loss

Detailed colab for training.
This model was contributed by thomwolf. This model's TensorFlow and Flax versions
were contributed by ydshieh.
EncoderDecoderConfig
[[autodoc]] EncoderDecoderConfig
EncoderDecoderModel
[[autodoc]] EncoderDecoderModel
    - forward
    - from_encoder_decoder_pretrained
TFEncoderDecoderModel
[[autodoc]] TFEncoderDecoderModel
    - call
    - from_encoder_decoder_pretrained
FlaxEncoderDecoderModel
[[autodoc]] FlaxEncoderDecoderModel
    - call
    - from_encoder_decoder_pretrained"
"
mLUKE
Overview
The mLUKE model was proposed in mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka. It's a multilingual extension
of the LUKE model trained on the basis of XLM-RoBERTa.
It is based on XLM-RoBERTa and adds entity embeddings, which helps improve performance on various downstream tasks
involving reasoning about entities such as named entity recognition, extractive question answering, relation
classification, cloze-style knowledge completion.
The abstract from the paper is the following:
Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual
alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining
and do not explicitly use entities in downstream tasks. In this study, we explore the effectiveness of leveraging
entity representations for downstream cross-lingual tasks. We train a multilingual language model with 24 languages
with entity representations and show the model consistently outperforms word-based pretrained models in various
cross-lingual transfer tasks. We also analyze the model and the key insight is that incorporating entity
representations into the input allows us to extract more language-agnostic features. We also evaluate the model with a
multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual
knowledge more likely than using only word representations.
One can directly plug in the weights of mLUKE into a LUKE model, like so:
thon
from transformers import LukeModel
model = LukeModel.from_pretrained(""studio-ousia/mluke-base"")

Note that mLUKE has its own tokenizer, [MLukeTokenizer]. You can initialize it as follows:
thon
from transformers import MLukeTokenizer
tokenizer = MLukeTokenizer.from_pretrained(""studio-ousia/mluke-base"")

As mLUKE's architecture is equivalent to that of LUKE, one can refer to LUKE's documentation page for all
tips, code examples and notebooks.
This model was contributed by ryo0634. The original code can be found here.
MLukeTokenizer
[[autodoc]] MLukeTokenizer
    - call
    - save_vocabulary"
"
CamemBERT
Overview
The CamemBERT model was proposed in CamemBERT: a Tasty French Language Model by
Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la
Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook's RoBERTa model released in 2019. It is a model
trained on 138GB of French text.
The abstract from the paper is the following:
Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available
models have either been trained on English data or on the concatenation of data in multiple languages. This makes
practical use of such models --in all languages except English-- very limited. Aiming to address this issue for French,
we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the
performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging,
dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art
for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and
downstream applications for French NLP.
Tips:

This implementation is the same as RoBERTa. Refer to the documentation of RoBERTa for usage examples
  as well as the information relative to the inputs and outputs.

This model was contributed by camembert. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

CamembertConfig
[[autodoc]] CamembertConfig
CamembertTokenizer
[[autodoc]] CamembertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
CamembertTokenizerFast
[[autodoc]] CamembertTokenizerFast
CamembertModel
[[autodoc]] CamembertModel
CamembertForCausalLM
[[autodoc]] CamembertForCausalLM
CamembertForMaskedLM
[[autodoc]] CamembertForMaskedLM
CamembertForSequenceClassification
[[autodoc]] CamembertForSequenceClassification
CamembertForMultipleChoice
[[autodoc]] CamembertForMultipleChoice
CamembertForTokenClassification
[[autodoc]] CamembertForTokenClassification
CamembertForQuestionAnswering
[[autodoc]] CamembertForQuestionAnswering
TFCamembertModel
[[autodoc]] TFCamembertModel
TFCamembertForCasualLM
[[autodoc]] TFCamembertForCausalLM
TFCamembertForMaskedLM
[[autodoc]] TFCamembertForMaskedLM
TFCamembertForSequenceClassification
[[autodoc]] TFCamembertForSequenceClassification
TFCamembertForMultipleChoice
[[autodoc]] TFCamembertForMultipleChoice
TFCamembertForTokenClassification
[[autodoc]] TFCamembertForTokenClassification
TFCamembertForQuestionAnswering
[[autodoc]] TFCamembertForQuestionAnswering"
"
BARThez
Overview
The BARThez model was proposed in BARThez: a Skilled Pretrained French Sequence-to-Sequence Model by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
2020.
The abstract of the paper:
Inductive transfer learning, enabled by self-supervised learning, have taken the entire Natural Language Processing
(NLP) field by storm, with models such as BERT and BART setting new state of the art on countless natural language
understanding tasks. While there are some notable exceptions, most of the available models and research have been
conducted for the English language. In this work, we introduce BARThez, the first BART model for the French language
(to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research
that we adapted to suit BART's perturbation schemes. Unlike already existing BERT-based French language models such as
CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks, since not only its encoder but also
its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark, we evaluate BARThez on a novel
summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already
pretrained multilingual BART on BARThez's corpus, and we show that the resulting model, which we call mBARTHez,
provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.
This model was contributed by moussakam. The Authors' code can be found here.
Examples

BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check:
  examples/pytorch/summarization/.

BarthezTokenizer
[[autodoc]] BarthezTokenizer
BarthezTokenizerFast
[[autodoc]] BarthezTokenizerFast"
"
CLIPSeg
Overview
The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke
and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.
The abstract from the paper is the following:
Image segmentation is usually addressed by training a
model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive
as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system
that can generate image segmentations based on arbitrary
prompts at test time. A prompt can be either a text or an
image. This approach enables us to create a unified model
(trained once) for three common segmentation tasks, which
come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation.
We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense
prediction. After training on an extended version of the
PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on
an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail.
This novel hybrid input allows for dynamic adaptation not
only to the three segmentation tasks mentioned above, but
to any binary segmentation task where a text or image query
can be formulated. Finally, we find our system to adapt well
to generalized queries involving affordances or properties
Tips:

[CLIPSegForImageSegmentation] adds a decoder on top of [CLIPSegModel]. The latter is identical to [CLIPModel].
[CLIPSegForImageSegmentation] can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text
(provided to the model as input_ids) or an image (provided to the model as conditional_pixel_values). One can also provide custom
conditional embeddings (provided to the model as conditional_embeddings).

 
 CLIPSeg overview. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIPSeg. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A notebook that illustrates zero-shot image segmentation with CLIPSeg.

CLIPSegConfig
[[autodoc]] CLIPSegConfig
    - from_text_vision_configs
CLIPSegTextConfig
[[autodoc]] CLIPSegTextConfig
CLIPSegVisionConfig
[[autodoc]] CLIPSegVisionConfig
CLIPSegProcessor
[[autodoc]] CLIPSegProcessor
CLIPSegModel
[[autodoc]] CLIPSegModel
    - forward
    - get_text_features
    - get_image_features
CLIPSegTextModel
[[autodoc]] CLIPSegTextModel
    - forward
CLIPSegVisionModel
[[autodoc]] CLIPSegVisionModel
    - forward
CLIPSegForImageSegmentation
[[autodoc]] CLIPSegForImageSegmentation
    - forward"
"
T5

Overview
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
The abstract from the paper is the following:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream
task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning
has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of
transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a
text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled datasets, transfer
approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration
with scale and our new ""Colossal Clean Crawled Corpus"", we achieve state-of-the-art results on many benchmarks covering
summarization, question answering, text classification, and more. To facilitate future work on transfer learning for
NLP, we release our dataset, pre-trained models, and code.
Tips:

T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which
each task is converted into a text-to-text format. T5 works well on a variety of tasks out-of-the-box by prepending a
different prefix to the input corresponding to each task, e.g., for translation: translate English to German: ,
for summarization: summarize: .
The pretraining includes both supervised and self-supervised training. Supervised training is conducted on downstream tasks provided by the GLUE and SuperGLUE benchmarks (converting them into text-to-text tasks as explained above).

Self-supervised training uses corrupted tokens, by randomly removing 15% of the tokens and replacing them with individual sentinel tokens (if several consecutive tokens are marked for removal, the whole group is replaced with a single sentinel token). The input of the encoder is the corrupted sentence, the input of the decoder is the original sentence and the target is then the dropped out tokens delimited by their sentinel tokens.

T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.

See the training, inference and scripts sections below for all details regarding usage.

T5 comes in different sizes:

t5-small

t5-base

t5-large

t5-3b

t5-11b.

Based on the original T5 model, Google has released some follow-up works:

T5v1.1: T5v1.1 is an improved version of T5 with some architectural tweaks, and is pre-trained on C4 only without
  mixing in the supervised tasks. Refer to the documentation of T5v1.1 which can be found here.

mT5: mT5 is a multilingual T5 model. It is pre-trained on the mC4 corpus, which includes 101 languages. Refer to
  the documentation of mT5 which can be found here.

byT5: byT5 is a T5 model pre-trained on byte sequences rather than SentencePiece subword token sequences. Refer
  to the documentation of byT5 which can be found here.

UL2: UL2 is a T5 like model pretrained on various denoising objectives

Flan-T5: Flan is a pretraining methods that is based on prompting. The Flan-T5 are T5 models trained on the Flan collection of 
    datasets which include: taskmaster2, djaym7/wiki_dialog, deepmind/code_contests, lambada, gsm8k, aqua_rat, esnli, quasc and qed.

FLan-UL2 : the UL2 model finetuned using the ""Flan"" prompt tuning and dataset collection.

UMT5: UmT5 is a multilingual T5 model trained on an improved and refreshed mC4 multilingual corpus,  29 trillion characters across 107 language, using a new sampling method, UniMax. Refer to
 the documentation of mT5 which can be found here.

All checkpoints can be found on the hub.
This model was contributed by thomwolf. The original code can be found here.

Training
T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher
forcing. This means that for training, we always need an input sequence and a corresponding target sequence. The input
sequence is fed to the model using input_ids. The target sequence is shifted to the right, i.e., prepended by a
start-sequence token and fed to the decoder using the decoder_input_ids. In teacher-forcing style, the target
sequence is then appended by the EOS token and corresponds to the labels. The PAD token is hereby used as the
start-sequence token. T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
One can use [T5ForConditionalGeneration] (or the Tensorflow/Flax variant), which includes the
language modeling head on top of the decoder.

Unsupervised denoising training

In this setup, spans of the input sequence are masked by so-called sentinel tokens (a.k.a unique mask tokens) and
the output sequence is formed as a concatenation of the same sentinel tokens and the real masked tokens. Each
sentinel token represents a unique mask token for this sentence and should start with <extra_id_0>,
<extra_id_1>,  up to <extra_id_99>. As a default, 100 sentinel tokens are available in
[T5Tokenizer].
For instance, the sentence ""The cute dog walks in the park"" with the masks put on ""cute dog"" and ""the"" should be
processed as follows:
thon

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained(""t5-small"")
model = T5ForConditionalGeneration.from_pretrained(""t5-small"")
input_ids = tokenizer(""The  walks in  park"", return_tensors=""pt"").input_ids
labels = tokenizer("" cute dog  the "", return_tensors=""pt"").input_ids
the forward function automatically creates the correct decoder_input_ids
loss = model(input_ids=input_ids, labels=labels).loss
loss.item()
3.7837

If you're interested in pre-training T5 on a new corpus, check out the run_t5_mlm_flax.py script in the Examples
directory.

Supervised training

In this setup, the input sequence and output sequence are a standard sequence-to-sequence input-output mapping.
Suppose that we want to fine-tune the model for translation for example, and we have a training example: the input
sequence ""The house is wonderful."" and output sequence ""Das Haus ist wunderbar."", then they should be prepared for
the model as follows:
thon

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained(""t5-small"")
model = T5ForConditionalGeneration.from_pretrained(""t5-small"")
input_ids = tokenizer(""translate English to German: The house is wonderful."", return_tensors=""pt"").input_ids
labels = tokenizer(""Das Haus ist wunderbar."", return_tensors=""pt"").input_ids
the forward function automatically creates the correct decoder_input_ids
loss = model(input_ids=input_ids, labels=labels).loss
loss.item()
0.2542

As you can see, only 2 inputs are required for the model in order to compute a loss: input_ids (which are the
input_ids of the encoded input sequence) and labels (which are the input_ids of the encoded
target sequence). The model will automatically create the decoder_input_ids based on the labels, by
shifting them one position to the right and prepending the config.decoder_start_token_id, which for T5 is
equal to 0 (i.e. the id of the pad token). Also note the task prefix: we prepend the input sequence with 'translate
English to German: ' before encoding it. This will help in improving the performance, as this task prefix was used
during T5's pre-training.
However, the example above only shows a single training example. In practice, one trains deep learning models in
batches. This entails that we must pad/truncate examples to the same length. For encoder-decoder models, one
typically defines a max_source_length and max_target_length, which determine the maximum length of the
input and output sequences respectively (otherwise they are truncated). These should be carefully set depending on
the task.
In addition, we must make sure that padding token id's of the labels are not taken into account by the loss
function. In PyTorch and Tensorflow, this can be done by replacing them with -100, which is the ignore_index
of the CrossEntropyLoss. In Flax, one can use the decoder_attention_mask to ignore padded tokens from
the loss (see the Flax summarization script for details). We also pass
attention_mask as additional input to the model, which makes sure that padding tokens of the inputs are
ignored. The code example below illustrates all of this.
thon

from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
tokenizer = T5Tokenizer.from_pretrained(""t5-small"")
model = T5ForConditionalGeneration.from_pretrained(""t5-small"")
the following 2 hyperparameters are task-specific
max_source_length = 512
max_target_length = 128
Suppose we have the following 2 training examples:
input_sequence_1 = ""Welcome to NYC""
output_sequence_1 = ""Bienvenue à NYC""
input_sequence_2 = ""HuggingFace is a company""
output_sequence_2 = ""HuggingFace est une entreprise""
encode the inputs
task_prefix = ""translate English to French: ""
input_sequences = [input_sequence_1, input_sequence_2]
encoding = tokenizer(
     [task_prefix + sequence for sequence in input_sequences],
     padding=""longest"",
     max_length=max_source_length,
     truncation=True,
     return_tensors=""pt"",
 )
input_ids, attention_mask = encoding.input_ids, encoding.attention_mask
encode the targets
target_encoding = tokenizer(
     [output_sequence_1, output_sequence_2],
     padding=""longest"",
     max_length=max_target_length,
     truncation=True,
     return_tensors=""pt"",
 )
labels = target_encoding.input_ids
replace padding token id's of the labels by -100 so it's ignored by the loss
labels[labels == tokenizer.pad_token_id] = -100
forward pass
loss = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels).loss
loss.item()
0.188

Additional training tips:

T5 models need a slightly higher learning rate than the default one set in the Trainer when using the AdamW
optimizer. Typically, 1e-4 and 3e-4 work well for most problems (classification, summarization, translation, question
answering, question generation). Note that T5 was pre-trained using the AdaFactor optimizer.

According to this forum post, task prefixes matter when
(1) doing multi-task training (2) your task is similar or related to one of the supervised tasks used in T5's
pre-training mixture (see Appendix D of the paper for the task prefixes
used).
If training on TPU, it is recommended to pad all examples of the dataset to the same length or make use of
pad_to_multiple_of to have a small number of predefined bucket sizes to fit all examples in. Dynamically padding
batches to the longest example is not recommended on TPU as it triggers a recompilation for every batch shape that is
encountered during training thus significantly slowing down the training. only padding up to the longest example in a
batch) leads to very slow training on TPU.

Inference
At inference time, it is recommended to use [~generation.GenerationMixin.generate]. This
method takes care of encoding the input and feeding the encoded hidden states via cross-attention layers to the decoder
and auto-regressively generates the decoder output. Check out this blog post to know all the details about generating text with Transformers.
There's also this blog post which explains how
generation works in general in encoder-decoder models.
thon

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained(""t5-small"")
model = T5ForConditionalGeneration.from_pretrained(""t5-small"")
input_ids = tokenizer(""translate English to German: The house is wonderful."", return_tensors=""pt"").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Das Haus ist wunderbar.

Note that T5 uses the pad_token_id as the decoder_start_token_id, so when doing generation without using
[~generation.GenerationMixin.generate], make sure you start it with the pad_token_id.
The example above only shows a single example. You can also do batched inference, like so:
thon

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained(""t5-small"")
model = T5ForConditionalGeneration.from_pretrained(""t5-small"")
task_prefix = ""translate English to German: ""
use different length sentences to test batching
sentences = [""The house is wonderful."", ""I like to work in NYC.""]
inputs = tokenizer([task_prefix + sentence for sentence in sentences], return_tensors=""pt"", padding=True)
output_sequences = model.generate(
     input_ids=inputs[""input_ids""],
     attention_mask=inputs[""attention_mask""],
     do_sample=False,  # disable sampling to test if batching affects output
 )
print(tokenizer.batch_decode(output_sequences, skip_special_tokens=True))
['Das Haus ist wunderbar.', 'Ich arbeite gerne in NYC.']

Because T5 has been trained with the span-mask denoising objective,
it can be used to predict the sentinel (masked-out) tokens during inference.
The predicted tokens will then be placed between the sentinel tokens.
thon

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained(""t5-small"")
model = T5ForConditionalGeneration.from_pretrained(""t5-small"")
input_ids = tokenizer(""The  walks in  park"", return_tensors=""pt"").input_ids
sequence_ids = model.generate(input_ids)
sequences = tokenizer.batch_decode(sequence_ids)
sequences
[' park offers the park.']

Performance
If you'd like a faster training and inference performance, install apex and then the model will automatically use apex.normalization.FusedRMSNorm instead of T5LayerNorm. The former uses an optimized fused kernel which is several times faster than the latter.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with T5. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A notebook for how to finetune T5 for classification and multiple choice.
A notebook for how to finetune T5 for sentiment span extraction. 🌎

A notebook for how to finetune T5 for named entity recognition. 🌎

A notebook for Finetuning CodeT5 for generating docstrings from Ruby code.

A notebook to Finetune T5-base-dutch to perform Dutch abstractive summarization on a TPU.
A notebook for how to finetune T5 for summarization in PyTorch and track experiments with WandB. 🌎
A blog post on Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker.
[T5ForConditionalGeneration] is supported by this example script and notebook.
[TFT5ForConditionalGeneration] is supported by this example script and notebook.
[FlaxT5ForConditionalGeneration] is supported by this example script.
Summarization chapter of the 🤗 Hugging Face course.
Summarization task guide

[FlaxT5ForConditionalGeneration] is supported by this example script for training T5 with a span-masked language model objective. The script also shows how to train a T5 tokenizer. [FlaxT5ForConditionalGeneration] is also supported by this notebook.

[T5ForConditionalGeneration] is supported by this example script and notebook.
[TFT5ForConditionalGeneration] is supported by this example script and notebook.
Translation task guide

A notebook on how to finetune T5 for question answering with TensorFlow 2. 🌎
A notebook on how to finetune T5 for question answering on a TPU.

🚀 Deploy
- A blog post on how to deploy T5 11B for inference for less than $500.
T5Config
[[autodoc]] T5Config
T5Tokenizer
[[autodoc]] T5Tokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
T5TokenizerFast
[[autodoc]] T5TokenizerFast
T5Model
[[autodoc]] T5Model
    - forward
T5ForConditionalGeneration
[[autodoc]] T5ForConditionalGeneration
    - forward
T5EncoderModel
[[autodoc]] T5EncoderModel
    - forward
T5ForQuestionAnswering
[[autodoc]] T5ForQuestionAnswering
    - forward
TFT5Model
[[autodoc]] TFT5Model
    - call
TFT5ForConditionalGeneration
[[autodoc]] TFT5ForConditionalGeneration
    - call
TFT5EncoderModel
[[autodoc]] TFT5EncoderModel
    - call
FlaxT5Model
[[autodoc]] FlaxT5Model
    - call
    - encode
    - decode
FlaxT5ForConditionalGeneration
[[autodoc]] FlaxT5ForConditionalGeneration
    - call
    - encode
    - decode
FlaxT5EncoderModel
[[autodoc]] FlaxT5EncoderModel
    - call"
"
DETA
Overview
The DETA model was proposed in NMS Strikes Back by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
DETA (short for Detection Transformers with Assignment) improves Deformable DETR by replacing the one-to-one bipartite Hungarian matching loss
with one-to-many label assignments used in traditional detectors with non-maximum suppression (NMS). This leads to significant gains of up to 2.5 mAP.
The abstract from the paper is the following:
Detection Transformer (DETR) directly transforms queries to unique objects by using one-to-one bipartite matching during training and enables end-to-end object detection. Recently, these models have surpassed traditional detectors on COCO with undeniable elegance. However, they differ from traditional detectors in multiple designs, including model architecture and training schedules, and thus the effectiveness of one-to-one matching is not fully understood. In this work, we conduct a strict comparison between the one-to-one Hungarian matching in DETRs and the one-to-many label assignments in traditional detectors with non-maximum supervision (NMS). Surprisingly, we observe one-to-many assignments with NMS consistently outperform standard one-to-one matching under the same setting, with a significant gain of up to 2.5 mAP. Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50.2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting. On multiple datasets, schedules, and architectures, we consistently show bipartite matching is unnecessary for performant detection transformers. Furthermore, we attribute the success of detection transformers to their expressive transformer architecture.
Tips:

One can use [DetaImageProcessor] to prepare images and optional targets for the model.

 DETA overview. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DETA.

Demo notebooks for DETA can be found here.
See also: Object detection task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
DetaConfig
[[autodoc]] DetaConfig
DetaImageProcessor
[[autodoc]] DetaImageProcessor
    - preprocess
    - post_process_object_detection
DetaModel
[[autodoc]] DetaModel
    - forward
DetaForObjectDetection
[[autodoc]] DetaForObjectDetection
    - forward"
"
Conditional DETR
Overview
The Conditional DETR model was proposed in Conditional DETR for Fast Training Convergence by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Conditional DETR converges 6.7× to 10× faster than DETR.
The abstract from the paper is the following:
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.

 Conditional DETR shows much faster convergence compared to the original DETR. Taken from the original paper.
This model was contributed by DepuMeng. The original code can be found here.
Documentation resources

Object detection task guide

ConditionalDetrConfig
[[autodoc]] ConditionalDetrConfig
ConditionalDetrImageProcessor
[[autodoc]] ConditionalDetrImageProcessor
    - preprocess
    - post_process_object_detection
    - post_process_instance_segmentation
    - post_process_semantic_segmentation
    - post_process_panoptic_segmentation
ConditionalDetrFeatureExtractor
[[autodoc]] ConditionalDetrFeatureExtractor
    - call
    - post_process_object_detection
    - post_process_instance_segmentation
    - post_process_semantic_segmentation
    - post_process_panoptic_segmentation
ConditionalDetrModel
[[autodoc]] ConditionalDetrModel
    - forward
ConditionalDetrForObjectDetection
[[autodoc]] ConditionalDetrForObjectDetection
    - forward
ConditionalDetrForSegmentation
[[autodoc]] ConditionalDetrForSegmentation
    - forward"
"
CLAP
Overview
The CLAP model was proposed in Large Scale Contrastive Language-Audio pretraining with
feature fusion and keyword-to-caption augmentation by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a variety of (audio, text) pairs. It can be instructed in to predict the most relevant text snippet, given an audio, without directly optimizing for the task. The CLAP model uses a SWINTransformer to get audio features from a log-Mel spectrogram input, and a RoBERTa model to get text features. Both the text and audio features are then projected to a latent space with identical dimension. The dot product between the projected audio and text features is then used as a similar score.
The abstract from the paper is the following:
Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zeroshot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-6
This model was contributed by Younes Belkada and Arthur Zucker .
The original code can be found here.
ClapConfig
[[autodoc]] ClapConfig
    - from_text_audio_configs
ClapTextConfig
[[autodoc]] ClapTextConfig
ClapAudioConfig
[[autodoc]] ClapAudioConfig
ClapFeatureExtractor
[[autodoc]] ClapFeatureExtractor
ClapProcessor
[[autodoc]] ClapProcessor
ClapModel
[[autodoc]] ClapModel
    - forward
    - get_text_features
    - get_audio_features
ClapTextModel
[[autodoc]] ClapTextModel
    - forward
ClapTextModelWithProjection
[[autodoc]] ClapTextModelWithProjection
    - forward
ClapAudioModel
[[autodoc]] ClapAudioModel
    - forward
ClapAudioModelWithProjection
[[autodoc]] ClapAudioModelWithProjection
    - forward"
"
DiT
Overview
DiT was proposed in DiT: Self-supervised Pre-training for Document Image Transformer by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
DiT applies the self-supervised objective of BEiT (BERT pre-training of Image Transformers) to 42 million document images, allowing for state-of-the-art results on tasks including:

document image classification: the RVL-CDIP dataset (a collection of
  400,000 images belonging to one of 16 classes).
document layout analysis: the PubLayNet dataset (a collection of more
  than 360,000 document images constructed by automatically parsing PubMed XML files).
table detection: the ICDAR 2019 cTDaR dataset (a collection of
  600 training images and 240 testing images).

The abstract from the paper is the following:
*Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we propose DiT, a self-supervised pre-trained Document Image Transformer model using large-scale unlabeled text images for Document AI tasks, which is essential since no supervised counterparts ever exist due to the lack of human labeled document images. We leverage DiT as the backbone network in a variety of vision-based Document AI tasks, including document image classification, document layout analysis, as well as table detection. Experiment results have illustrated that the self-supervised pre-trained DiT model achieves new state-of-the-art results on these downstream tasks, e.g. document image classification (91.11 → 92.69), document layout analysis (91.0 → 94.9) and table detection (94.23 → 96.55). *
 
 Summary of the approach. Taken from the original paper. 
One can directly use the weights of DiT with the AutoModel API:
thon
from transformers import AutoModel
model = AutoModel.from_pretrained(""microsoft/dit-base"")

This will load the model pre-trained on masked image modeling. Note that this won't include the language modeling head on top, used to predict visual tokens.
To include the head, you can load the weights into a BeitForMaskedImageModeling model, like so:
thon
from transformers import BeitForMaskedImageModeling
model = BeitForMaskedImageModeling.from_pretrained(""microsoft/dit-base"")

You can also load a fine-tuned model from the hub, like so:
thon
from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained(""microsoft/dit-base-finetuned-rvlcdip"")

This particular checkpoint was fine-tuned on RVL-CDIP, an important benchmark for document image classification.
A notebook that illustrates inference for document image classification can be found here.
As DiT's architecture is equivalent to that of BEiT, one can refer to BEiT's documentation page for all tips, code examples and notebooks.
This model was contributed by nielsr. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DiT.

[BeitForImageClassification] is supported by this example script and notebook.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource."
"
OpenAI GPT2

Overview
OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec
Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever from OpenAI. It's a causal (unidirectional)
transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.
The abstract from the paper is the following:
GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million
web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
10X the amount of data.
Tips:

GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
  observed in the run_generation.py example script.
The model can take the past_key_values (for PyTorch) or past (for TF) as input, which is the previously computed
  key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing
  pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the
  [GPT2Model.forward] method, or for TF the past argument of the
  [TFGPT2Model.call] method for more information on its usage.
Enabling the scale_attn_by_inverse_layer_idx and reorder_and_upcast_attn flags will apply the training stability
  improvements from Mistral (for PyTorch only).

Write With Transformer is a webapp created and hosted by
Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
This model was contributed by thomwolf. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog on how to Finetune a non-English GPT-2 Model with Hugging Face.
A blog on How to generate text: using different decoding methods for language generation with Transformers with GPT-2.
A blog on Training CodeParrot 🦜 from Scratch, a large GPT-2 model.
A blog on Faster Text Generation with TensorFlow and XLA with GPT-2.
A blog on How to train a Language Model with Megatron-LM with a GPT-2 model.
A notebook on how to finetune GPT2 to generate lyrics in the style of your favorite artist. 🌎
A notebook on how to finetune GPT2 to generate tweets in the style of your favorite Twitter user. 🌎
Causal language modeling chapter of the 🤗 Hugging Face Course.
[GPT2LMHeadModel] is supported by this causal language modeling example script, text generation example script, and notebook.
[TFGPT2LMHeadModel] is supported by this causal language modeling example script and notebook.
[FlaxGPT2LMHeadModel] is supported by this causal language modeling example script and notebook.
Text classification task guide
Token classification task guide
Causal language modeling task guide

GPT2Config
[[autodoc]] GPT2Config
GPT2Tokenizer
[[autodoc]] GPT2Tokenizer
    - save_vocabulary
GPT2TokenizerFast
[[autodoc]] GPT2TokenizerFast
GPT2 specific outputs
[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
[[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
GPT2Model
[[autodoc]] GPT2Model
    - forward
GPT2LMHeadModel
[[autodoc]] GPT2LMHeadModel
    - forward
GPT2DoubleHeadsModel
[[autodoc]] GPT2DoubleHeadsModel
    - forward
GPT2ForQuestionAnswering
[[autodoc]] GPT2ForQuestionAnswering
    - forward
GPT2ForSequenceClassification
[[autodoc]] GPT2ForSequenceClassification
    - forward
GPT2ForTokenClassification
[[autodoc]] GPT2ForTokenClassification
    - forward
TFGPT2Model
[[autodoc]] TFGPT2Model
    - call
TFGPT2LMHeadModel
[[autodoc]] TFGPT2LMHeadModel
    - call
TFGPT2DoubleHeadsModel
[[autodoc]] TFGPT2DoubleHeadsModel
    - call
TFGPT2ForSequenceClassification
[[autodoc]] TFGPT2ForSequenceClassification
    - call
TFSequenceClassifierOutputWithPast
[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutputWithPast
TFGPT2Tokenizer
[[autodoc]] TFGPT2Tokenizer
FlaxGPT2Model
[[autodoc]] FlaxGPT2Model
    - call
FlaxGPT2LMHeadModel
[[autodoc]] FlaxGPT2LMHeadModel
    - call"
"
RetriBERT

This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: pip install -U transformers==4.30.0.

Overview
The RetriBERT model was proposed in the blog post Explain Anything Like I'm Five: A Model for Open Domain Long Form
Question Answering. RetriBERT is a small model that uses either a single or
pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
This model was contributed by yjernite. Code to train and use the model can be
found here.
RetriBertConfig
[[autodoc]] RetriBertConfig
RetriBertTokenizer
[[autodoc]] RetriBertTokenizer
RetriBertTokenizerFast
[[autodoc]] RetriBertTokenizerFast
RetriBertModel
[[autodoc]] RetriBertModel
    - forward"
"
XLM-RoBERTa

Overview
The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's
RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
data.
The abstract from the paper is the following:
This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a
wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on
XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We
also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
will make XLM-R code, data, and models publicly available.
Tips:

XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
  not require lang tensors to understand which language is used, and should be able to determine the correct
  language from the input ids.
Uses RoBERTa tricks on the XLM approach, but does not use the translation language modeling objective. It only uses masked language modeling on sentences coming from one language.
This implementation is the same as RoBERTa. Refer to the documentation of RoBERTa for usage examples
  as well as the information relative to the inputs and outputs.

This model was contributed by stefan-it. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with XLM-RoBERTa. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog post on how to finetune XLM RoBERTa for multiclass classification with Habana Gaudi on AWS
[XLMRobertaForSequenceClassification] is supported by this example script and notebook.
[TFXLMRobertaForSequenceClassification] is supported by this example script and notebook.
[FlaxXLMRobertaForSequenceClassification] is supported by this example script and notebook.
Text classification chapter of the 🤗 Hugging Face Task Guides.
Text classification task guide

[XLMRobertaForTokenClassification] is supported by this example script and notebook.
[TFXLMRobertaForTokenClassification] is supported by this example script and notebook.
[FlaxXLMRobertaForTokenClassification] is supported by this example script.
Token classification chapter of the 🤗 Hugging Face Course.
Token classification task guide

[XLMRobertaForCausalLM] is supported by this example script and notebook.
Causal language modeling chapter of the 🤗 Hugging Face Task Guides.
Causal language modeling task guide

[XLMRobertaForMaskedLM] is supported by this example script and notebook.
[TFXLMRobertaForMaskedLM] is supported by this example script and notebook.
[FlaxXLMRobertaForMaskedLM] is supported by this example script and notebook.
Masked language modeling chapter of the 🤗 Hugging Face Course.
Masked language modeling

[XLMRobertaForQuestionAnswering] is supported by this example script and notebook.
[TFXLMRobertaForQuestionAnswering] is supported by this example script and notebook.
[FlaxXLMRobertaForQuestionAnswering] is supported by this example script.
Question answering chapter of the 🤗 Hugging Face Course.
Question answering task guide

Multiple choice

[XLMRobertaForMultipleChoice] is supported by this example script and notebook.
[TFXLMRobertaForMultipleChoice] is supported by this example script and notebook.
Multiple choice task guide

🚀 Deploy

A blog post on how to Deploy Serverless XLM RoBERTa on AWS Lambda.

XLMRobertaConfig
[[autodoc]] XLMRobertaConfig
XLMRobertaTokenizer
[[autodoc]] XLMRobertaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
XLMRobertaTokenizerFast
[[autodoc]] XLMRobertaTokenizerFast
XLMRobertaModel
[[autodoc]] XLMRobertaModel
    - forward
XLMRobertaForCausalLM
[[autodoc]] XLMRobertaForCausalLM
    - forward
XLMRobertaForMaskedLM
[[autodoc]] XLMRobertaForMaskedLM
    - forward
XLMRobertaForSequenceClassification
[[autodoc]] XLMRobertaForSequenceClassification
    - forward
XLMRobertaForMultipleChoice
[[autodoc]] XLMRobertaForMultipleChoice
    - forward
XLMRobertaForTokenClassification
[[autodoc]] XLMRobertaForTokenClassification
    - forward
XLMRobertaForQuestionAnswering
[[autodoc]] XLMRobertaForQuestionAnswering
    - forward
TFXLMRobertaModel
[[autodoc]] TFXLMRobertaModel
    - call
TFXLMRobertaForCausalLM
[[autodoc]] TFXLMRobertaForCausalLM
    - call
TFXLMRobertaForMaskedLM
[[autodoc]] TFXLMRobertaForMaskedLM
    - call
TFXLMRobertaForSequenceClassification
[[autodoc]] TFXLMRobertaForSequenceClassification
    - call
TFXLMRobertaForMultipleChoice
[[autodoc]] TFXLMRobertaForMultipleChoice
    - call
TFXLMRobertaForTokenClassification
[[autodoc]] TFXLMRobertaForTokenClassification
    - call
TFXLMRobertaForQuestionAnswering
[[autodoc]] TFXLMRobertaForQuestionAnswering
    - call
FlaxXLMRobertaModel
[[autodoc]] FlaxXLMRobertaModel
    - call
FlaxXLMRobertaForCausalLM
[[autodoc]] FlaxXLMRobertaForCausalLM
    - call
FlaxXLMRobertaForMaskedLM
[[autodoc]] FlaxXLMRobertaForMaskedLM
    - call
FlaxXLMRobertaForSequenceClassification
[[autodoc]] FlaxXLMRobertaForSequenceClassification
    - call
FlaxXLMRobertaForMultipleChoice
[[autodoc]] FlaxXLMRobertaForMultipleChoice
    - call
FlaxXLMRobertaForTokenClassification
[[autodoc]] FlaxXLMRobertaForTokenClassification
    - call
FlaxXLMRobertaForQuestionAnswering
[[autodoc]] FlaxXLMRobertaForQuestionAnswering
    - call"
"
ViTMSN
Overview
The ViTMSN model was proposed in Masked Siamese Networks for Label-Efficient Learning by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes,
Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas. The paper presents a joint-embedding architecture to match the prototypes
of masked patches with that of the unmasked patches. With this setup, their method yields excellent performance in the low-shot and extreme low-shot
regimes.
The abstract from the paper is the following:
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our
approach matches the representation of an image view containing randomly masked patches to the representation of the original
unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the
unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures,
while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance,
on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy,
and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark.
Tips:

MSN (masked siamese networks) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training
objective is to match the prototypes assigned to the unmasked views of the images to that of the masked views of the same images.
The authors have only released pre-trained weights of the backbone (ImageNet-1k pre-training). So, to use that on your own image classification dataset,
use the [ViTMSNForImageClassification] class which is initialized from [ViTMSNModel]. Follow
this notebook for a detailed tutorial on fine-tuning.
MSN is particularly useful in the low-shot and extreme low-shot regimes. Notably, it achieves 75.7% top-1 accuracy with only 1% of ImageNet-1K
labels when fine-tuned.

 
 MSN architecture. Taken from the original paper. 
This model was contributed by sayakpaul. The original code can be found here. 
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT MSN.

[ViTMSNForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ViTMSNConfig
[[autodoc]] ViTMSNConfig
ViTMSNModel
[[autodoc]] ViTMSNModel
    - forward
ViTMSNForImageClassification
[[autodoc]] ViTMSNForImageClassification
    - forward"
"
Time Series Transformer

This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a Github Issue.

Overview
The Time Series Transformer model is a vanilla encoder-decoder Transformer for time series forecasting.
Tips:

Similar to other models in the library, [TimeSeriesTransformerModel] is the raw Transformer without any head on top, and [TimeSeriesTransformerForPrediction]
adds a distribution head on top of the former, which can be used for time-series forecasting. Note that this is a so-called probabilistic forecasting model, not a
point forecasting model. This means that the model learns a distribution, from which one can sample. The model doesn't directly output values.
[TimeSeriesTransformerForPrediction] consists of 2 blocks: an encoder, which takes a context_length of time series values as input (called past_values),
and a decoder, which predicts a prediction_length of time series values into the future (called future_values). During training, one needs to provide
pairs of (past_values and future_values) to the model.
In addition to the raw (past_values and future_values), one typically provides additional features to the model. These can be the following:
past_time_features: temporal features which the model will add to past_values. These serve as ""positional encodings"" for the Transformer encoder.
Examples are ""day of the month"", ""month of the year"", etc. as scalar values (and then stacked together as a vector).
e.g. if a given time-series value was obtained on the 11th of August, then one could have [11, 8] as time feature vector (11 being ""day of the month"", 8 being ""month of the year"").
future_time_features: temporal features which the model will add to future_values. These serve as ""positional encodings"" for the Transformer decoder.
Examples are ""day of the month"", ""month of the year"", etc. as scalar values (and then stacked together as a vector).
e.g. if a given time-series value was obtained on the 11th of August, then one could have [11, 8] as time feature vector (11 being ""day of the month"", 8 being ""month of the year"").
static_categorical_features: categorical features which are static over time (i.e., have the same value for all past_values and future_values).
An example here is the store ID or region ID that identifies a given time-series.
Note that these features need to be known for ALL data points (also those in the future).
static_real_features: real-valued features which are static over time (i.e., have the same value for all past_values and future_values).
An example here is the image representation of the product for which you have the time-series values (like the ResNet embedding of a ""shoe"" picture,
if your time-series is about the sales of shoes).
Note that these features need to be known for ALL data points (also those in the future).

The model is trained using ""teacher-forcing"", similar to how a Transformer is trained for machine translation. This means that, during training, one shifts the
future_values one position to the right as input to the decoder, prepended by the last value of past_values. At each time step, the model needs to predict the
next target. So the set-up of training is similar to a GPT model for language, except that there's no notion of decoder_start_token_id (we just use the last value
of the context as initial input for the decoder).
At inference time, we give the final value of the past_values as input to the decoder. Next, we can sample from the model to make a prediction at the next time step,
which is then fed to the decoder in order to make the next prediction (also called autoregressive generation).

This model was contributed by kashif.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

Check out the Time Series Transformer blog-post in HuggingFace blog: Probabilistic Time Series Forecasting with 🤗 Transformers

TimeSeriesTransformerConfig
[[autodoc]] TimeSeriesTransformerConfig
TimeSeriesTransformerModel
[[autodoc]] TimeSeriesTransformerModel
    - forward
TimeSeriesTransformerForPrediction
[[autodoc]] TimeSeriesTransformerForPrediction
    - forward"
"
FLAVA
Overview
The FLAVA model was proposed in FLAVA: A Foundational Language And Vision Alignment Model by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
The paper aims at creating a single unified foundation model which can work across vision, language
as well as vision-and-language multimodal tasks.
The abstract from the paper is the following:
State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety
of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal
(with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising
direction would be to use a single holistic universal model, as a ""foundation"", that targets all modalities
at once -- a true vision and language foundation model should be good at vision tasks, language tasks, and
cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate
impressive performance on a wide range of 35 tasks spanning these target modalities.
This model was contributed by aps. The original code can be found here.
FlavaConfig
[[autodoc]] FlavaConfig
FlavaTextConfig
[[autodoc]] FlavaTextConfig
FlavaImageConfig
[[autodoc]] FlavaImageConfig
FlavaMultimodalConfig
[[autodoc]] FlavaMultimodalConfig
FlavaImageCodebookConfig
[[autodoc]] FlavaImageCodebookConfig
FlavaProcessor
[[autodoc]] FlavaProcessor
FlavaFeatureExtractor
[[autodoc]] FlavaFeatureExtractor
FlavaImageProcessor
[[autodoc]] FlavaImageProcessor
    - preprocess
FlavaForPreTraining
[[autodoc]] FlavaForPreTraining
    - forward
FlavaModel
[[autodoc]] FlavaModel
    - forward
    - get_text_features
    - get_image_features
FlavaImageCodebook
[[autodoc]] FlavaImageCodebook
    - forward
    - get_codebook_indices
    - get_codebook_probs
FlavaTextModel
[[autodoc]] FlavaTextModel
    - forward
FlavaImageModel
[[autodoc]] FlavaImageModel
    - forward
FlavaMultimodalModel
[[autodoc]] FlavaMultimodalModel
    - forward"
"
GPT-NeoX
Overview
We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will
be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge,
the largest dense autoregressive model that has publicly available weights at the time of submission. In this work,
we describe GPT-NeoX-20B's architecture and training and evaluate its performance on a range of language-understanding,
mathematics, and knowledge-based tasks. We find that GPT-NeoX-20B is a particularly powerful few-shot reasoner and
gains far more in performance when evaluated five-shot than similarly sized GPT-3 and FairSeq models. We open-source
the training and evaluation code, as well as the model weights, at https://github.com/EleutherAI/gpt-neox.
Development of the model was led by Sid Black, Stella Biderman and Eric Hallahan, and the model was trained with
generous the support of CoreWeave.
GPT-NeoX-20B was trained with fp16, thus it is recommended to initialize the model as follows:
python
model = GPTNeoXForCausalLM.from_pretrained(""EleutherAI/gpt-neox-20b"").half().cuda()
GPT-NeoX-20B also has a different tokenizer from the one used in GPT-J-6B and GPT-Neo. The new tokenizer allocates
additional tokens to whitespace characters, making the model more suitable for certain tasks like code generation.
Generation
The generate() method can be used to generate text using GPT Neo model.
thon

from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
model = GPTNeoXForCausalLM.from_pretrained(""EleutherAI/gpt-neox-20b"")
tokenizer = GPTNeoXTokenizerFast.from_pretrained(""EleutherAI/gpt-neox-20b"")
prompt = ""GPTNeoX20B is a 20B-parameter autoregressive Transformer model developed by EleutherAI.""
input_ids = tokenizer(prompt, return_tensors=""pt"").input_ids
gen_tokens = model.generate(
     input_ids,
     do_sample=True,
     temperature=0.9,
     max_length=100,
 )
gen_text = tokenizer.batch_decode(gen_tokens)[0]

Documentation resources

Causal language modeling task guide

GPTNeoXConfig
[[autodoc]] GPTNeoXConfig
GPTNeoXTokenizerFast
[[autodoc]] GPTNeoXTokenizerFast
GPTNeoXModel
[[autodoc]] GPTNeoXModel
    - forward
GPTNeoXForCausalLM
[[autodoc]] GPTNeoXForCausalLM
    - forward
GPTNeoXForQuestionAnswering
[[autodoc]] GPTNeoXForQuestionAnswering
    - forward
GPTNeoXForSequenceClassification
[[autodoc]] GPTNeoXForSequenceClassification
    - forward
GPTNeoXForTokenClassification
[[autodoc]] GPTNeoXForTokenClassification
    - forward"
"
BARTpho
Overview
The BARTpho model was proposed in BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
The abstract from the paper is the following:
We present BARTpho with two versions -- BARTpho_word and BARTpho_syllable -- the first public large-scale monolingual
sequence-to-sequence models pre-trained for Vietnamese. Our BARTpho uses the ""large"" architecture and pre-training
scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments
on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, our BARTpho
outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future
research and applications of generative Vietnamese NLP tasks.
Example of use:
thon

import torch
from transformers import AutoModel, AutoTokenizer
bartpho = AutoModel.from_pretrained(""vinai/bartpho-syllable"")
tokenizer = AutoTokenizer.from_pretrained(""vinai/bartpho-syllable"")
line = ""Chúng tôi là những nghiên cứu viên.""
input_ids = tokenizer(line, return_tensors=""pt"")
with torch.no_grad():
     features = bartpho(**input_ids)  # Models outputs are now tuples
With TensorFlow 2.0+:
from transformers import TFAutoModel
bartpho = TFAutoModel.from_pretrained(""vinai/bartpho-syllable"")
input_ids = tokenizer(line, return_tensors=""tf"")
features = bartpho(**input_ids)

Tips:

Following mBART, BARTpho uses the ""large"" architecture of BART with an additional layer-normalization layer on top of
  both the encoder and decoder. Thus, usage examples in the documentation of BART, when adapting to use
  with BARTpho, should be adjusted by replacing the BART-specialized classes with the mBART-specialized counterparts.
  For example:

thon

from transformers import MBartForConditionalGeneration
bartpho = MBartForConditionalGeneration.from_pretrained(""vinai/bartpho-syllable"")
TXT = ""Chúng tôi là  nghiên cứu viên.""
input_ids = tokenizer([TXT], return_tensors=""pt"")[""input_ids""]
logits = bartpho(input_ids).logits
masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
probs = logits[0, masked_index].softmax(dim=0)
values, predictions = probs.topk(5)
print(tokenizer.decode(predictions).split())

This implementation is only for tokenization: ""monolingual_vocab_file"" consists of Vietnamese-specialized types
  extracted from the pre-trained SentencePiece model ""vocab_file"" that is available from the multilingual XLM-RoBERTa.
  Other languages, if employing this pre-trained multilingual SentencePiece model ""vocab_file"" for subword
  segmentation, can reuse BartphoTokenizer with their own language-specialized ""monolingual_vocab_file"".

This model was contributed by dqnguyen. The original code can be found here.
BartphoTokenizer
[[autodoc]] BartphoTokenizer"
"
Audio Spectrogram Transformer
Overview
The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass.
The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results
for audio classification.
The abstract from the paper is the following:
In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2.
Tips:

When fine-tuning the Audio Spectrogram Transformer (AST) on your own dataset, it's recommended to take care of the input normalization (to make
sure the input has mean of 0 and std of 0.5). [ASTFeatureExtractor] takes care of this. Note that it uses the AudioSet
mean and std by default. You can check ast/src/get_norm_stats.py to see how
the authors compute the stats for a downstream dataset.
Note that the AST needs a low learning rate (the authors use a 10 times smaller learning rate compared to their CNN model proposed in the
PSLA paper) and converges quickly, so please search for a suitable learning rate and learning rate scheduler for your task.

 Audio pectrogram Transformer architecture. Taken from the original paper.
This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with the Audio Spectrogram Transformer.

A notebook illustrating inference with AST for audio classification can be found here.
[ASTForAudioClassification] is supported by this example script and notebook.
See also: Audio classification.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ASTConfig
[[autodoc]] ASTConfig
ASTFeatureExtractor
[[autodoc]] ASTFeatureExtractor
    - call
ASTModel
[[autodoc]] ASTModel
    - forward
ASTForAudioClassification
[[autodoc]] ASTForAudioClassification
    - forward"
"
mT5

Overview
The mT5 model was presented in mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya
Siddhant, Aditya Barua, Colin Raffel.
The abstract from the paper is the following:
The recent ""Text-to-Text Transfer Transformer"" (T5) leveraged a unified text-to-text format and scale to attain
state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a
multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail
the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual
benchmarks. We also describe a simple technique to prevent ""accidental translation"" in the zero-shot setting, where a
generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model
checkpoints used in this work are publicly available.
Note: mT5 was only pre-trained on mC4 excluding any supervised training.
Therefore, this model has to be fine-tuned before it is usable on a downstream task, unlike the original T5 model.
Since mT5 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.
Google has released the following variants:

google/mt5-small

google/mt5-base

google/mt5-large

google/mt5-xl

google/mt5-xxl.

This model was contributed by patrickvonplaten. The original code can be
found here.
Documentation resources

Translation task guide
Summarization task guide

MT5Config
[[autodoc]] MT5Config
MT5Tokenizer
[[autodoc]] MT5Tokenizer
See [T5Tokenizer] for all details.
MT5TokenizerFast
[[autodoc]] MT5TokenizerFast
See [T5TokenizerFast] for all details.
MT5Model
[[autodoc]] MT5Model
MT5ForConditionalGeneration
[[autodoc]] MT5ForConditionalGeneration
MT5EncoderModel
[[autodoc]] MT5EncoderModel
MT5ForQuestionAnswering
[[autodoc]] MT5ForQuestionAnswering
TFMT5Model
[[autodoc]] TFMT5Model
TFMT5ForConditionalGeneration
[[autodoc]] TFMT5ForConditionalGeneration
TFMT5EncoderModel
[[autodoc]] TFMT5EncoderModel
FlaxMT5Model
[[autodoc]] FlaxMT5Model
FlaxMT5ForConditionalGeneration
[[autodoc]] FlaxMT5ForConditionalGeneration
FlaxMT5EncoderModel
[[autodoc]] FlaxMT5EncoderModel"
"
Longformer

Overview
The Longformer model was presented in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
The abstract from the paper is the following:
Transformer-based models are unable to process long sequences due to their self-attention operation, which scales
quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention
mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or
longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local
windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we
evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In
contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our
pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
WikiHop and TriviaQA.
Tips:

Since the Longformer is based on RoBERTa, it doesn't have token_type_ids. You don't need to indicate which
  token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or
  </s>).
A transformer model replacing the attention matrices by sparse matrices to go faster. Often, the local context (e.g., what are the two tokens left and right?) is enough to take action for a given token. Some preselected input tokens are still given global attention, but the attention matrix has way less parameters, resulting in a speed-up. See the local attention section for more information.

This model was contributed by beltagy. The Authors' code can be found here.
Longformer Self Attention
Longformer self attention employs self attention on both a ""local"" context and a ""global"" context. Most tokens only
attend ""locally"" to each other meaning that each token attends to its \(\frac{1}{2} w\) previous tokens and
\(\frac{1}{2} w\) succeeding tokens with \(w\) being the window length as defined in
config.attention_window. Note that config.attention_window can be of type List to define a
different \(w\) for each layer. A selected few tokens attend ""globally"" to all other tokens, as it is
conventionally done for all tokens in BertSelfAttention.
Note that ""locally"" and ""globally"" attending tokens are projected by different query, key and value matrices. Also note
that every ""locally"" attending token not only attends to tokens within its window \(w\), but also to all ""globally""
attending tokens so that global attention is symmetric.
The user can define which tokens attend ""locally"" and which tokens attend ""globally"" by setting the tensor
global_attention_mask at run-time appropriately. All Longformer models employ the following logic for
global_attention_mask:

0: the token attends ""locally"",
1: the token attends ""globally"".

For more information please also refer to [~LongformerModel.forward] method.
Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually
represents the memory and time bottleneck, can be reduced from \(\mathcal{O}(n_s \times n_s)\) to
\(\mathcal{O}(n_s \times w)\), with \(n_s\) being the sequence length and \(w\) being the average window
size. It is assumed that the number of ""globally"" attending tokens is insignificant as compared to the number of
""locally"" attending tokens.
For more information, please refer to the official paper.
Training
[LongformerForMaskedLM] is trained the exact same way [RobertaForMaskedLM] is
trained and should be used as follows:
thon
input_ids = tokenizer.encode(""This is a sentence from [MASK] training data"", return_tensors=""pt"")
mlm_labels = tokenizer.encode(""This is a sentence from the training data"", return_tensors=""pt"")
loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]

Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

LongformerConfig
[[autodoc]] LongformerConfig
LongformerTokenizer
[[autodoc]] LongformerTokenizer
LongformerTokenizerFast
[[autodoc]] LongformerTokenizerFast
Longformer specific outputs
[[autodoc]] models.longformer.modeling_longformer.LongformerBaseModelOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling
[[autodoc]] models.longformer.modeling_longformer.LongformerMaskedLMOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerSequenceClassifierOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerTokenClassifierOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutputWithPooling
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput
LongformerModel
[[autodoc]] LongformerModel
    - forward
LongformerForMaskedLM
[[autodoc]] LongformerForMaskedLM
    - forward
LongformerForSequenceClassification
[[autodoc]] LongformerForSequenceClassification
    - forward
LongformerForMultipleChoice
[[autodoc]] LongformerForMultipleChoice
    - forward
LongformerForTokenClassification
[[autodoc]] LongformerForTokenClassification
    - forward
LongformerForQuestionAnswering
[[autodoc]] LongformerForQuestionAnswering
    - forward
TFLongformerModel
[[autodoc]] TFLongformerModel
    - call
TFLongformerForMaskedLM
[[autodoc]] TFLongformerForMaskedLM
    - call
TFLongformerForQuestionAnswering
[[autodoc]] TFLongformerForQuestionAnswering
    - call
TFLongformerForSequenceClassification
[[autodoc]] TFLongformerForSequenceClassification
    - call
TFLongformerForTokenClassification
[[autodoc]] TFLongformerForTokenClassification
    - call
TFLongformerForMultipleChoice
[[autodoc]] TFLongformerForMultipleChoice
    - call"
"
ErnieM
Overview
The ErnieM model was proposed in ERNIE-M: Enhanced Multilingual Representation by Aligning
Cross-lingual Semantics with Monolingual Corpora  by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun,
Hao Tian, Hua Wu, Haifeng Wang.
The abstract from the paper is the following:
Recent studies have demonstrated that pre-trained cross-lingual models achieve impressive performance in downstream cross-lingual tasks. This improvement benefits from learning a large amount of monolingual and parallel corpora. Although it is generally acknowledged that parallel corpora are critical for improving the model performance, existing methods are often constrained by the size of parallel corpora, especially for lowresource languages. In this paper, we propose ERNIE-M, a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. Our key insight is to integrate back-translation into the pre-training process. We generate pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that ERNIE-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks.
Tips:

Ernie-M is a BERT-like model so it is a stacked Transformer Encoder.
Instead of using MaskedLM for pretraining (like BERT) the authors used two novel techniques: Cross-attention Masked Language Modeling and Back-translation Masked Language Modeling. For now these two LMHead objectives are not implemented here.
It is a multilingual language model.
Next Sentence Prediction was not used in pretraining process.

This model was contributed by Susnato Dhar. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Multiple choice task guide

ErnieMConfig
[[autodoc]] ErnieMConfig
ErnieMTokenizer
[[autodoc]] ErnieMTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
ErnieMModel
[[autodoc]] ErnieMModel
    - forward
ErnieMForSequenceClassification
[[autodoc]] ErnieMForSequenceClassification
    - forward
ErnieMForMultipleChoice
[[autodoc]] ErnieMForMultipleChoice
    - forward
ErnieMForTokenClassification
[[autodoc]] ErnieMForTokenClassification
    - forward
ErnieMForQuestionAnswering
[[autodoc]] ErnieMForQuestionAnswering
    - forward
ErnieMForInformationExtraction
[[autodoc]] ErnieMForInformationExtraction
    - forward"
"
X-CLIP
Overview
The X-CLIP model was proposed in Expanding Language-Image Pretrained Models for General Video Recognition by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
X-CLIP is a minimal extension of CLIP for video. The model consists of a text encoder, a cross-frame vision encoder, a multi-frame integration Transformer, and a video-specific prompt generator.
The abstract from the paper is the following:
Contrastive language-image pretraining has shown great success in learning visual-textual joint representation from web-scale data, demonstrating remarkable ""zero-shot"" generalization ability for various image tasks. However, how to effectively expand such new language-image pretraining methods to video domains is still an open problem. In this work, we present a simple yet effective approach that adapts the pretrained language-image models to video recognition directly, instead of pretraining a new model from scratch. More concretely, to capture the long-range dependencies of frames along the temporal dimension, we propose a cross-frame attention mechanism that explicitly exchanges information across frames. Such module is lightweight and can be plugged into pretrained language-image models seamlessly. Moreover, we propose a video-specific prompting scheme, which leverages video content information for generating discriminative textual prompts. Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios. In particular, under fully-supervised settings, our approach achieves a top-1 accuracy of 87.1% on Kinectics-400, while using 12 times fewer FLOPs compared with Swin-L and ViViT-H. In zero-shot experiments, our approach surpasses the current state-of-the-art methods by +7.6% and +14.9% in terms of top-1 accuracy under two popular protocols. In few-shot scenarios, our approach outperforms previous best methods by +32.1% and +23.1% when the labeled data is extremely limited.
Tips:

Usage of X-CLIP is identical to CLIP.

 
 X-CLIP architecture. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with X-CLIP.

Demo notebooks for X-CLIP can be found here.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
XCLIPProcessor
[[autodoc]] XCLIPProcessor
XCLIPConfig
[[autodoc]] XCLIPConfig
    - from_text_vision_configs
XCLIPTextConfig
[[autodoc]] XCLIPTextConfig
XCLIPVisionConfig
[[autodoc]] XCLIPVisionConfig
XCLIPModel
[[autodoc]] XCLIPModel
    - forward
    - get_text_features
    - get_video_features
XCLIPTextModel
[[autodoc]] XCLIPTextModel
    - forward
XCLIPVisionModel
[[autodoc]] XCLIPVisionModel
    - forward"
"
Whisper
Overview
The Whisper model was proposed in Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
The abstract from the paper is the following:
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zeroshot transfer setting without the need for any finetuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.
Tips:

The model usually performs well without requiring any finetuning.
The architecture follows a classic encoder-decoder architecture, which means that it relies on the [~generation.GenerationMixin.generate] function for inference.
Inference is currently only implemented for short-form i.e. audio is pre-segmented into <=30s segments. Long-form (including timestamps) will be implemented in a future release.
One can use [WhisperProcessor] to prepare audio for the model, and decode the predicted ID's back into text.

This model was contributed by Arthur Zucker. The Tensorflow version of this model was contributed by amyeroberts.
The original code can be found here.
WhisperConfig
[[autodoc]] WhisperConfig
WhisperTokenizer
[[autodoc]] WhisperTokenizer
    - set_prefix_tokens
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
WhisperTokenizerFast
[[autodoc]] WhisperTokenizerFast
    - set_prefix_tokens
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
WhisperFeatureExtractor
[[autodoc]] WhisperFeatureExtractor
    - call
WhisperProcessor
[[autodoc]] WhisperProcessor
    - call
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
WhisperModel
[[autodoc]] WhisperModel
    - forward
    - _mask_input_features
WhisperForConditionalGeneration
[[autodoc]] WhisperForConditionalGeneration
    - forward
WhisperForAudioClassification
[[autodoc]] WhisperForAudioClassification
    - forward
TFWhisperModel
[[autodoc]] TFWhisperModel
    - call
TFWhisperForConditionalGeneration
[[autodoc]] TFWhisperForConditionalGeneration
    - call
FlaxWhisperModel
[[autodoc]] FlaxWhisperModel
    - call
FlaxWhisperForConditionalGeneration
[[autodoc]] FlaxWhisperForConditionalGeneration
    - call
FlaxWhisperForAudioClassification
[[autodoc]] FlaxWhisperForAudioClassification
    - call"
"
BLIP
Overview
The BLIP model was proposed in BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
BLIP is a model that is able to perform various multi-modal tasks including
- Visual Question Answering 
- Image-Text retrieval (Image-text matching)
- Image Captioning
The abstract from the paper is the following:
Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. 
However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.

This model was contributed by ybelkada.
The original code can be found here.
Resources

Jupyter notebook on how to fine-tune BLIP for image captioning on a custom dataset

BlipConfig
[[autodoc]] BlipConfig
    - from_text_vision_configs
BlipTextConfig
[[autodoc]] BlipTextConfig
BlipVisionConfig
[[autodoc]] BlipVisionConfig
BlipProcessor
[[autodoc]] BlipProcessor
BlipImageProcessor
[[autodoc]] BlipImageProcessor
    - preprocess
BlipModel
[[autodoc]] BlipModel
    - forward
    - get_text_features
    - get_image_features
BlipTextModel
[[autodoc]] BlipTextModel
    - forward
BlipVisionModel
[[autodoc]] BlipVisionModel
    - forward
BlipForConditionalGeneration
[[autodoc]] BlipForConditionalGeneration
    - forward
BlipForImageTextRetrieval
[[autodoc]] BlipForImageTextRetrieval
    - forward
BlipForQuestionAnswering
[[autodoc]] BlipForQuestionAnswering
    - forward
TFBlipModel
[[autodoc]] TFBlipModel
    - call
    - get_text_features
    - get_image_features
TFBlipTextModel
[[autodoc]] TFBlipTextModel
    - call
TFBlipVisionModel
[[autodoc]] TFBlipVisionModel
    - call
TFBlipForConditionalGeneration
[[autodoc]] TFBlipForConditionalGeneration
    - call
TFBlipForImageTextRetrieval
[[autodoc]] TFBlipForImageTextRetrieval
    - call
TFBlipForQuestionAnswering
[[autodoc]] TFBlipForQuestionAnswering
    - call"
"
Decision Transformer
Overview
The Decision Transformer model was proposed in Decision Transformer: Reinforcement Learning via Sequence Modeling
by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
The abstract from the paper is the following:
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. 
This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances
 in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that 
 casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or 
 compute policy gradients, Decision Transformer simply outputs the optimal actions by leveraging a causally masked 
 Transformer. By conditioning an autoregressive model on the desired return (reward), past states, and actions, our 
 Decision Transformer model can generate future actions that achieve the desired return. Despite its simplicity, 
 Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on 
 Atari, OpenAI Gym, and Key-to-Door tasks.
Tips:
This version of the model is for tasks where the state is a vector, image-based states will come soon.
This model was contributed by edbeeching. The original code can be found here.
DecisionTransformerConfig
[[autodoc]] DecisionTransformerConfig
DecisionTransformerGPT2Model
[[autodoc]] DecisionTransformerGPT2Model
    - forward
DecisionTransformerModel
[[autodoc]] DecisionTransformerModel
    - forward"
"
LeViT
Overview
The LeViT model was proposed in LeViT: Introducing Convolutions to Vision Transformers by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze. LeViT improves the Vision Transformer (ViT) in performance and efficiency by a few architectural differences such as activation maps with decreasing resolutions in Transformers and the introduction of an attention bias to integrate positional information.
The abstract from the paper is the following:
*We design a family of image classification architectures that optimize the trade-off between accuracy
and efficiency in a high-speed regime. Our work exploits recent findings in attention-based architectures,
which are competitive on highly parallel processing hardware. We revisit principles from the extensive
literature on convolutional neural networks to apply them to transformers, in particular activation maps
with decreasing resolutions. We also introduce the attention bias, a new way to integrate positional information
in vision transformers. As a result, we propose LeVIT: a hybrid neural network for fast inference image classification.
We consider different measures of efficiency on different hardware platforms, so as to best reflect a wide range of
application scenarios. Our extensive experiments empirically validate our technical choices and show they are suitable
to most architectures. Overall, LeViT significantly outperforms existing convnets and vision transformers with respect
to the speed/accuracy tradeoff. For example, at 80% ImageNet top-1 accuracy, LeViT is 5 times faster than EfficientNet on CPU. *

 LeViT Architecture. Taken from the original paper.
Tips:

Compared to ViT, LeViT models use an additional distillation head to effectively learn from a teacher (which, in the LeViT paper, is a ResNet like-model). The distillation head is learned through backpropagation under supervision of a ResNet like-model. They also draw inspiration from convolution neural networks to use activation maps with decreasing resolutions to increase the efficiency.
There are 2 ways to fine-tune distilled models, either (1) in a classic way, by only placing a prediction head on top
  of the final hidden state and not using the distillation head, or (2) by placing both a prediction head and distillation
  head on top of the final hidden state. In that case, the prediction head is trained using regular cross-entropy between
  the prediction of the head and the ground-truth label, while the distillation prediction head is trained using hard distillation
  (cross-entropy between the prediction of the distillation head and the label predicted by the teacher). At inference time,
  one takes the average prediction between both heads as final prediction. (2) is also called ""fine-tuning with distillation"",
  because one relies on a teacher that has already been fine-tuned on the downstream dataset. In terms of models, (1) corresponds
  to [LevitForImageClassification] and (2) corresponds to [LevitForImageClassificationWithTeacher].
All released checkpoints were pre-trained and fine-tuned on  ImageNet-1k
  (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes). only. No external data was used. This is in
  contrast with the original ViT model, which used external data like the JFT-300M dataset/Imagenet-21k for
  pre-training.
The authors of LeViT released 5 trained LeViT models, which you can directly plug into [LevitModel] or [LevitForImageClassification].
  Techniques like data augmentation, optimization, and regularization were used in order to simulate training on a much larger dataset
  (while only using ImageNet-1k for pre-training). The 5 variants available are (all trained on images of size 224x224):
  facebook/levit-128S, facebook/levit-128, facebook/levit-192, facebook/levit-256 and
  facebook/levit-384. Note that one should use [LevitImageProcessor] in order to
  prepare images for the model.
[LevitForImageClassificationWithTeacher] currently supports only inference and not training or fine-tuning.
You can check out demo notebooks regarding inference as well as fine-tuning on custom data here
  (you can just replace [ViTFeatureExtractor] by [LevitImageProcessor] and [ViTForImageClassification] by [LevitForImageClassification] or [LevitForImageClassificationWithTeacher]).

This model was contributed by anugunj. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LeViT.

[LevitForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
LevitConfig
[[autodoc]] LevitConfig
LevitFeatureExtractor
[[autodoc]] LevitFeatureExtractor
    - call
LevitImageProcessor
[[autodoc]] LevitImageProcessor
    - preprocess
LevitModel
[[autodoc]] LevitModel
    - forward
LevitForImageClassification
[[autodoc]] LevitForImageClassification
    - forward
LevitForImageClassificationWithTeacher
[[autodoc]] LevitForImageClassificationWithTeacher
    - forward"
"
Speech2Text
Overview
The Speech2Text model was proposed in fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. It's a
transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech
Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are
fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the
transcripts/translations autoregressively. Speech2Text has been fine-tuned on several datasets for ASR and ST:
LibriSpeech, CoVoST 2, MuST-C.
This model was contributed by valhalla. The original code can be found here.
Inference
Speech2Text is a speech model that accepts a float tensor of log-mel filter-bank features extracted from the speech
signal. It's a transformer-based seq2seq model, so the transcripts/translations are generated autoregressively. The
generate() method can be used for inference.
The [Speech2TextFeatureExtractor] class is responsible for extracting the log-mel filter-bank
features. The [Speech2TextProcessor] wraps [Speech2TextFeatureExtractor] and
[Speech2TextTokenizer] into a single instance to both extract the input features and decode the
predicted token ids.
The feature extractor depends on torchaudio and the tokenizer depends on sentencepiece so be sure to
install those packages before running the examples. You could either install those as extra speech dependencies with
pip install transformers""[speech, sentencepiece]"" or install the packages separately with pip install torchaudio sentencepiece. Also torchaudio requires the development version of the libsndfile package which can be installed via a system package manager. On Ubuntu it can
be installed as follows: apt install libsndfile1-dev

ASR and Speech Translation

thon

import torch
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from datasets import load_dataset
model = Speech2TextForConditionalGeneration.from_pretrained(""facebook/s2t-small-librispeech-asr"")
processor = Speech2TextProcessor.from_pretrained(""facebook/s2t-small-librispeech-asr"")
ds = load_dataset(""hf-internal-testing/librispeech_asr_demo"", ""clean"", split=""validation"")
inputs = processor(ds[0][""audio""][""array""], sampling_rate=ds[0][""audio""][""sampling_rate""], return_tensors=""pt"")
generated_ids = model.generate(inputs[""input_features""], attention_mask=inputs[""attention_mask""])
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
transcription
['mister quilter is the apostle of the middle classes and we are glad to welcome his gospel']

Multilingual speech translation

For multilingual speech translation models, eos_token_id is used as the decoder_start_token_id and
  the target language id is forced as the first generated token. To force the target language id as the first
  generated token, pass the forced_bos_token_id parameter to the generate() method. The following
  example shows how to transate English speech to French text using the facebook/s2t-medium-mustc-multilingual-st
  checkpoint.
thon

import torch
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from datasets import load_dataset
model = Speech2TextForConditionalGeneration.from_pretrained(""facebook/s2t-medium-mustc-multilingual-st"")
processor = Speech2TextProcessor.from_pretrained(""facebook/s2t-medium-mustc-multilingual-st"")
ds = load_dataset(""hf-internal-testing/librispeech_asr_demo"", ""clean"", split=""validation"")
inputs = processor(ds[0][""audio""][""array""], sampling_rate=ds[0][""audio""][""sampling_rate""], return_tensors=""pt"")
generated_ids = model.generate(
     inputs[""input_features""],
     attention_mask=inputs[""attention_mask""],
     forced_bos_token_id=processor.tokenizer.lang_code_to_id[""fr""],
 )
translation = processor.batch_decode(generated_ids, skip_special_tokens=True)
translation
[""(Vidéo) Si M. Kilder est l'apossible des classes moyennes, et nous sommes heureux d'être accueillis dans son évangile.""]

See the model hub to look for Speech2Text checkpoints.
Speech2TextConfig
[[autodoc]] Speech2TextConfig
Speech2TextTokenizer
[[autodoc]] Speech2TextTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
Speech2TextFeatureExtractor
[[autodoc]] Speech2TextFeatureExtractor
    - call
Speech2TextProcessor
[[autodoc]] Speech2TextProcessor
    - call
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
Speech2TextModel
[[autodoc]] Speech2TextModel
    - forward
Speech2TextForConditionalGeneration
[[autodoc]] Speech2TextForConditionalGeneration
    - forward
TFSpeech2TextModel
[[autodoc]] TFSpeech2TextModel
    - call
TFSpeech2TextForConditionalGeneration
[[autodoc]] TFSpeech2TextForConditionalGeneration
    - call"
"
TrOCR
Overview
The TrOCR model was proposed in TrOCR: Transformer-based Optical Character Recognition with Pre-trained
Models by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
Zhoujun Li, Furu Wei. TrOCR consists of an image Transformer encoder and an autoregressive text Transformer decoder to
perform optical character recognition (OCR).
The abstract from the paper is the following:
Text recognition is a long-standing research problem for document digitalization. Existing approaches for text recognition
are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language
model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end
text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the
Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but
effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments
show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition
tasks.

 TrOCR architecture. Taken from the original paper. 
Please refer to the [VisionEncoderDecoder] class on how to use this model.
This model was contributed by nielsr. The original code can be found
here.
Tips:

The quickest way to get started with TrOCR is by checking the tutorial
  notebooks, which show how to use the model
  at inference time as well as fine-tuning on custom data.
TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results
  on both printed (e.g. the SROIE dataset and handwritten (e.g. the IAM
  Handwriting dataset text recognition tasks. For more
  information, see the official models.
TrOCR is always used within the VisionEncoderDecoder framework.

Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with TrOCR. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog post on Accelerating Document AI with TrOCR.
A blog post on how to Document AI with TrOCR.
A notebook on how to finetune TrOCR on IAM Handwriting Database using Seq2SeqTrainer.
A notebook on inference with TrOCR and Gradio demo.
A notebook on finetune TrOCR on the IAM Handwriting Database using native PyTorch.
A notebook on evaluating TrOCR on the IAM test set.

Casual language modeling task guide.

⚡️ Inference

An interactive-demo on TrOCR handwritten character recognition.

Inference
TrOCR's [VisionEncoderDecoder] model accepts images as input and makes use of
[~generation.GenerationMixin.generate] to autoregressively generate text given the input image.
The [ViTImageProcessor/DeiTImageProcessor] class is responsible for preprocessing the input image and
[RobertaTokenizer/XLMRobertaTokenizer] decodes the generated target tokens to the target string. The
[TrOCRProcessor] wraps [ViTImageProcessor/DeiTImageProcessor] and [RobertaTokenizer/XLMRobertaTokenizer]
into a single instance to both extract the input features and decode the predicted token ids.

Step-by-step Optical Character Recognition (OCR)

``` py

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import requests
from PIL import Image
processor = TrOCRProcessor.from_pretrained(""microsoft/trocr-base-handwritten"")
model = VisionEncoderDecoderModel.from_pretrained(""microsoft/trocr-base-handwritten"")
load image from the IAM dataset
url = ""https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg""
image = Image.open(requests.get(url, stream=True).raw).convert(""RGB"")
pixel_values = processor(image, return_tensors=""pt"").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

See the model hub to look for TrOCR checkpoints.
TrOCRConfig
[[autodoc]] TrOCRConfig
TrOCRProcessor
[[autodoc]] TrOCRProcessor
    - call
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
TrOCRForCausalLM
[[autodoc]] TrOCRForCausalLM
     - forward"
"
OpenAI GPT

Overview
OpenAI GPT model was proposed in Improving Language Understanding by Generative Pre-Training
by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional) transformer
pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book Corpus.
The abstract from the paper is the following:
Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering,
semantic similarity assessment, and document classification. Although large unlabeled text corpora are abundant,
labeled data for learning these specific tasks is scarce, making it challenging for discriminatively trained models to
perform adequately. We demonstrate that large gains on these tasks can be realized by generative pretraining of a
language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task. In
contrast to previous approaches, we make use of task-aware input transformations during fine-tuning to achieve
effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our
approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms
discriminatively trained models that use architectures specifically crafted for each task, significantly improving upon
the state of the art in 9 out of the 12 tasks studied.
Tips:

GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
GPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
  observed in the run_generation.py example script.

Write With Transformer is a webapp created and hosted by Hugging Face
showcasing the generative capabilities of several models. GPT is one of them.
This model was contributed by thomwolf. The original code can be found here.
Note:
If you want to reproduce the original tokenization process of the OpenAI GPT paper, you will need to install ftfy
and SpaCy:

pip install spacy ftfy==4.4.3
python -m spacy download en
If you don't install ftfy and SpaCy, the [OpenAIGPTTokenizer] will default to tokenize
using BERT's BasicTokenizer followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with OpenAI GPT. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog post on outperforming OpenAI GPT-3 with SetFit for text-classification.
See also: Text classification task guide

A blog on how to Finetune a non-English GPT-2 Model with Hugging Face.
A blog on How to generate text: using different decoding methods for language generation with Transformers with GPT-2.
A blog on Training CodeParrot 🦜 from Scratch, a large GPT-2 model.
A blog on Faster Text Generation with TensorFlow and XLA with GPT-2.
A blog on How to train a Language Model with Megatron-LM with a GPT-2 model.
A notebook on how to finetune GPT2 to generate lyrics in the style of your favorite artist. 🌎
A notebook on how to finetune GPT2 to generate tweets in the style of your favorite Twitter user. 🌎
Causal language modeling chapter of the 🤗 Hugging Face Course.
[OpenAIGPTLMHeadModel] is supported by this causal language modeling example script, text generation example script and notebook.
[TFOpenAIGPTLMHeadModel] is supported by this causal language modeling example script and notebook.
See also: Causal language modeling task guide

A course material on Byte-Pair Encoding tokenization.

OpenAIGPTConfig
[[autodoc]] OpenAIGPTConfig
OpenAIGPTTokenizer
[[autodoc]] OpenAIGPTTokenizer
    - save_vocabulary
OpenAIGPTTokenizerFast
[[autodoc]] OpenAIGPTTokenizerFast
OpenAI specific outputs
[[autodoc]] models.openai.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
[[autodoc]] models.openai.modeling_tf_openai.TFOpenAIGPTDoubleHeadsModelOutput
OpenAIGPTModel
[[autodoc]] OpenAIGPTModel
    - forward
OpenAIGPTLMHeadModel
[[autodoc]] OpenAIGPTLMHeadModel
    - forward
OpenAIGPTDoubleHeadsModel
[[autodoc]] OpenAIGPTDoubleHeadsModel
    - forward
OpenAIGPTForSequenceClassification
[[autodoc]] OpenAIGPTForSequenceClassification
    - forward
TFOpenAIGPTModel
[[autodoc]] TFOpenAIGPTModel
    - call
TFOpenAIGPTLMHeadModel
[[autodoc]] TFOpenAIGPTLMHeadModel
    - call
TFOpenAIGPTDoubleHeadsModel
[[autodoc]] TFOpenAIGPTDoubleHeadsModel
    - call
TFOpenAIGPTForSequenceClassification
[[autodoc]] TFOpenAIGPTForSequenceClassification
    - call"
"
Speech2Text2
Overview
The Speech2Text2 model is used together with Wav2Vec2 for Speech Translation models proposed in
Large-Scale Self- and Semi-Supervised Learning for Speech Translation by
Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
Speech2Text2 is a decoder-only transformer model that can be used with any speech encoder-only, such as
Wav2Vec2 or HuBERT for Speech-to-Text tasks. Please refer to the
SpeechEncoderDecoder class on how to combine Speech2Text2 with any speech encoder-only
model.
This model was contributed by Patrick von Platen.
The original code can be found here.
Tips:

Speech2Text2 achieves state-of-the-art results on the CoVoST Speech Translation dataset. For more information, see
  the official models .
Speech2Text2 is always used within the SpeechEncoderDecoder framework.
Speech2Text2's tokenizer is based on fastBPE.

Inference
Speech2Text2's [SpeechEncoderDecoderModel] model accepts raw waveform input values from speech and
makes use of [~generation.GenerationMixin.generate] to translate the input speech
autoregressively to the target language.
The [Wav2Vec2FeatureExtractor] class is responsible for preprocessing the input speech and
[Speech2Text2Tokenizer] decodes the generated target tokens to the target string. The
[Speech2Text2Processor] wraps [Wav2Vec2FeatureExtractor] and
[Speech2Text2Tokenizer] into a single instance to both extract the input features and decode the
predicted token ids.

Step-by-step Speech Translation

thon

import torch
from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
from datasets import load_dataset
import soundfile as sf
model = SpeechEncoderDecoderModel.from_pretrained(""facebook/s2t-wav2vec2-large-en-de"")
processor = Speech2Text2Processor.from_pretrained(""facebook/s2t-wav2vec2-large-en-de"")
def map_to_array(batch):
     speech, _ = sf.read(batch[""file""])
     batch[""speech""] = speech
     return batch
ds = load_dataset(""hf-internal-testing/librispeech_asr_dummy"", ""clean"", split=""validation"")
ds = ds.map(map_to_array)
inputs = processor(ds[""speech""][0], sampling_rate=16_000, return_tensors=""pt"")
generated_ids = model.generate(inputs=inputs[""input_values""], attention_mask=inputs[""attention_mask""])
transcription = processor.batch_decode(generated_ids)

Speech Translation via Pipelines

The automatic speech recognition pipeline can also be used to translate speech in just a couple lines of code
thon

from datasets import load_dataset
from transformers import pipeline
librispeech_en = load_dataset(""hf-internal-testing/librispeech_asr_dummy"", ""clean"", split=""validation"")
asr = pipeline(
     ""automatic-speech-recognition"",
     model=""facebook/s2t-wav2vec2-large-en-de"",
     feature_extractor=""facebook/s2t-wav2vec2-large-en-de"",
 )
translation_de = asr(librispeech_en[0][""file""])

See model hub to look for Speech2Text2 checkpoints.
Documentation resources

Causal language modeling task guide

Speech2Text2Config
[[autodoc]] Speech2Text2Config
Speech2TextTokenizer
[[autodoc]] Speech2Text2Tokenizer
    - batch_decode
    - decode
    - save_vocabulary
Speech2Text2Processor
[[autodoc]] Speech2Text2Processor
    - call
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
Speech2Text2ForCausalLM
[[autodoc]] Speech2Text2ForCausalLM
    - forward"
"
Perceiver
Overview
The Perceiver IO model was proposed in Perceiver IO: A General Architecture for Structured Inputs &
Outputs by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch,
Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M.
Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
Perceiver IO is a generalization of Perceiver to handle arbitrary outputs in
addition to arbitrary inputs. The original Perceiver only produced a single classification label. In addition to
classification labels, Perceiver IO can produce (for example) language, optical flow, and multimodal videos with audio.
This is done using the same building blocks as the original Perceiver. The computational complexity of Perceiver IO is
linear in the input and output size and the bulk of the processing occurs in the latent space, allowing us to process
inputs and outputs that are much larger than can be handled by standard Transformers. This means, for example,
Perceiver IO can do BERT-style masked language modeling directly using bytes instead of tokenized inputs.
The abstract from the paper is the following:
The recently-proposed Perceiver model obtains good results on several domains (images, audio, multimodal, point
clouds) while scaling linearly in compute and memory with the input size. While the Perceiver supports many kinds of
inputs, it can only produce very simple outputs such as class scores. Perceiver IO overcomes this limitation without
sacrificing the original's appealing properties by learning to flexibly query the model's latent space to produce
outputs of arbitrary size and semantics. Perceiver IO still decouples model depth from data size and still scales
linearly with data size, but now with respect to both input and output sizes. The full Perceiver IO model achieves
strong results on tasks with highly structured output spaces, such as natural language and visual understanding,
StarCraft II, and multi-task and multi-modal domains. As highlights, Perceiver IO matches a Transformer-based BERT
baseline on the GLUE language benchmark without the need for input tokenization and achieves state-of-the-art
performance on Sintel optical flow estimation.
Here's a TLDR explaining how Perceiver works:
The main problem with the self-attention mechanism of the Transformer is that the time and memory requirements scale
quadratically with the sequence length. Hence, models like BERT and RoBERTa are limited to a max sequence length of 512
tokens. Perceiver aims to solve this issue by, instead of performing self-attention on the inputs, perform it on a set
of latent variables, and only use the inputs for cross-attention. In this way, the time and memory requirements don't
depend on the length of the inputs anymore, as one uses a fixed amount of latent variables, like 256 or 512. These are
randomly initialized, after which they are trained end-to-end using backpropagation.
Internally, [PerceiverModel] will create the latents, which is a tensor of shape (batch_size, num_latents,
d_latents). One must provide inputs (which could be text, images, audio, you name it!) to the model, which it will
use to perform cross-attention with the latents. The output of the Perceiver encoder is a tensor of the same shape. One
can then, similar to BERT, convert the last hidden states of the latents to classification logits by averaging along
the sequence dimension, and placing a linear layer on top of that to project the d_latents to num_labels.
This was the idea of the original Perceiver paper. However, it could only output classification logits. In a follow-up
work, PerceiverIO, they generalized it to let the model also produce outputs of arbitrary size. How, you might ask? The
idea is actually relatively simple: one defines outputs of an arbitrary size, and then applies cross-attention with the
last hidden states of the latents, using the outputs as queries, and the latents as keys and values.
So let's say one wants to perform masked language modeling (BERT-style) with the Perceiver. As the Perceiver's input
length will not have an impact on the computation time of the self-attention layers, one can provide raw bytes,
providing inputs of length 2048 to the model. If one now masks out certain of these 2048 tokens, one can define the
outputs as being of shape: (batch_size, 2048, 768). Next, one performs cross-attention with the final hidden states
of the latents to update the outputs tensor. After cross-attention, one still has a tensor of shape (batch_size,
2048, 768). One can then place a regular language modeling head on top, to project the last dimension to the
vocabulary size of the model, i.e. creating logits of shape (batch_size, 2048, 262) (as Perceiver uses a vocabulary
size of 262 byte IDs).

 Perceiver IO architecture. Taken from the original paper 
This model was contributed by nielsr. The original code can be found
here.
Tips:

The quickest way to get started with the Perceiver is by checking the tutorial
  notebooks.
Refer to the blog post if you want to fully understand how the model works and
is implemented in the library. Note that the models available in the library only showcase some examples of what you can do
with the Perceiver. There are many more use cases, including question answering, named-entity recognition, object detection,
audio classification, video classification, etc.

Note:

Perceiver does not work with torch.nn.DataParallel due to a bug in PyTorch, see issue #36035

Documentation resources

Text classification task guide
Masked language modeling task guide
Image classification task guide

Perceiver specific outputs
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverModelOutput
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverDecoderOutput
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverMaskedLMOutput
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverClassifierOutput
PerceiverConfig
[[autodoc]] PerceiverConfig
PerceiverTokenizer
[[autodoc]] PerceiverTokenizer
    - call
PerceiverFeatureExtractor
[[autodoc]] PerceiverFeatureExtractor
    - call
PerceiverImageProcessor
[[autodoc]] PerceiverImageProcessor
    - preprocess
PerceiverTextPreprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverTextPreprocessor
PerceiverImagePreprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverImagePreprocessor
PerceiverOneHotPreprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverOneHotPreprocessor
PerceiverAudioPreprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverAudioPreprocessor
PerceiverMultimodalPreprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverMultimodalPreprocessor
PerceiverProjectionDecoder
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverProjectionDecoder
PerceiverBasicDecoder
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverBasicDecoder
PerceiverClassificationDecoder
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverClassificationDecoder
PerceiverOpticalFlowDecoder
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverOpticalFlowDecoder
PerceiverBasicVideoAutoencodingDecoder
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverBasicVideoAutoencodingDecoder
PerceiverMultimodalDecoder
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverMultimodalDecoder
PerceiverProjectionPostprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverProjectionPostprocessor
PerceiverAudioPostprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverAudioPostprocessor
PerceiverClassificationPostprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverClassificationPostprocessor
PerceiverMultimodalPostprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverMultimodalPostprocessor
PerceiverModel
[[autodoc]] PerceiverModel
    - forward
PerceiverForMaskedLM
[[autodoc]] PerceiverForMaskedLM
    - forward
PerceiverForSequenceClassification
[[autodoc]] PerceiverForSequenceClassification
    - forward
PerceiverForImageClassificationLearned
[[autodoc]] PerceiverForImageClassificationLearned
    - forward
PerceiverForImageClassificationFourier
[[autodoc]] PerceiverForImageClassificationFourier
    - forward
PerceiverForImageClassificationConvProcessing
[[autodoc]] PerceiverForImageClassificationConvProcessing
    - forward
PerceiverForOpticalFlow
[[autodoc]] PerceiverForOpticalFlow
    - forward
PerceiverForMultimodalAutoencoding
[[autodoc]] PerceiverForMultimodalAutoencoding
    - forward"
"
RAG

Overview
Retrieval-augmented generation (""RAG"") models combine the powers of pretrained dense retrieval (DPR) and
sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate
outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing
both retrieval and generation to adapt to downstream tasks.
It is based on the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir
Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
The abstract from the paper is the following:
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve
state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely
manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind
task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge
remain open research problems. Pre-trained models with a differentiable access mechanism to explicit nonparametric
memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a
general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained
parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a
pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a
pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages
across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our
models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks,
outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation
tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art
parametric-only seq2seq baseline.
This model was contributed by ola13.
Tips:
- Retrieval-augmented generation (“RAG”) models combine the powers of pretrained dense retrieval (DPR) and Seq2Seq models. RAG models retrieve docs, pass them to a seq2seq model, then marginalize to generate outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing both retrieval and generation to adapt to downstream tasks.
RagConfig
[[autodoc]] RagConfig
RagTokenizer
[[autodoc]] RagTokenizer
Rag specific outputs
[[autodoc]] models.rag.modeling_rag.RetrievAugLMMarginOutput
[[autodoc]] models.rag.modeling_rag.RetrievAugLMOutput
RagRetriever
[[autodoc]] RagRetriever
RagModel
[[autodoc]] RagModel
    - forward
RagSequenceForGeneration
[[autodoc]] RagSequenceForGeneration
    - forward
    - generate
RagTokenForGeneration
[[autodoc]] RagTokenForGeneration
    - forward
    - generate
TFRagModel
[[autodoc]] TFRagModel
    - call
TFRagSequenceForGeneration
[[autodoc]] TFRagSequenceForGeneration
    - call
    - generate
TFRagTokenForGeneration
[[autodoc]] TFRagTokenForGeneration
    - call
    - generate"
"
FLAN-T5
Overview
FLAN-T5 was released in the paper Scaling Instruction-Finetuned Language Models - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.
One can directly use FLAN-T5 weights without finetuning the model:
thon

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(""google/flan-t5-small"")
tokenizer = AutoTokenizer.from_pretrained(""google/flan-t5-small"")
inputs = tokenizer(""A step by step recipe to make bolognese pasta:"", return_tensors=""pt"")
outputs = model.generate(**inputs)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['Pour a cup of bolognese into a large bowl and add the pasta']

FLAN-T5 includes the same improvements as T5 version 1.1 (see here for the full details of the model's improvements.)
Google has released the following variants:

google/flan-t5-small

google/flan-t5-base

google/flan-t5-large

google/flan-t5-xl

google/flan-t5-xxl.

One can refer to T5's documentation page for all tips, code examples and notebooks. As well as the FLAN-T5 model card for more details regarding training and evaluation of the model.
The original checkpoints can be found here."
"
DPT
Overview
The DPT model was proposed in Vision Transformers for Dense Prediction by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
DPT is a model that leverages the Vision Transformer (ViT) as backbone for dense prediction tasks like semantic segmentation and depth estimation.
The abstract from the paper is the following:
We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense vision transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art.

 DPT architecture. Taken from the original paper. 
This model was contributed by nielsr. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DPT.

Demo notebooks for [DPTForDepthEstimation] can be found here.

Semantic segmentation task guide

Monocular depth estimation task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
DPTConfig
[[autodoc]] DPTConfig
DPTFeatureExtractor
[[autodoc]] DPTFeatureExtractor
    - call
    - post_process_semantic_segmentation
DPTImageProcessor
[[autodoc]] DPTImageProcessor
    - preprocess
    - post_process_semantic_segmentation
DPTModel
[[autodoc]] DPTModel
    - forward
DPTForDepthEstimation
[[autodoc]] DPTForDepthEstimation
    - forward
DPTForSemanticSegmentation
[[autodoc]] DPTForSemanticSegmentation
    - forward"
"
M-CTC-T

This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: pip install -U transformers==4.30.0.

Overview
The M-CTC-T model was proposed in Pseudo-Labeling For Massively Multilingual Speech Recognition by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal.
The abstract from the paper is the following:
Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual
speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech
recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even
with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised
learning on a target language, generate pseudo-labels for that language, and train a final model using
pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled
Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better
performance for many languages that also transfers well to LibriSpeech.
This model was contributed by cwkeam. The original code can be found here.
Documentation resources

Automatic speech recognition task guide

Tips:

The PyTorch version of this model is only available in torch 1.9 and higher.

MCTCTConfig
[[autodoc]] MCTCTConfig
MCTCTFeatureExtractor
[[autodoc]] MCTCTFeatureExtractor
    - call
MCTCTProcessor
[[autodoc]] MCTCTProcessor
    - call
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
MCTCTModel
[[autodoc]] MCTCTModel
    - forward
MCTCTForCTC
[[autodoc]] MCTCTForCTC
    - forward"
"
Hybrid Vision Transformer (ViT Hybrid)
Overview
The hybrid Vision Transformer (ViT) model was proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition
at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk
Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob
Uszkoreit, Neil Houlsby. It's the first paper that successfully trains a Transformer encoder on ImageNet, attaining
very good results compared to familiar convolutional architectures. ViT hybrid is a slight variant of the plain Vision Transformer,
by leveraging a convolutional backbone (specifically, BiT) whose features are used as initial ""tokens"" for the Transformer.
The abstract from the paper is the following:
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its
applications to computer vision remain limited. In vision, attention is either applied in conjunction with
convolutional networks, or used to replace certain components of convolutional networks while keeping their overall
structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to
sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of
data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.),
Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring
substantially fewer computational resources to train.
This model was contributed by nielsr. The original code (written in JAX) can be
found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT Hybrid.

[ViTHybridForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ViTHybridConfig
[[autodoc]] ViTHybridConfig
ViTHybridImageProcessor
[[autodoc]] ViTHybridImageProcessor
    - preprocess
ViTHybridModel
[[autodoc]] ViTHybridModel
    - forward
ViTHybridForImageClassification
[[autodoc]] ViTHybridForImageClassification
    - forward"
"
MMS
Overview
The MMS model was proposed in Scaling Speech Technology to 1,000+ Languages 
by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli
The abstract from the paper is the following:
Expanding the language coverage of speech technology has the potential to improve access to information for many more people. 
However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000
languages spoken around the world. 
The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. 
The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging
self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, 
a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models 
for the same number of languages, as well as a language identification model for 4,017 languages. 
Experiments show that our multilingual speech recognition model more than halves the word error rate of 
Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data.
Here are the different models open sourced in the MMS project. The models and code are originally released here. We have add them to the transformers framework, making them easier to use.
Automatic Speech Recognition (ASR)
The ASR model checkpoints  can be found here : mms-1b-fl102, mms-1b-l1107, mms-1b-all. For best accuracy, use the mms-1b-all model. 
Tips:

All ASR models accept a float array corresponding to the raw waveform of the speech signal. The raw waveform should be pre-processed with [Wav2Vec2FeatureExtractor].
The models were trained using connectionist temporal classification (CTC) so the model output has to be decoded using
  [Wav2Vec2CTCTokenizer].
You can load different language adapter weights for different languages via [~Wav2Vec2PreTrainedModel.load_adapter]. Language adapters only consists of roughly 2 million parameters 
  and can therefore be efficiently loaded on the fly when needed.

Loading
By default MMS loads adapter weights for English. If you want to load adapter weights of another language 
make sure to specify target_lang=<your-chosen-target-lang> as well as ""ignore_mismatched_sizes=True.
The ignore_mismatched_sizes=True keyword has to be passed to allow the language model head to be resized according
to the vocabulary of the specified language.
Similarly, the processor should be loaded with the same target language

from transformers import Wav2Vec2ForCTC, AutoProcessor
model_id = ""facebook/mms-1b-all""
target_lang = ""fra""
processor = AutoProcessor.from_pretrained(model_id, target_lang=target_lang)
model = Wav2Vec2ForCTC.from_pretrained(model_id, target_lang=target_lang, ignore_mismatched_sizes=True)

You can safely ignore a warning such as:
text
Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/mms-1b-all and are newly initialized because the shapes did not match:
- lm_head.bias: found shape torch.Size([154]) in the checkpoint and torch.Size([314]) in the model instantiated
- lm_head.weight: found shape torch.Size([154, 1280]) in the checkpoint and torch.Size([314, 1280]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

If you want to use the ASR pipeline, you can load your chosen target language as such:

from transformers import pipeline
model_id = ""facebook/mms-1b-all""
target_lang = ""fra""
pipe = pipeline(model=model_id, model_kwargs={""target_lang"": ""fra"", ""ignore_mismatched_sizes"": True})

Inference
Next, let's look at how we can run MMS in inference and change adapter layers after having called [~PretrainedModel.from_pretrained]
First, we load audio data in different languages using the Datasets.

from datasets import load_dataset, Audio
English
stream_data = load_dataset(""mozilla-foundation/common_voice_13_0"", ""en"", split=""test"", streaming=True)
stream_data = stream_data.cast_column(""audio"", Audio(sampling_rate=16000))
en_sample = next(iter(stream_data))[""audio""][""array""]
French
stream_data = load_dataset(""mozilla-foundation/common_voice_13_0"", ""fr"", split=""test"", streaming=True)
stream_data = stream_data.cast_column(""audio"", Audio(sampling_rate=16000))
fr_sample = next(iter(stream_data))[""audio""][""array""]

Next, we load the model and processor

from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
model_id = ""facebook/mms-1b-all""
processor = AutoProcessor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)

Now we process the audio data, pass the processed audio data to the model and transcribe the model output,
just like we usually do for [Wav2Vec2ForCTC].

inputs = processor(en_sample, sampling_rate=16_000, return_tensors=""pt"")
with torch.no_grad():
    outputs = model(**inputs).logits
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
'joe keton disapproved of films and buster also had reservations about the media'

We can now keep the same model in memory and simply switch out the language adapters by
calling the convenient [~Wav2Vec2ForCTC.load_adapter] function for the model and [~Wav2Vec2CTCTokenizer.set_target_lang] for the tokenizer.
We pass the target language as an input - ""fra"" for French.

processor.tokenizer.set_target_lang(""fra"")
model.load_adapter(""fra"")
inputs = processor(fr_sample, sampling_rate=16_000, return_tensors=""pt"")
with torch.no_grad():
    outputs = model(**inputs).logits
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
""ce dernier est volé tout au long de l'histoire romaine""

In the same way the language can be switched out for all other supported languages. Please have a look at:
py
processor.tokenizer.vocab.keys()
to see all supported languages.
To further improve performance from ASR models, language model decoding can be used. See the documentation here for further details.  
Speech Synthesis (TTS)
Individual TTS models are available for each of the 1100+ languages. The models and inference documentation can be found here.
Language Identification (LID)
Different LID models are available based on the number of languages they can recognize - 126, 256, 512, 1024, 2048, 4017. 
Inference
First, we install transformers and some other libraries
``
pip install torch accelerate torchaudio datasets
pip install --upgrade transformers
`
pip install torch datasets[audio]
Next, we load a couple of audio samples viadatasets`. Make sure that the audio data is sampled to 16000 kHz.

from datasets import load_dataset, Audio
English
stream_data = load_dataset(""mozilla-foundation/common_voice_13_0"", ""en"", split=""test"", streaming=True)
stream_data = stream_data.cast_column(""audio"", Audio(sampling_rate=16000))
en_sample = next(iter(stream_data))[""audio""][""array""]
Arabic
stream_data = load_dataset(""mozilla-foundation/common_voice_13_0"", ""ar"", split=""test"", streaming=True)
stream_data = stream_data.cast_column(""audio"", Audio(sampling_rate=16000))
ar_sample = next(iter(stream_data))[""audio""][""array""]

Next, we load the model and processor

from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
import torch
model_id = ""facebook/mms-lid-126""
processor = AutoFeatureExtractor.from_pretrained(model_id)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id)

Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition

English
inputs = processor(en_sample, sampling_rate=16_000, return_tensors=""pt"")
with torch.no_grad():
    outputs = model(**inputs).logits
lang_id = torch.argmax(outputs, dim=-1)[0].item()
detected_lang = model.config.id2label[lang_id]
'eng'
Arabic
inputs = processor(ar_sample, sampling_rate=16_000, return_tensors=""pt"")
with torch.no_grad():
    outputs = model(**inputs).logits
lang_id = torch.argmax(outputs, dim=-1)[0].item()
detected_lang = model.config.id2label[lang_id]
'ara'

To see all the supported languages of a checkpoint, you can print out the language ids as follows:
py
processor.id2label.values()
Audio Pretrained Models
Pretrained models are available for two different sizes - 300M , 1Bil. The architecture is based on the Wav2Vec2 model, so one can refer to Wav2Vec2's documentation page for further details on how to finetune with models for various downstream tasks."
"
ALIGN
Overview
The ALIGN model was proposed in Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. ALIGN is a multi-modal vision and language model. It can be used for image-text similarity and for zero-shot image classification. ALIGN features a dual-encoder architecture with EfficientNet as its vision encoder and BERT as its text encoder, and learns to align visual and text representations with contrastive learning. Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe.
The abstract from the paper is the following:
Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive or require expert knowledge. For vision applications, representations are mostly learned using datasets with explicit class labels such as ImageNet or OpenImages. For vision-language, popular datasets like Conceptual Captions, MSCOCO, or CLIP all involve a non-trivial data collection (and cleaning) process. This costly curation process limits the size of datasets and hence hinders the scaling of trained models. In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme. Our visual representation achieves strong performance when transferred to classification tasks such as ImageNet and VTAB. The aligned visual and language representations enables zero-shot image classification and also set new state-of-the-art results on Flickr30K and MSCOCO image-text retrieval benchmarks, even when compared with more sophisticated cross-attention models. The representations also enable cross-modality search with complex text and text + image queries.
Usage
ALIGN uses EfficientNet to get visual features and BERT to get the text features. Both the text and visual features are then projected to a latent space with identical dimension. The dot product between the projected image and text features is then used as a similarity score.
[AlignProcessor] wraps [EfficientNetImageProcessor] and [BertTokenizer] into a single instance to both encode the text and preprocess the images. The following example shows how to get the image-text similarity scores using [AlignProcessor] and [AlignModel].
thon
import requests
import torch
from PIL import Image
from transformers import AlignProcessor, AlignModel
processor = AlignProcessor.from_pretrained(""kakaobrain/align-base"")
model = AlignModel.from_pretrained(""kakaobrain/align-base"")
url = ""http://images.cocodataset.org/val2017/000000039769.jpg""
image = Image.open(requests.get(url, stream=True).raw)
candidate_labels = [""an image of a cat"", ""an image of a dog""]
inputs = processor(text=candidate_labels, images=image, return_tensors=""pt"")
with torch.no_grad():
    outputs = model(**inputs)
this is the image-text similarity score
logits_per_image = outputs.logits_per_image
we can take the softmax to get the label probabilities
probs = logits_per_image.softmax(dim=1)
print(probs)

This model was contributed by Alara Dirik.
The original code is not released, this implementation is based on the Kakao Brain implementation based on the original paper.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ALIGN.

A blog post on ALIGN and the COYO-700M dataset.
A zero-shot image classification demo.
Model card of kakaobrain/align-base model.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it. The resource should ideally demonstrate something new instead of duplicating an existing resource.
AlignConfig
[[autodoc]] AlignConfig
    - from_text_vision_configs
AlignTextConfig
[[autodoc]] AlignTextConfig
AlignVisionConfig
[[autodoc]] AlignVisionConfig
AlignProcessor
[[autodoc]] AlignProcessor
AlignModel
[[autodoc]] AlignModel
    - forward
    - get_text_features
    - get_image_features
AlignTextModel
[[autodoc]] AlignTextModel
    - forward
AlignVisionModel
[[autodoc]] AlignVisionModel
    - forward"
"
Big Transfer (BiT)
Overview
The BiT model was proposed in Big Transfer (BiT): General Visual Representation Learning by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
BiT is a simple recipe for scaling up pre-training of ResNet-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
The abstract from the paper is the following:
Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -- from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.
Tips:

BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by group normalization,
2) weight standardization is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
impact on transfer learning.

This model was contributed by nielsr.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BiT.

[BitForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
BitConfig
[[autodoc]] BitConfig
BitImageProcessor
[[autodoc]] BitImageProcessor
    - preprocess
BitModel
[[autodoc]] BitModel
    - forward
BitForImageClassification
[[autodoc]] BitForImageClassification
    - forward"
"
Wav2Vec2Phoneme
Overview
The Wav2Vec2Phoneme model was proposed in Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al.,
2021 by Qiantong Xu, Alexei Baevski, Michael Auli.
The abstract from the paper is the following:
Recent progress in self-training, self-supervised pretraining and unsupervised learning enabled well performing speech
recognition systems without any labeled data. However, in many cases there is labeled data available for related
languages which is not utilized by these methods. This paper extends previous work on zero-shot cross-lingual transfer
learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. This is done by
mapping phonemes of the training languages to the target language using articulatory features. Experiments show that
this simple method significantly outperforms prior work which introduced task-specific architectures and used only part
of a monolingually pretrained model.
Tips:

Wav2Vec2Phoneme uses the exact same architecture as Wav2Vec2
Wav2Vec2Phoneme is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
Wav2Vec2Phoneme model was trained using connectionist temporal classification (CTC) so the model output has to be
  decoded using [Wav2Vec2PhonemeCTCTokenizer].
Wav2Vec2Phoneme can be fine-tuned on multiple language at once and decode unseen languages in a single forward pass
  to a sequence of phonemes
By default the model outputs a sequence of phonemes. In order to transform the phonemes to a sequence of words one
  should make use of a dictionary and language model.

Relevant checkpoints can be found under https://huggingface.co/models?other=phoneme-recognition.
This model was contributed by patrickvonplaten
The original code can be found here.
Wav2Vec2Phoneme's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2]'s documentation page except for the tokenizer.
Wav2Vec2PhonemeCTCTokenizer
[[autodoc]] Wav2Vec2PhonemeCTCTokenizer
    - call
    - batch_decode
    - decode
    - phonemize"
"
DePlot
Overview
DePlot was proposed in the paper DePlot: One-shot visual language reasoning by plot-to-table translation from Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
The abstract of the paper states the following:
Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models require at least tens of thousands of training examples and their reasoning capabilities are still much limited, especially on complex human-written queries. This paper presents the first one-shot solution to visual language reasoning. We decompose the challenge of visual language reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over the translated text. The key in this method is a modality conversion module, named as DePlot, which translates the image of a plot or chart to a linearized table. The output of DePlot can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs. To obtain DePlot, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DePlot end-to-end on this task. DePlot can then be used off-the-shelf together with LLMs in a plug-and-play fashion. Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24.0% improvement over finetuned SOTA on human-written queries from the task of chart QA.
Model description
DePlot is a model that is trained using Pix2Struct architecture. You can find more information about Pix2Struct in the Pix2Struct documentation.
DePlot is a Visual Question Answering subset of Pix2Struct architecture. It renders the input question on the image and predicts the answer.
Usage
Currently one checkpoint is available for DePlot:

google/deplot: DePlot fine-tuned on ChartQA dataset 

thon
from transformers import AutoProcessor, Pix2StructForConditionalGeneration
import requests
from PIL import Image
model = Pix2StructForConditionalGeneration.from_pretrained(""google/deplot"")
processor = AutoProcessor.from_pretrained(""google/deplot"")
url = ""https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/5090.png""
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, text=""Generate underlying data table of the figure below:"", return_tensors=""pt"")
predictions = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(predictions[0], skip_special_tokens=True))

Fine-tuning
To fine-tune DePlot, refer to the pix2struct fine-tuning notebook. For Pix2Struct models, we have found out that fine-tuning the model with Adafactor and cosine learning rate scheduler leads to faster convergence:
thon
from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup
optimizer = Adafactor(self.parameters(), scale_parameter=False, relative_step=False, lr=0.01, weight_decay=1e-05)
scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=1000, num_training_steps=40000)
```"
"
TAPAS
Overview
The TAPAS model was proposed in TAPAS: Weakly Supervised Table Parsing via Pre-training
by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically 
designed (and pre-trained) for answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 
token types that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset comprising 
millions of tables from English Wikipedia and corresponding texts. 
For question answering, TAPAS has 2 heads on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or summing) among selected cells. TAPAS has been fine-tuned on several datasets: 
- SQA (Sequential Question Answering by Microsoft)
- WTQ (Wiki Table Questions by Stanford University)
- WikiSQL (by Salesforce). 
It achieves state-of-the-art on both SQA and WTQ, while having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
The abstract from the paper is the following:
Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.
In addition, the authors have further pre-trained TAPAS to recognize table entailment, by creating a balanced dataset of millions of automatically created training examples which are learned in an intermediate step prior to fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on TabFact, a large-scale dataset with 16k Wikipedia tables for table entailment (a binary classification task). For more details, see their follow-up paper: Understanding tables with intermediate pre-training by Julian Martin Eisenschlos, Syrine Krichene and Thomas Müller.
 
 TAPAS architecture. Taken from the original blog post.
This model was contributed by nielsr. The Tensorflow version of this model was contributed by kamalkraj. The original code can be found here.
Tips:

TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell of the table). Note that this is something that was added after the publication of the original TAPAS paper. According to the authors, this usually results in a slightly better performance, and allows you to encode longer sequences without running out of embeddings. This is reflected in the reset_position_index_per_cell parameter of [TapasConfig], which is set to True by default. The default versions of the models available on the hub all use relative position embeddings. You can still use the ones with absolute position embeddings by passing in an additional argument revision=""no_reset"" when calling the from_pretrained() method. Note that it's usually advised to pad the inputs on the right rather than the left.
TAPAS is based on BERT, so TAPAS-base for example corresponds to a BERT-base architecture. Of course, TAPAS-large will result in the best performance (the results reported in the paper are from TAPAS-large). Results of the various sized models are shown on the original Github repository.
TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a conversational set-up. This means that you can ask follow-up questions such as ""what is his age?"" related to the previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that case, you have to feed every table-question pair one by one to the model, such that the prev_labels token type ids can be overwritten by the predicted labels of the model to the previous question. See ""Usage"" section for more info.
TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained with a causal language modeling (CLM) objective are better in that regard. Note that TAPAS can be used as an encoder in the EncoderDecoderModel framework, to combine it with an autoregressive text decoder such as GPT-2.

Usage: fine-tuning
Here we explain how you can fine-tune [TapasForQuestionAnswering] on your own dataset.
STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment
Basically, there are 3 different ways in which one can fine-tune [TapasForQuestionAnswering], corresponding to the different datasets on which Tapas was fine-tuned:

SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example if you first ask ""what's the name of the first actor?"" then you can ask a follow-up question such as ""how old is he?"". Here, questions do not involve any aggregation (all questions are cell selection questions).
WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or averaging cell values. You can then for example ask ""what's the total number of goals Cristiano Ronaldo made in his career?"". This case is also called weak supervision, since the model itself must learn the appropriate aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation operator during training. This is also called strong supervision. Here, learning the appropriate aggregation operator is much easier.

To summarize:
| Task                            | Example dataset | Description                                                                                         |
|-------------------------------------|---------------------|---------------------------------------------------------------------------------------------------------|
| Conversational                      | SQA                 | Conversational, only cell selection questions                                                           |
| Weak supervision for aggregation    | WTQ                 | Questions might involve aggregation, and the model must learn this given only the answer as supervision |
| Strong supervision for aggregation  | WikiSQL-supervised  | Questions might involve aggregation, and the model must learn this given the gold aggregation operator  |

Initializing a model with a pre-trained base and randomly initialized classification heads from the hub can be done as shown below.

from transformers import TapasConfig, TapasForQuestionAnswering
for example, the base sized model with default SQA configuration
model = TapasForQuestionAnswering.from_pretrained(""google/tapas-base"")
or, the base sized model with WTQ configuration
config = TapasConfig.from_pretrained(""google/tapas-base-finetuned-wtq"")
model = TapasForQuestionAnswering.from_pretrained(""google/tapas-base"", config=config)
or, the base sized model with WikiSQL configuration
config = TapasConfig(""google-base-finetuned-wikisql-supervised"")
model = TapasForQuestionAnswering.from_pretrained(""google/tapas-base"", config=config)

Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also experiment by defining any hyperparameters you want when initializing [TapasConfig], and then create a [TapasForQuestionAnswering] based on that configuration. For example, if you have a dataset that has both conversational questions and questions that might involve aggregation, then you can do it this way. Here's an example:

from transformers import TapasConfig, TapasForQuestionAnswering
you can initialize the classification heads any way you want (see docs of TapasConfig)
config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
initializing the pre-trained base sized model with our custom classification heads
model = TapasForQuestionAnswering.from_pretrained(""google/tapas-base"", config=config)

Initializing a model with a pre-trained base and randomly initialized classification heads from the hub can be done as shown below. Be sure to have installed the tensorflow_probability dependency:

from transformers import TapasConfig, TFTapasForQuestionAnswering
for example, the base sized model with default SQA configuration
model = TFTapasForQuestionAnswering.from_pretrained(""google/tapas-base"")
or, the base sized model with WTQ configuration
config = TapasConfig.from_pretrained(""google/tapas-base-finetuned-wtq"")
model = TFTapasForQuestionAnswering.from_pretrained(""google/tapas-base"", config=config)
or, the base sized model with WikiSQL configuration
config = TapasConfig(""google-base-finetuned-wikisql-supervised"")
model = TFTapasForQuestionAnswering.from_pretrained(""google/tapas-base"", config=config)

Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also experiment by defining any hyperparameters you want when initializing [TapasConfig], and then create a [TFTapasForQuestionAnswering] based on that configuration. For example, if you have a dataset that has both conversational questions and questions that might involve aggregation, then you can do it this way. Here's an example:

from transformers import TapasConfig, TFTapasForQuestionAnswering
you can initialize the classification heads any way you want (see docs of TapasConfig)
config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
initializing the pre-trained base sized model with our custom classification heads
model = TFTapasForQuestionAnswering.from_pretrained(""google/tapas-base"", config=config)

What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See here for more info.
For a list of all pre-trained and fine-tuned TAPAS checkpoints available on HuggingFace's  hub, see here.
STEP 2: Prepare your data in the SQA format
Second, no matter what you picked above, you should prepare your dataset in the SQA format. This format is a TSV/CSV file with the following columns:

id: optional, id of the table-question pair, for bookkeeping purposes.
annotator: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
position: integer indicating if the question is the first, second, third, related to the table. Only required in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
question: string
table_file: string, name of a csv file containing the tabular data
answer_coordinates: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is part of the answer)
answer_text: list of one or more strings (each string being a cell value that is part of the answer)
aggregation_label: index of the aggregation operator. Only required in case of strong supervision for aggregation (the WikiSQL-supervised case)
float_answer: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of weak supervision for aggregation (such as WTQ and WikiSQL)

The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the SQA format. The author explains this here. A conversion of this script that works with HuggingFace's implementation can be found here. Interestingly, these conversion scripts are not perfect (the answer_coordinates and float_answer fields are populated based on the answer_text), meaning that WTQ and WikiSQL results could actually be improved.
STEP 3: Convert your data into tensors using TapasTokenizer

Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular data), you can then use [TapasTokenizer] to convert table-question pairs into input_ids, attention_mask, token_type_ids and so on. Again, based on which of the three cases you picked above, [TapasForQuestionAnswering] requires different
inputs to be fine-tuned:
| Task                           | Required inputs                                                                                                 |
|------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Conversational                     | input_ids, attention_mask, token_type_ids, labels                                                           |
|  Weak supervision for aggregation  | input_ids, attention_mask, token_type_ids, labels, numeric_values, numeric_values_scale, float_answer |
| Strong supervision for aggregation | input ids, attention mask, token type ids, labels, aggregation_labels                                     |
[TapasTokenizer] creates the labels, numeric_values and numeric_values_scale based on the answer_coordinates and answer_text columns of the TSV file. The float_answer and aggregation_labels are already in the TSV file of step 2. Here's an example:

from transformers import TapasTokenizer
import pandas as pd
model_name = ""google/tapas-base""
tokenizer = TapasTokenizer.from_pretrained(model_name)
data = {""Actors"": [""Brad Pitt"", ""Leonardo Di Caprio"", ""George Clooney""], ""Number of movies"": [""87"", ""53"", ""69""]}
queries = [
     ""What is the name of the first actor?"",
     ""How many movies has George Clooney played in?"",
     ""What is the total number of movies?"",
 ]
answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
answer_text = [[""Brad Pitt""], [""69""], [""209""]]
table = pd.DataFrame.from_dict(data)
inputs = tokenizer(
     table=table,
     queries=queries,
     answer_coordinates=answer_coordinates,
     answer_text=answer_text,
     padding=""max_length"",
     return_tensors=""pt"",
 )
inputs
{'input_ids': tensor([[  ]]), 'attention_mask': tensor([[]]), 'token_type_ids': tensor([[[]]]),
'numeric_values': tensor([[  ]]), 'numeric_values_scale: tensor([[  ]]), labels: tensor([[  ]])}

Note that [TapasTokenizer] expects the data of the table to be text-only. You can use .astype(str) on a dataframe to turn it into text-only data.
Of course, this only shows how to encode a single training example. It is advised to create a dataloader to iterate over batches:

import torch
import pandas as pd
tsv_path = ""your_path_to_the_tsv_file""
table_csv_path = ""your_path_to_a_directory_containing_all_csv_files""
class TableDataset(torch.utils.data.Dataset):
     def init(self, data, tokenizer):
         self.data = data
         self.tokenizer = tokenizer

     def getitem(self, idx):
         item = data.iloc[idx]
         table = pd.read_csv(table_csv_path + item.table_file).astype(
             str
         )  # be sure to make your table data text only
         encoding = self.tokenizer(
             table=table,
             queries=item.question,
             answer_coordinates=item.answer_coordinates,
             answer_text=item.answer_text,
             truncation=True,
             padding=""max_length"",
             return_tensors=""pt"",
         )
         # remove the batch dimension which the tokenizer adds by default
         encoding = {key: val.squeeze(0) for key, val in encoding.items()}
         # add the float_answer which is also required (weak supervision for aggregation case)
         encoding[""float_answer""] = torch.tensor(item.float_answer)
         return encoding
     def len(self):
         return len(self.data)

data = pd.read_csv(tsv_path, sep=""\t"")
train_dataset = TableDataset(data, tokenizer)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
``
</pt>
<tf>
Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular data), you can then use [TapasTokenizer] to convert table-question pairs intoinput_ids,attention_mask,token_type_idsand so on. Again, based on which of the three cases you picked above, [TFTapasForQuestionAnswering`] requires different
inputs to be fine-tuned:

| Task                           | Required inputs                                                                                                 |
|------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Conversational                     | input_ids, attention_mask, token_type_ids, labels                                                           |
|  Weak supervision for aggregation  | input_ids, attention_mask, token_type_ids, labels, numeric_values, numeric_values_scale, float_answer |
| Strong supervision for aggregation | input ids, attention mask, token type ids, labels, aggregation_labels                                     |
[TapasTokenizer] creates the labels, numeric_values and numeric_values_scale based on the answer_coordinates and answer_text columns of the TSV file. The float_answer and aggregation_labels are already in the TSV file of step 2. Here's an example:

from transformers import TapasTokenizer
import pandas as pd
model_name = ""google/tapas-base""
tokenizer = TapasTokenizer.from_pretrained(model_name)
data = {""Actors"": [""Brad Pitt"", ""Leonardo Di Caprio"", ""George Clooney""], ""Number of movies"": [""87"", ""53"", ""69""]}
queries = [
     ""What is the name of the first actor?"",
     ""How many movies has George Clooney played in?"",
     ""What is the total number of movies?"",
 ]
answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
answer_text = [[""Brad Pitt""], [""69""], [""209""]]
table = pd.DataFrame.from_dict(data)
inputs = tokenizer(
     table=table,
     queries=queries,
     answer_coordinates=answer_coordinates,
     answer_text=answer_text,
     padding=""max_length"",
     return_tensors=""tf"",
 )
inputs
{'input_ids': tensor([[  ]]), 'attention_mask': tensor([[]]), 'token_type_ids': tensor([[[]]]),
'numeric_values': tensor([[  ]]), 'numeric_values_scale: tensor([[  ]]), labels: tensor([[  ]])}

Note that [TapasTokenizer] expects the data of the table to be text-only. You can use .astype(str) on a dataframe to turn it into text-only data.
Of course, this only shows how to encode a single training example. It is advised to create a dataloader to iterate over batches:

import tensorflow as tf
import pandas as pd
tsv_path = ""your_path_to_the_tsv_file""
table_csv_path = ""your_path_to_a_directory_containing_all_csv_files""
class TableDataset:
     def init(self, data, tokenizer):
         self.data = data
         self.tokenizer = tokenizer

     def iter(self):
         for idx in range(self.len()):
             item = self.data.iloc[idx]
             table = pd.read_csv(table_csv_path + item.table_file).astype(
                 str
             )  # be sure to make your table data text only
             encoding = self.tokenizer(
                 table=table,
                 queries=item.question,
                 answer_coordinates=item.answer_coordinates,
                 answer_text=item.answer_text,
                 truncation=True,
                 padding=""max_length"",
                 return_tensors=""tf"",
             )
             # remove the batch dimension which the tokenizer adds by default
             encoding = {key: tf.squeeze(val, 0) for key, val in encoding.items()}
             # add the float_answer which is also required (weak supervision for aggregation case)
             encoding[""float_answer""] = tf.convert_to_tensor(item.float_answer, dtype=tf.float32)
             yield encoding[""input_ids""], encoding[""attention_mask""], encoding[""numeric_values""], encoding[
                 ""numeric_values_scale""
             ], encoding[""token_type_ids""], encoding[""labels""], encoding[""float_answer""]
     def len(self):
         return len(self.data)

data = pd.read_csv(tsv_path, sep=""\t"")
train_dataset = TableDataset(data, tokenizer)
output_signature = (
     tf.TensorSpec(shape=(512,), dtype=tf.int32),
     tf.TensorSpec(shape=(512,), dtype=tf.int32),
     tf.TensorSpec(shape=(512,), dtype=tf.float32),
     tf.TensorSpec(shape=(512,), dtype=tf.float32),
     tf.TensorSpec(shape=(512, 7), dtype=tf.int32),
     tf.TensorSpec(shape=(512,), dtype=tf.int32),
     tf.TensorSpec(shape=(512,), dtype=tf.float32),
 )
train_dataloader = tf.data.Dataset.from_generator(train_dataset, output_signature=output_signature).batch(32)

Note that here, we encode each table-question pair independently. This is fine as long as your dataset is not conversational. In case your dataset involves conversational questions (such as in SQA), then you should first group together the queries, answer_coordinates and answer_text per table (in the order of their position
index) and batch encode each table with its questions. This will make sure that the prev_labels token types (see docs of [TapasTokenizer]) are set correctly. See this notebook for more info. See this notebook for more info regarding using the TensorFlow model.
**STEP 4: Train (fine-tune) the model

You can then fine-tune [TapasForQuestionAnswering] as follows (shown here for the weak supervision for aggregation case):

from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
this is the default WTQ configuration
config = TapasConfig(
     num_aggregation_labels=4,
     use_answer_as_supervision=True,
     answer_loss_cutoff=0.664694,
     cell_selection_preference=0.207951,
     huber_loss_delta=0.121194,
     init_cell_selection_weights_to_zero=True,
     select_one_column=True,
     allow_empty_column_selection=False,
     temperature=0.0352513,
 )
model = TapasForQuestionAnswering.from_pretrained(""google/tapas-base"", config=config)
optimizer = AdamW(model.parameters(), lr=5e-5)
model.train()
for epoch in range(2):  # loop over the dataset multiple times
     for batch in train_dataloader:
         # get the inputs;
         input_ids = batch[""input_ids""]
         attention_mask = batch[""attention_mask""]
         token_type_ids = batch[""token_type_ids""]
         labels = batch[""labels""]
         numeric_values = batch[""numeric_values""]
         numeric_values_scale = batch[""numeric_values_scale""]
         float_answer = batch[""float_answer""]

         # zero the parameter gradients
         optimizer.zero_grad()
         # forward + backward + optimize
         outputs = model(
             input_ids=input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             labels=labels,
             numeric_values=numeric_values,
             numeric_values_scale=numeric_values_scale,
             float_answer=float_answer,
         )
         loss = outputs.loss
         loss.backward()
         optimizer.step()
``
</pt>
<tf>
You can then fine-tune [TFTapasForQuestionAnswering`] as follows (shown here for the weak supervision for aggregation case):

import tensorflow as tf
from transformers import TapasConfig, TFTapasForQuestionAnswering
this is the default WTQ configuration
config = TapasConfig(
     num_aggregation_labels=4,
     use_answer_as_supervision=True,
     answer_loss_cutoff=0.664694,
     cell_selection_preference=0.207951,
     huber_loss_delta=0.121194,
     init_cell_selection_weights_to_zero=True,
     select_one_column=True,
     allow_empty_column_selection=False,
     temperature=0.0352513,
 )
model = TFTapasForQuestionAnswering.from_pretrained(""google/tapas-base"", config=config)
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
for epoch in range(2):  # loop over the dataset multiple times
     for batch in train_dataloader:
         # get the inputs;
         input_ids = batch[0]
         attention_mask = batch[1]
         token_type_ids = batch[4]
         labels = batch[-1]
         numeric_values = batch[2]
         numeric_values_scale = batch[3]
         float_answer = batch[6]

         # forward + backward + optimize
         with tf.GradientTape() as tape:
             outputs = model(
                 input_ids=input_ids,
                 attention_mask=attention_mask,
                 token_type_ids=token_type_ids,
                 labels=labels,
                 numeric_values=numeric_values,
                 numeric_values_scale=numeric_values_scale,
                 float_answer=float_answer,
             )
         grads = tape.gradient(outputs.loss, model.trainable_weights)
         optimizer.apply_gradients(zip(grads, model.trainable_weights))

Usage: inference

Here we explain how you can use [TapasForQuestionAnswering] or [TFTapasForQuestionAnswering] for inference (i.e. making predictions on new data). For inference, only input_ids, attention_mask and token_type_ids (which you can obtain using [TapasTokenizer]) have to be provided to the model to obtain the logits. Next, you can use the handy [~models.tapas.tokenization_tapas.convert_logits_to_predictions] method to convert these into predicted coordinates and optional aggregation indices.
However, note that inference is different depending on whether or not the setup is conversational. In a non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example of that:

from transformers import TapasTokenizer, TapasForQuestionAnswering
import pandas as pd
model_name = ""google/tapas-base-finetuned-wtq""
model = TapasForQuestionAnswering.from_pretrained(model_name)
tokenizer = TapasTokenizer.from_pretrained(model_name)
data = {""Actors"": [""Brad Pitt"", ""Leonardo Di Caprio"", ""George Clooney""], ""Number of movies"": [""87"", ""53"", ""69""]}
queries = [
     ""What is the name of the first actor?"",
     ""How many movies has George Clooney played in?"",
     ""What is the total number of movies?"",
 ]
table = pd.DataFrame.from_dict(data)
inputs = tokenizer(table=table, queries=queries, padding=""max_length"", return_tensors=""pt"")
outputs = model(**inputs)
predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
     inputs, outputs.logits.detach(), outputs.logits_aggregation.detach()
 )
let's print out the results:
id2aggregation = {0: ""NONE"", 1: ""SUM"", 2: ""AVERAGE"", 3: ""COUNT""}
aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
answers = []
for coordinates in predicted_answer_coordinates:
     if len(coordinates) == 1:
         # only a single cell:
         answers.append(table.iat[coordinates[0]])
     else:
         # multiple cells
         cell_values = []
         for coordinate in coordinates:
             cell_values.append(table.iat[coordinate])
         answers.append("", "".join(cell_values))
display(table)
print("""")
for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
     print(query)
     if predicted_agg == ""NONE"":
         print(""Predicted answer: "" + answer)
     else:
         print(""Predicted answer: "" + predicted_agg + "" > "" + answer)
What is the name of the first actor?
Predicted answer: Brad Pitt
How many movies has George Clooney played in?
Predicted answer: COUNT > 69
What is the total number of movies?
Predicted answer: SUM > 87, 53, 69
``
</pt>
<tf>
Here we explain how you can use [TFTapasForQuestionAnswering] for inference (i.e. making predictions on new data). For inference, onlyinput_ids,attention_maskandtoken_type_ids(which you can obtain using [TapasTokenizer]) have to be provided to the model to obtain the logits. Next, you can use the handy [~models.tapas.tokenization_tapas.convert_logits_to_predictions`] method to convert these into predicted coordinates and optional aggregation indices.

However, note that inference is different depending on whether or not the setup is conversational. In a non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example of that:

from transformers import TapasTokenizer, TFTapasForQuestionAnswering
import pandas as pd
model_name = ""google/tapas-base-finetuned-wtq""
model = TFTapasForQuestionAnswering.from_pretrained(model_name)
tokenizer = TapasTokenizer.from_pretrained(model_name)
data = {""Actors"": [""Brad Pitt"", ""Leonardo Di Caprio"", ""George Clooney""], ""Number of movies"": [""87"", ""53"", ""69""]}
queries = [
     ""What is the name of the first actor?"",
     ""How many movies has George Clooney played in?"",
     ""What is the total number of movies?"",
 ]
table = pd.DataFrame.from_dict(data)
inputs = tokenizer(table=table, queries=queries, padding=""max_length"", return_tensors=""tf"")
outputs = model(**inputs)
predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
     inputs, outputs.logits, outputs.logits_aggregation
 )
let's print out the results:
id2aggregation = {0: ""NONE"", 1: ""SUM"", 2: ""AVERAGE"", 3: ""COUNT""}
aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
answers = []
for coordinates in predicted_answer_coordinates:
     if len(coordinates) == 1:
         # only a single cell:
         answers.append(table.iat[coordinates[0]])
     else:
         # multiple cells
         cell_values = []
         for coordinate in coordinates:
             cell_values.append(table.iat[coordinate])
         answers.append("", "".join(cell_values))
display(table)
print("""")
for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
     print(query)
     if predicted_agg == ""NONE"":
         print(""Predicted answer: "" + answer)
     else:
         print(""Predicted answer: "" + predicted_agg + "" > "" + answer)
What is the name of the first actor?
Predicted answer: Brad Pitt
How many movies has George Clooney played in?
Predicted answer: COUNT > 69
What is the total number of movies?
Predicted answer: SUM > 87, 53, 69

In case of a conversational set-up, then each table-question pair must be provided sequentially to the model, such that the prev_labels token types can be overwritten by the predicted labels of the previous table-question pair. Again, more info can be found in this notebook (for PyTorch) and this notebook (for TensorFlow).
Documentation resources

Text classification task guide
Masked language modeling task guide

TAPAS specific outputs
[[autodoc]] models.tapas.modeling_tapas.TableQuestionAnsweringOutput
TapasConfig
[[autodoc]] TapasConfig
TapasTokenizer
[[autodoc]] TapasTokenizer
    - call
    - convert_logits_to_predictions
    - save_vocabulary
TapasModel
[[autodoc]] TapasModel
    - forward
TapasForMaskedLM
[[autodoc]] TapasForMaskedLM
    - forward
TapasForSequenceClassification
[[autodoc]] TapasForSequenceClassification
    - forward
TapasForQuestionAnswering
[[autodoc]] TapasForQuestionAnswering
    - forward
TFTapasModel
[[autodoc]] TFTapasModel
    - call
TFTapasForMaskedLM
[[autodoc]] TFTapasForMaskedLM
    - call
TFTapasForSequenceClassification
[[autodoc]] TFTapasForSequenceClassification
    - call
TFTapasForQuestionAnswering
[[autodoc]] TFTapasForQuestionAnswering
    - call"
"
PEGASUS-X
Overview
The PEGASUS-X model was proposed in Investigating Efficiently Extending Transformers for Long Input Summarization  by Jason Phang, Yao Zhao and Peter J. Liu.
PEGASUS-X (PEGASUS eXtended) extends the PEGASUS models for long input summarization through additional long input pretraining and using staggered block-local attention with global tokens in the encoder.
The abstract from the paper is the following:
While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X achieves strong performance on long input summarization tasks comparable with much larger models while adding few additional parameters and not requiring model parallelism to train.
Tips:

PEGASUS-X uses the same tokenizer as PEGASUS.

This model was contributed by zphang. The original code can be found here.
Documentation resources

Translation task guide
Summarization task guide

PegasusXConfig
[[autodoc]] PegasusXConfig
PegasusXModel
[[autodoc]] PegasusXModel
    - forward
PegasusXForConditionalGeneration
[[autodoc]] PegasusXForConditionalGeneration
    - forward"
"
Swin Transformer
Overview
The Swin Transformer was proposed in Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
The abstract from the paper is the following:
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone
for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains,
such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text.
To address these differences, we propose a hierarchical Transformer whose representation is computed with \bold{S}hifted
\bold{win}dows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping
local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at
various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it
compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense
prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation
(53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and
+2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones.
The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures.
Tips:
- One can use the [AutoImageProcessor] API to prepare images for the model.
- Swin pads the inputs supporting any input height and width (if divisible by 32).
- Swin can be used as a backbone. When output_hidden_states = True, it will output both hidden_states and reshaped_hidden_states. The reshaped_hidden_states have a shape of (batch, num_channels, height, width) rather than (batch_size, sequence_length, num_channels).

 Swin Transformer architecture. Taken from the original paper.
This model was contributed by novice03. The Tensorflow version of this model was contributed by amyeroberts. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Swin Transformer.

[SwinForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

Besides that:

[SwinForMaskedImageModeling] is supported by this example script.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
SwinConfig
[[autodoc]] SwinConfig
SwinModel
[[autodoc]] SwinModel
    - forward
SwinForMaskedImageModeling
[[autodoc]] SwinForMaskedImageModeling
    - forward
SwinForImageClassification
[[autodoc]] transformers.SwinForImageClassification
    - forward
TFSwinModel
[[autodoc]] TFSwinModel
    - call
TFSwinForMaskedImageModeling
[[autodoc]] TFSwinForMaskedImageModeling
    - call
TFSwinForImageClassification
[[autodoc]] transformers.TFSwinForImageClassification
    - call"
"
GPT-NeoX-Japanese
Overview
We introduce GPT-NeoX-Japanese, which is an autoregressive language model for Japanese, trained on top of https://github.com/EleutherAI/gpt-neox.
Japanese is a unique language with its large vocabulary and a combination of hiragana, katakana, and kanji writing scripts.
To address this distinct structure of the Japanese language, we use a special sub-word tokenizer. We are very grateful to tanreinama for open-sourcing this incredibly helpful tokenizer.
Following the recommendations from Google's research on PaLM, we have removed bias parameters from transformer blocks, achieving better model performance. Please refer this article in detail.
Development of the model was led by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori from ABEJA, Inc.. For more information on this model-building activity, please refer here (ja).
Generation
The generate() method can be used to generate text using GPT NeoX Japanese model.
thon

from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseTokenizer
model = GPTNeoXJapaneseForCausalLM.from_pretrained(""abeja/gpt-neox-japanese-2.7b"")
tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained(""abeja/gpt-neox-japanese-2.7b"")
prompt = ""人とAIが協調するためには、""
input_ids = tokenizer(prompt, return_tensors=""pt"").input_ids
gen_tokens = model.generate(
     input_ids,
     do_sample=True,
     temperature=0.9,
     max_length=100,
 )
gen_text = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)[0]
print(gen_text)
人とAIが協調するためには、AIと人が共存し、AIを正しく理解する必要があります。

Documentation resources

Causal language modeling task guide

GPTNeoXJapaneseConfig
[[autodoc]] GPTNeoXJapaneseConfig
GPTNeoXJapaneseTokenizer
[[autodoc]] GPTNeoXJapaneseTokenizer
GPTNeoXJapaneseModel
[[autodoc]] GPTNeoXJapaneseModel
    - forward
GPTNeoXJapaneseForCausalLM
[[autodoc]] GPTNeoXJapaneseForCausalLM
    - forward"
"
LayoutXLM
Overview
LayoutXLM was proposed in LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha
Zhang, Furu Wei. It's a multilingual extension of the LayoutLMv2 model trained
on 53 languages.
The abstract from the paper is the following:
Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document
understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In
this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to
bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also
introduce a multilingual form understanding benchmark dataset named XFUN, which includes form understanding samples in
7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled
for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA
cross-lingual pre-trained models on the XFUN dataset.
One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like so:
thon
from transformers import LayoutLMv2Model
model = LayoutLMv2Model.from_pretrained(""microsoft/layoutxlm-base"")

Note that LayoutXLM has its own tokenizer, based on
[LayoutXLMTokenizer]/[LayoutXLMTokenizerFast]. You can initialize it as
follows:
thon
from transformers import LayoutXLMTokenizer
tokenizer = LayoutXLMTokenizer.from_pretrained(""microsoft/layoutxlm-base"")

Similar to LayoutLMv2, you can use [LayoutXLMProcessor] (which internally applies
[LayoutLMv2ImageProcessor] and
[LayoutXLMTokenizer]/[LayoutXLMTokenizerFast] in sequence) to prepare all
data for the model.
As LayoutXLM's architecture is equivalent to that of LayoutLMv2, one can refer to LayoutLMv2's documentation page for all tips, code examples and notebooks.
This model was contributed by nielsr. The original code can be found here.
LayoutXLMTokenizer
[[autodoc]] LayoutXLMTokenizer
    - call
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
LayoutXLMTokenizerFast
[[autodoc]] LayoutXLMTokenizerFast
    - call
LayoutXLMProcessor
[[autodoc]] LayoutXLMProcessor
    - call"
"
CPMAnt
Overview
CPM-Ant is an open-source Chinese pre-trained language model (PLM) with 10B parameters. It is also the first milestone of the live training process of CPM-Live. The training process is cost-effective and environment-friendly. CPM-Ant also achieves promising results with delta tuning on the CUGE benchmark. Besides the full model, we also provide various compressed versions to meet the requirements of different hardware configurations. See more
Tips:
This model was contributed by OpenBMB. The original code can be found here.
⚙️ Training & Inference
- A tutorial on CPM-Live.
CpmAntConfig
[[autodoc]] CpmAntConfig
    - all
CpmAntTokenizer
[[autodoc]] CpmAntTokenizer
    - all
CpmAntModel
[[autodoc]] CpmAntModel
    - all
CpmAntForCausalLM
[[autodoc]] CpmAntForCausalLM
    - all"
"
PhoBERT
Overview
The PhoBERT model was proposed in PhoBERT: Pre-trained language models for Vietnamese by Dat Quoc Nguyen, Anh Tuan Nguyen.
The abstract from the paper is the following:
We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual
language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent
best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple
Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and
Natural language inference.
Example of use:
thon

import torch
from transformers import AutoModel, AutoTokenizer
phobert = AutoModel.from_pretrained(""vinai/phobert-base"")
tokenizer = AutoTokenizer.from_pretrained(""vinai/phobert-base"")
INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
line = ""Tôi là sinh_viên trường đại_học Công_nghệ .""
input_ids = torch.tensor([tokenizer.encode(line)])
with torch.no_grad():
     features = phobert(input_ids)  # Models outputs are now tuples
With TensorFlow 2.0+:
from transformers import TFAutoModel
phobert = TFAutoModel.from_pretrained(""vinai/phobert-base"")

This model was contributed by dqnguyen. The original code can be found here.
PhobertTokenizer
[[autodoc]] PhobertTokenizer"
"
Chinese-CLIP
Overview
The Chinese-CLIP model was proposed in Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
Chinese-CLIP is an implementation of CLIP (Radford et al., 2021) on a large-scale dataset of Chinese image-text pairs. It is capable of performing cross-modal retrieval and also playing as a vision backbone for vision tasks like zero-shot image classification, open-domain object detection, etc. The original Chinese-CLIP code is released at this link.
The abstract from the paper is the following:
The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining. In this work, we construct a large-scale dataset of image-text pairs in Chinese, where most data are retrieved from publicly available datasets, and we pretrain Chinese CLIP models on the new dataset. We develop 5 Chinese CLIP models of multiple sizes, spanning from 77 to 958 million parameters. Furthermore, we propose a two-stage pretraining method, where the model is first trained with the image encoder frozen and then trained with all parameters being optimized, to achieve enhanced model performance. Our comprehensive experiments demonstrate that Chinese CLIP can achieve the state-of-the-art performance on MUGE, Flickr30K-CN, and COCO-CN in the setups of zero-shot learning and finetuning, and it is able to achieve competitive performance in zero-shot image classification based on the evaluation on the ELEVATER benchmark (Li et al., 2022). Our codes, pretrained models, and demos have been released.
Usage
The code snippet below shows how to compute image & text features and similarities:
thon

from PIL import Image
import requests
from transformers import ChineseCLIPProcessor, ChineseCLIPModel
model = ChineseCLIPModel.from_pretrained(""OFA-Sys/chinese-clip-vit-base-patch16"")
processor = ChineseCLIPProcessor.from_pretrained(""OFA-Sys/chinese-clip-vit-base-patch16"")
url = ""https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg""
image = Image.open(requests.get(url, stream=True).raw)
Squirtle, Bulbasaur, Charmander, Pikachu in English
texts = [""杰尼龟"", ""妙蛙种子"", ""小火龙"", ""皮卡丘""]
compute image feature
inputs = processor(images=image, return_tensors=""pt"")
image_features = model.get_image_features(**inputs)
image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)  # normalize
compute text features
inputs = processor(text=texts, padding=True, return_tensors=""pt"")
text_features = model.get_text_features(**inputs)
text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)  # normalize
compute image-text similarity scores
inputs = processor(text=texts, images=image, return_tensors=""pt"", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1)  # probs: [[1.2686e-03, 5.4499e-02, 6.7968e-04, 9.4355e-01]]

Currently, we release the following scales of pretrained Chinese-CLIP models at HF Model Hub:

OFA-Sys/chinese-clip-vit-base-patch16
OFA-Sys/chinese-clip-vit-large-patch14
OFA-Sys/chinese-clip-vit-large-patch14-336px
OFA-Sys/chinese-clip-vit-huge-patch14

The Chinese-CLIP model was contributed by OFA-Sys. 
ChineseCLIPConfig
[[autodoc]] ChineseCLIPConfig
    - from_text_vision_configs
ChineseCLIPTextConfig
[[autodoc]] ChineseCLIPTextConfig
ChineseCLIPVisionConfig
[[autodoc]] ChineseCLIPVisionConfig
ChineseCLIPImageProcessor
[[autodoc]] ChineseCLIPImageProcessor
    - preprocess
ChineseCLIPFeatureExtractor
[[autodoc]] ChineseCLIPFeatureExtractor
ChineseCLIPProcessor
[[autodoc]] ChineseCLIPProcessor
ChineseCLIPModel
[[autodoc]] ChineseCLIPModel
    - forward
    - get_text_features
    - get_image_features
ChineseCLIPTextModel
[[autodoc]] ChineseCLIPTextModel
    - forward
ChineseCLIPVisionModel
[[autodoc]] ChineseCLIPVisionModel
    - forward"
"
MegatronBERT
Overview
The MegatronBERT model was proposed in Megatron-LM: Training Multi-Billion Parameter Language Models Using Model
Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley,
Jared Casper and Bryan Catanzaro.
The abstract from the paper is the following:
Recent work in language modeling demonstrates that training large transformer models advances the state of the art in
Natural Language Processing applications. However, very large models can be quite difficult to train due to memory
constraints. In this work, we present our techniques for training very large transformer models and implement a simple,
efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our
approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model
parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We
illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain
15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline
that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance
the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9
billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in
BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we
achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA
accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy
of 89.4%).
Tips:
We have provided pretrained BERT-345M checkpoints
for use to evaluate or finetuning downstream tasks.
To access these checkpoints, first sign up for and setup the NVIDIA GPU Cloud (NGC)
Registry CLI. Further documentation for downloading models can be found in the NGC documentation.
Alternatively, you can directly download the checkpoints using:
BERT-345M-uncased:

wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_uncased/zip
-O megatron_bert_345m_v0_1_uncased.zip
BERT-345M-cased:

wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O
megatron_bert_345m_v0_1_cased.zip
Once you have obtained the checkpoints from NVIDIA GPU Cloud (NGC), you have to convert them to a format that will
easily be loaded by Hugging Face Transformers and our port of the BERT code.
The following commands allow you to do the conversion. We assume that the folder models/megatron_bert contains
megatron_bert_345m_v0_1_{cased, uncased}.zip and that the commands are run from inside that folder:

python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_uncased.zip

python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_cased.zip
This model was contributed by jdemouth. The original code can be found here. That repository contains a multi-GPU and multi-node implementation of the
Megatron Language models. In particular, it contains a hybrid model parallel approach using ""tensor parallel"" and
""pipeline parallel"" techniques.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

MegatronBertConfig
[[autodoc]] MegatronBertConfig
MegatronBertModel
[[autodoc]] MegatronBertModel
    - forward
MegatronBertForMaskedLM
[[autodoc]] MegatronBertForMaskedLM
    - forward
MegatronBertForCausalLM
[[autodoc]] MegatronBertForCausalLM
    - forward
MegatronBertForNextSentencePrediction
[[autodoc]] MegatronBertForNextSentencePrediction
    - forward
MegatronBertForPreTraining
[[autodoc]] MegatronBertForPreTraining
    - forward
MegatronBertForSequenceClassification
[[autodoc]] MegatronBertForSequenceClassification
    - forward
MegatronBertForMultipleChoice
[[autodoc]] MegatronBertForMultipleChoice
    - forward
MegatronBertForTokenClassification
[[autodoc]] MegatronBertForTokenClassification
    - forward
MegatronBertForQuestionAnswering
[[autodoc]] MegatronBertForQuestionAnswering
    - forward"
"
MarianMT

Bugs: If you see something strange, file a Github Issue
and assign @patrickvonplaten.
Translations should be similar, but not identical to output in the test set linked to in each model card.
Tips:

A framework for translation models, using the same models as BART.

Implementation Notes

Each model is about 298 MB on disk, there are more than 1,000 models.
The list of supported language pairs can be found here.
Models were originally trained by Jörg Tiedemann using the Marian C++ library, which supports fast training and translation.
All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented
  in a model card.
The 80 opus models that require BPE preprocessing are not supported.

The modeling code is the same as [BartForConditionalGeneration] with a few minor modifications:

static (sinusoid) positional embeddings (MarianConfig.static_position_embeddings=True)

no layernorm_embedding (MarianConfig.normalize_embedding=False)
the model starts generating with pad_token_id (which has 0 as a token_embedding) as the prefix (Bart uses
    <s/>),
Code to bulk convert models can be found in convert_marian_to_pytorch.py.
This model was contributed by sshleifer.

Naming

All model names use the following format: Helsinki-NLP/opus-mt-{src}-{tgt}
The language codes used to name models are inconsistent. Two digit codes can usually be found here, three digit codes require googling ""language
  code {code}"".
Codes formatted like es_AR are usually code_{region}. That one is Spanish from Argentina.
The models were converted in two stages. The first 1000 models use ISO-639-2 codes to identify languages, the second
  group use a combination of ISO-639-5 codes and ISO-639-2 codes.

Examples

Since Marian models are smaller than many other translation models available in the library, they can be useful for
  fine-tuning experiments and integration tests.
Fine-tune on GPU

Multilingual Models

All model names use the following format: Helsinki-NLP/opus-mt-{src}-{tgt}:
If a model can output multiple languages, and you should specify a language code by prepending the desired output
  language to the src_text.
You can see a models's supported language codes in its model card, under target constituents, like in opus-mt-en-roa.
Note that if a model is only multilingual on the source side, like Helsinki-NLP/opus-mt-roa-en, no language
  codes are required.

New multi-lingual models from the Tatoeba-Challenge repo
require 3 character language codes:
thon

from transformers import MarianMTModel, MarianTokenizer
src_text = [
     "">>fra<< this is a sentence in english that we want to translate to french"",
     "">>por<< This should go to portuguese"",
     "">>esp<< And this to Spanish"",
 ]
model_name = ""Helsinki-NLP/opus-mt-en-roa""
tokenizer = MarianTokenizer.from_pretrained(model_name)
print(tokenizer.supported_language_codes)
['>>zlm_Latn<<', '>>mfe<<', '>>hat<<', '>>pap<<', '>>ast<<', '>>cat<<', '>>ind<<', '>>glg<<', '>>wln<<', '>>spa<<', '>>fra<<', '>>ron<<', '>>por<<', '>>ita<<', '>>oci<<', '>>arg<<', '>>min<<']
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors=""pt"", padding=True))
[tokenizer.decode(t, skip_special_tokens=True) for t in translated]
[""c'est une phrase en anglais que nous voulons traduire en français"",
 'Isto deve ir para o português.',
 'Y esto al español']

Here is the code to see all available pretrained models on the hub:
thon
from huggingface_hub import list_models
model_list = list_models()
org = ""Helsinki-NLP""
model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
suffix = [x.split(""/"")[1] for x in model_ids]
old_style_multi_models = [f""{org}/{s}"" for s in suffix if s != s.lower()]

Old Style Multi-Lingual Models
These are the old style multi-lingual models ported from the OPUS-MT-Train repo: and the members of each language
group:
python no-style
['Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU',
 'Helsinki-NLP/opus-mt-ROMANCE-en',
 'Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA',
 'Helsinki-NLP/opus-mt-de-ZH',
 'Helsinki-NLP/opus-mt-en-CELTIC',
 'Helsinki-NLP/opus-mt-en-ROMANCE',
 'Helsinki-NLP/opus-mt-es-NORWAY',
 'Helsinki-NLP/opus-mt-fi-NORWAY',
 'Helsinki-NLP/opus-mt-fi-ZH',
 'Helsinki-NLP/opus-mt-fi_nb_no_nn_ru_sv_en-SAMI',
 'Helsinki-NLP/opus-mt-sv-NORWAY',
 'Helsinki-NLP/opus-mt-sv-ZH']
GROUP_MEMBERS = {
 'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
 'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
 'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
 'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
 'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
 'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
 'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
}
Example of translating english to many romance languages, using old-style 2 character language codes
thon

from transformers import MarianMTModel, MarianTokenizer
src_text = [
     "">>fr<< this is a sentence in english that we want to translate to french"",
     "">>pt<< This should go to portuguese"",
     "">>es<< And this to Spanish"",
 ]
model_name = ""Helsinki-NLP/opus-mt-en-ROMANCE""
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors=""pt"", padding=True))
tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
[""c'est une phrase en anglais que nous voulons traduire en français"", 
 'Isto deve ir para o português.',
 'Y esto al español']

Documentation resources

Translation task guide
Summarization task guide
Causal language modeling task guide

MarianConfig
[[autodoc]] MarianConfig
MarianTokenizer
[[autodoc]] MarianTokenizer
    - build_inputs_with_special_tokens
MarianModel
[[autodoc]] MarianModel
    - forward
MarianMTModel
[[autodoc]] MarianMTModel
    - forward
MarianForCausalLM
[[autodoc]] MarianForCausalLM
    - forward
TFMarianModel
[[autodoc]] TFMarianModel
    - call
TFMarianMTModel
[[autodoc]] TFMarianMTModel
    - call
FlaxMarianModel
[[autodoc]] FlaxMarianModel
    - call
FlaxMarianMTModel
[[autodoc]] FlaxMarianMTModel
    - call"
"
Pegasus

DISCLAIMER: If you see something strange, file a Github Issue
and assign @patrickvonplaten.
Overview
The Pegasus model was proposed in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
According to the abstract,

Pegasus' pretraining task is intentionally similar to summarization: important sentences are removed/masked from an
  input document and are generated together as one output sequence from the remaining sentences, similar to an
  extractive summary.
Pegasus achieves SOTA summarization performance on all 12 downstream tasks, as measured by ROUGE and human eval.

This model was contributed by sshleifer. The Authors' code can be found here.
Tips:

Sequence-to-sequence model with the same encoder-decoder model architecture as BART. Pegasus is pre-trained jointly on two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pretraining objective, called Gap Sentence Generation (GSG).

MLM: encoder input tokens are randomly replaced by a mask tokens and have to be predicted by the encoder (like in BERT)

GSG: whole encoder input sentences are replaced by a second mask token and fed to the decoder, but which has a causal mask to hide the future words like a regular auto-regressive transformer decoder.

Checkpoints
All the checkpoints are fine-tuned for summarization, besides
pegasus-large, whence the other checkpoints are fine-tuned:

Each checkpoint is 2.2 GB on disk and 568M parameters.
FP16 is not supported (help/ideas on this appreciated!).
Summarizing xsum in fp32 takes about 400ms/sample, with default parameters on a v100 GPU.
Full replication results and correctly pre-processed data can be found in this Issue.
Distilled checkpoints are described in this paper.

Examples

Script to fine-tune pegasus
  on the XSUM dataset. Data download instructions at examples/pytorch/summarization/.
FP16 is not supported (help/ideas on this appreciated!).
The adafactor optimizer is recommended for pegasus fine-tuning.

Implementation Notes

All models are transformer encoder-decoders with 16 layers in each component.
The implementation is completely inherited from [BartForConditionalGeneration]

Some key configuration differences:

static, sinusoidal position embeddings

the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix.
more beams are used (num_beams=8)
All pretrained pegasus checkpoints are the same besides three attributes: tokenizer.model_max_length (maximum
  input size), max_length (the maximum number of tokens to generate) and length_penalty.
The code to convert checkpoints trained in the author's repo can be
  found in convert_pegasus_tf_to_pytorch.py.

Usage Example
thon

from transformers import PegasusForConditionalGeneration, PegasusTokenizer
import torch
src_text = [
     """""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""""""
 ]

 model_name = ""google/pegasus-xsum""
 device = ""cuda"" if torch.cuda.is_available() else ""cpu""
 tokenizer = PegasusTokenizer.from_pretrained(model_name)
 model = PegasusForConditionalGeneration.from_pretrained(model_name).to(device)
 batch = tokenizer(src_text, truncation=True, padding=""longest"", return_tensors=""pt"").to(device)
 translated = model.generate(**batch)
 tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
 assert (
     tgt_text[0]
     == ""California's largest electricity provider has turned off power to hundreds of thousands of customers.""
 )

Documentation resources

Causal language modeling task guide
Translation task guide
Summarization task guide

PegasusConfig
[[autodoc]] PegasusConfig
PegasusTokenizer
warning: add_tokens does not work at the moment.
[[autodoc]] PegasusTokenizer
PegasusTokenizerFast
[[autodoc]] PegasusTokenizerFast
PegasusModel
[[autodoc]] PegasusModel
    - forward
PegasusForConditionalGeneration
[[autodoc]] PegasusForConditionalGeneration
    - forward
PegasusForCausalLM
[[autodoc]] PegasusForCausalLM
    - forward
TFPegasusModel
[[autodoc]] TFPegasusModel
    - call
TFPegasusForConditionalGeneration
[[autodoc]] TFPegasusForConditionalGeneration
    - call
FlaxPegasusModel
[[autodoc]] FlaxPegasusModel
    - call
    - encode
    - decode
FlaxPegasusForConditionalGeneration
[[autodoc]] FlaxPegasusForConditionalGeneration
    - call
    - encode
    - decode"
"
X-MOD
Overview
The X-MOD model was proposed in Lifting the Curse of Multilinguality by Pre-training Modular Transformers by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, and Mikel Artetxe.
X-MOD extends multilingual masked language models like XLM-R to include language-specific modular components (language adapters) during pre-training. For fine-tuning, the language adapters in each transformer layer are frozen.
The abstract from the paper is the following:
Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant. In contrast with prior work that learns language-specific components post-hoc, we pre-train the modules of our Cross-lingual Modular (X-MOD) models from the start. Our experiments on natural language inference, named entity recognition and question answering show that our approach not only mitigates the negative interference between languages, but also enables positive transfer, resulting in improved monolingual and cross-lingual performance. Furthermore, our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages.
Tips:
- X-MOD is similar to XLM-R, but a difference is that the input language needs to be specified so that the correct language adapter can be activated.
- The main models – base and large – have adapters for 81 languages.
This model was contributed by jvamvas.
The original code can be found here and the original documentation is found here.
Adapter Usage
Input language
There are two ways to specify the input language:
1. By setting a default language before using the model:
thon
from transformers import XmodModel
model = XmodModel.from_pretrained(""facebook/xmod-base"")
model.set_default_language(""en_XX"")

By explicitly passing the index of the language adapter for each sample:

thon
import torch
input_ids = torch.tensor(
    [
        [0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2],
        [0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2],
    ]
)
lang_ids = torch.LongTensor(
    [
        0,  # en_XX
        8,  # de_DE
    ]
)
output = model(input_ids, lang_ids=lang_ids)

Fine-tuning
The paper recommends that the embedding layer and the language adapters are frozen during fine-tuning. A method for doing this is provided:
thon
model.freeze_embeddings_and_language_adapters()
Fine-tune the model 

Cross-lingual transfer
After fine-tuning, zero-shot cross-lingual transfer can be tested by activating the language adapter of the target language:
thon
model.set_default_language(""de_DE"")
Evaluate the model on German examples 

Resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

XmodConfig
[[autodoc]] XmodConfig
XmodModel
[[autodoc]] XmodModel
    - forward
XmodForCausalLM
[[autodoc]] XmodForCausalLM
    - forward
XmodForMaskedLM
[[autodoc]] XmodForMaskedLM
    - forward
XmodForSequenceClassification
[[autodoc]] XmodForSequenceClassification
    - forward
XmodForMultipleChoice
[[autodoc]] XmodForMultipleChoice
    - forward
XmodForTokenClassification
[[autodoc]] XmodForTokenClassification
    - forward
XmodForQuestionAnswering
[[autodoc]] XmodForQuestionAnswering
    - forward"
"
I-BERT
Overview
The I-BERT model was proposed in I-BERT: Integer-only BERT Quantization by
Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney and Kurt Keutzer. It's a quantized version of RoBERTa running
inference up to four times faster.
The abstract from the paper is the following:
Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language
Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive for
efficient inference at the edge, and even at the data center. While quantization can be a viable solution for this,
previous work on quantizing Transformer based models use floating-point arithmetic during inference, which cannot
efficiently utilize integer-only logical units such as the recent Turing Tensor Cores, or traditional integer-only ARM
processors. In this work, we propose I-BERT, a novel quantization scheme for Transformer based models that quantizes
the entire inference with integer-only arithmetic. Based on lightweight integer-only approximation methods for
nonlinear operations, e.g., GELU, Softmax, and Layer Normalization, I-BERT performs an end-to-end integer-only BERT
inference without any floating point calculation. We evaluate our approach on GLUE downstream tasks using
RoBERTa-Base/Large. We show that for both cases, I-BERT achieves similar (and slightly higher) accuracy as compared to
the full-precision baseline. Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4 - 4.0x for
INT8 inference on a T4 GPU system as compared to FP32 inference. The framework has been developed in PyTorch and has
been open-sourced.
This model was contributed by kssteven. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

IBertConfig
[[autodoc]] IBertConfig
IBertModel
[[autodoc]] IBertModel
    - forward
IBertForMaskedLM
[[autodoc]] IBertForMaskedLM
    - forward
IBertForSequenceClassification
[[autodoc]] IBertForSequenceClassification
    - forward
IBertForMultipleChoice
[[autodoc]] IBertForMultipleChoice
    - forward
IBertForTokenClassification
[[autodoc]] IBertForTokenClassification
    - forward
IBertForQuestionAnswering
[[autodoc]] IBertForQuestionAnswering
    - forward"
"
Jukebox
Overview
The Jukebox model was proposed in Jukebox: A generative model for music
by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford,
Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditioned on
an artist, genres and lyrics.
The abstract from the paper is the following:
We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multiscale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples, along with model weights and code.
As shown on the following figure, Jukebox is made of 3 priors which are decoder only models. They follow the architecture described in Generating Long Sequences with Sparse Transformers, modified to support longer context length.
First, a autoencoder is used to encode the text lyrics. Next, the first (also called top_prior) prior attends to the last hidden states extracted from the lyrics encoder. The priors are linked to the previous priors respectively via an AudioConditionner module. TheAudioConditioner upsamples the outputs of the previous prior to raw tokens at a certain audio frame per second resolution. 
The metadata such as artist, genre and timing are passed to each prior, in the form of a start token and positionnal embedding for the timing data.  The hidden states are mapped to the closest codebook vector from the VQVAE in order to convert them to raw audio.

Tips:
- This model only supports inference. This is for a few reasons, mostly because it requires a crazy amount of memory to train. Feel free to open a PR and add what's missing to have a full integration with the hugging face traineer!
- This model is very slow, and takes 8h to generate a minute long audio using the 5b top prior on a V100 GPU. In order automaticallay handle the device on which the model should execute, use accelerate.
- Contrary to the paper, the order of the priors goes from 0 to 1 as it felt more intuitive : we sample starting from 0.
- Primed sampling (conditionning the sampling on raw audio) requires more memory than ancestral sampling and should be used with fp16 set to True.
This model was contributed by Arthur Zucker.
The original code can be found here.
JukeboxConfig
[[autodoc]] JukeboxConfig
JukeboxPriorConfig
[[autodoc]] JukeboxPriorConfig
JukeboxVQVAEConfig
[[autodoc]] JukeboxVQVAEConfig
JukeboxTokenizer
[[autodoc]] JukeboxTokenizer
    - save_vocabulary
JukeboxModel
[[autodoc]] JukeboxModel
    - ancestral_sample
    - primed_sample
    - continue_sample
    - upsample
    - _sample
JukeboxPrior
[[autodoc]] JukeboxPrior
    - sample
    - forward
JukeboxVQVAE
[[autodoc]] JukeboxVQVAE
    - forward
    - encode
    - decode"
"
Autoformer
Overview
The Autoformer model was proposed in Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.
The abstract from the paper is the following:
Extending the forecasting time is a critical demand for real applications, such as extreme weather early warning and long-term energy consumption planning. This paper studies the long-term forecasting problem of time series. Prior Transformer-based models adopt various self-attention mechanisms to discover the long-range dependencies. However, intricate temporal patterns of the long-term future prohibit the model from finding reliable dependencies. Also, Transformers have to adopt the sparse versions of point-wise self-attentions for long series efficiency, resulting in the information utilization bottleneck. Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We break with the pre-processing convention of series decomposition and renovate it as a basic inner block of deep models. This design empowers Autoformer with progressive decomposition capacities for complex time series. Further, inspired by the stochastic process theory, we design the Auto-Correlation mechanism based on the series periodicity, which conducts the dependencies discovery and representation aggregation at the sub-series level. Auto-Correlation outperforms self-attention in both efficiency and accuracy. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a 38% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and disease.
This model was contributed by elisim and kashif.
The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

Check out the Autoformer blog-post in HuggingFace blog: Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)

AutoformerConfig
[[autodoc]] AutoformerConfig
AutoformerModel
[[autodoc]] AutoformerModel
    - forward
AutoformerForPrediction
[[autodoc]] AutoformerForPrediction
    - forward"
"
XLM

Overview
The XLM model was proposed in Cross-lingual Language Model Pretraining by
Guillaume Lample, Alexis Conneau. It's a transformer pretrained using one of the following objectives:

a causal language modeling (CLM) objective (next token prediction),
a masked language modeling (MLM) objective (BERT-like), or
a Translation Language Modeling (TLM) object (extension of BERT's MLM to multiple language inputs)

The abstract from the paper is the following:
Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding.
In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We
propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain
state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our
approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we
obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised
machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the
previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.
Tips:

XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
  select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
XLM has multilingual checkpoints which leverage a specific lang parameter. Check out the multi-lingual page for more information.

A transformer model trained on several languages. There are three different type of training for this model and the library provides checkpoints for all of them:

Causal language modeling (CLM) which is the traditional autoregressive training (so this model could be in the previous section as well). One of the languages is selected for each training sample, and the model input is a sentence of 256 tokens, that may span over several documents in one of those languages.
Masked language modeling (MLM) which is like RoBERTa. One of the languages is selected for each training sample, and the model input is a sentence of 256 tokens, that may span over several documents in one of those languages, with dynamic masking of the tokens.
A combination of MLM and translation language modeling (TLM). This consists of concatenating a sentence in two different languages, with random masking. To predict one of the masked tokens, the model can use both, the surrounding context in language 1 and the context given by language 2.

This model was contributed by thomwolf. The original code can be found here.
Documentation resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

XLMConfig
[[autodoc]] XLMConfig
XLMTokenizer
[[autodoc]] XLMTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
XLM specific outputs
[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
XLMModel
[[autodoc]] XLMModel
    - forward
XLMWithLMHeadModel
[[autodoc]] XLMWithLMHeadModel
    - forward
XLMForSequenceClassification
[[autodoc]] XLMForSequenceClassification
    - forward
XLMForMultipleChoice
[[autodoc]] XLMForMultipleChoice
    - forward
XLMForTokenClassification
[[autodoc]] XLMForTokenClassification
    - forward
XLMForQuestionAnsweringSimple
[[autodoc]] XLMForQuestionAnsweringSimple
    - forward
XLMForQuestionAnswering
[[autodoc]] XLMForQuestionAnswering
    - forward
TFXLMModel
[[autodoc]] TFXLMModel
    - call
TFXLMWithLMHeadModel
[[autodoc]] TFXLMWithLMHeadModel
    - call
TFXLMForSequenceClassification
[[autodoc]] TFXLMForSequenceClassification
    - call
TFXLMForMultipleChoice
[[autodoc]] TFXLMForMultipleChoice
    - call
TFXLMForTokenClassification
[[autodoc]] TFXLMForTokenClassification
    - call
TFXLMForQuestionAnsweringSimple
[[autodoc]] TFXLMForQuestionAnsweringSimple
    - call"
"
AltCLIP
Overview
The AltCLIP model was proposed in AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu. AltCLIP
(Altering the Language Encoder in CLIP) is a neural network trained on a variety of image-text and text-text pairs. By switching CLIP's
text encoder with a pretrained multilingual text encoder XLM-R, we could obtain very close performances with CLIP on almost all tasks, and extended original CLIP's capabilities such as multilingual understanding.
The abstract from the paper is the following:
In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. 
Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained 
multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of 
teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art 
performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with 
CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.
Usage
The usage of AltCLIP is very similar to the CLIP. the difference between CLIP is the text encoder. Note that we use bidirectional attention instead of casual attention
and we take the [CLS] token in XLM-R to represent text embedding.
AltCLIP is a multi-modal vision and language model. It can be used for image-text similarity and for zero-shot image
classification. AltCLIP uses a ViT like transformer to get visual features and a bidirectional language model to get the text
features. Both the text and visual features are then projected to a latent space with identical dimension. The dot
product between the projected image and text features is then used as a similar score.
To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image. The authors
also add absolute position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder.
The [CLIPImageProcessor] can be used to resize (or rescale) and normalize images for the model.
The [AltCLIPProcessor] wraps a [CLIPImageProcessor] and a [XLMRobertaTokenizer] into a single instance to both
encode the text and prepare the images. The following example shows how to get the image-text similarity scores using
[AltCLIPProcessor] and [AltCLIPModel].
thon

from PIL import Image
import requests
from transformers import AltCLIPModel, AltCLIPProcessor
model = AltCLIPModel.from_pretrained(""BAAI/AltCLIP"")
processor = AltCLIPProcessor.from_pretrained(""BAAI/AltCLIP"")
url = ""http://images.cocodataset.org/val2017/000000039769.jpg""
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=[""a photo of a cat"", ""a photo of a dog""], images=image, return_tensors=""pt"", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities

Tips:
This model is build on CLIPModel, so use it like a original CLIP. 
This model was contributed by jongjyh.
AltCLIPConfig
[[autodoc]] AltCLIPConfig
    - from_text_vision_configs
AltCLIPTextConfig
[[autodoc]] AltCLIPTextConfig
AltCLIPVisionConfig
[[autodoc]] AltCLIPVisionConfig
AltCLIPProcessor
[[autodoc]] AltCLIPProcessor
AltCLIPModel
[[autodoc]] AltCLIPModel
    - forward
    - get_text_features
    - get_image_features
AltCLIPTextModel
[[autodoc]] AltCLIPTextModel
    - forward
AltCLIPVisionModel
[[autodoc]] AltCLIPVisionModel
    - forward"
"
Convolutional Vision Transformer (CvT)
Overview
The CvT model was proposed in CvT: Introducing Convolutions to Vision Transformers by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs.
The abstract from the paper is the following:
We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) 
in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through 
two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer 
block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) 
to the ViT architecture (\ie shift, scale, and distortion invariance) while maintaining the merits of Transformers (\ie dynamic attention, 
global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves 
state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition, 
performance gains are maintained when pretrained on larger datasets (\eg ImageNet-22k) and fine-tuned to downstream tasks. Pre-trained on 
ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7\% on the ImageNet-1k val set. Finally, our results show that the positional encoding, 
a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks.
Tips:

CvT models are regular Vision Transformers, but trained with convolutions. They outperform the original model (ViT) when fine-tuned on ImageNet-1K and CIFAR-100.
You can check out demo notebooks regarding inference as well as fine-tuning on custom data here (you can just replace [ViTFeatureExtractor] by [AutoImageProcessor] and [ViTForImageClassification] by [CvtForImageClassification]).
The available checkpoints are either (1) pre-trained on ImageNet-22k (a collection of 14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million
  images and 1,000 classes).

This model was contributed by anugunj. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CvT.

[CvtForImageClassification] is supported by this example script and notebook.
See also: Image classification task guide

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
CvtConfig
[[autodoc]] CvtConfig
CvtModel
[[autodoc]] CvtModel
    - forward
CvtForImageClassification
[[autodoc]] CvtForImageClassification
    - forward
TFCvtModel
[[autodoc]] TFCvtModel
    - call
TFCvtForImageClassification
[[autodoc]] TFCvtForImageClassification
    - call"
"
GLPN

This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a Github Issue.

Overview
The GLPN model was proposed in Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth  by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
GLPN combines SegFormer's hierarchical mix-Transformer with a lightweight decoder for monocular depth estimation. The proposed decoder shows better performance than the previously proposed decoders, with considerably
less computational complexity.
The abstract from the paper is the following:
Depth estimation from a single image is an important task that can be applied to various fields in computer vision, and has grown rapidly with the development of convolutional neural networks. In this paper, we propose a novel structure and training strategy for monocular depth estimation to further improve the prediction accuracy of the network. We deploy a hierarchical transformer encoder to capture and convey the global context, and design a lightweight yet powerful decoder to generate an estimated depth map while considering local connectivity. By constructing connected paths between multi-scale local features and the global decoding stream with our proposed selective feature fusion module, the network can integrate both representations and recover fine details. In addition, the proposed decoder shows better performance than the previously proposed decoders, with considerably less computational complexity. Furthermore, we improve the depth-specific augmentation method by utilizing an important observation in depth estimation to enhance the model. Our network achieves state-of-the-art performance over the challenging depth dataset NYU Depth V2. Extensive experiments have been conducted to validate and show the effectiveness of the proposed approach. Finally, our model shows better generalisation ability and robustness than other comparative models.
Tips:

One can use [GLPNImageProcessor] to prepare images for the model.

 Summary of the approach. Taken from the original paper. 
This model was contributed by nielsr. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GLPN.

Demo notebooks for [GLPNForDepthEstimation] can be found here.
Monocular depth estimation task guide

GLPNConfig
[[autodoc]] GLPNConfig
GLPNFeatureExtractor
[[autodoc]] GLPNFeatureExtractor
    - call
GLPNImageProcessor
[[autodoc]] GLPNImageProcessor
    - preprocess
GLPNModel
[[autodoc]] GLPNModel
    - forward
GLPNForDepthEstimation
[[autodoc]] GLPNForDepthEstimation
    - forward"
"
DPR

Overview
Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was
introduced in Dense Passage Retrieval for Open-Domain Question Answering by
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.
The abstract from the paper is the following:
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional
sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can
be practically implemented using dense representations alone, where embeddings are learned from a small number of
questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets,
our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage
retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
benchmarks.
This model was contributed by lhoestq. The original code can be found here.
Tips:
- DPR consists in three models:
* Question encoder: encode questions as vectors
* Context encoder: encode contexts as vectors
* Reader: extract the answer of the questions inside retrieved contexts, along with a relevance score (high if the inferred span actually answers the question).

DPRConfig
[[autodoc]] DPRConfig
DPRContextEncoderTokenizer
[[autodoc]] DPRContextEncoderTokenizer
DPRContextEncoderTokenizerFast
[[autodoc]] DPRContextEncoderTokenizerFast
DPRQuestionEncoderTokenizer
[[autodoc]] DPRQuestionEncoderTokenizer
DPRQuestionEncoderTokenizerFast
[[autodoc]] DPRQuestionEncoderTokenizerFast
DPRReaderTokenizer
[[autodoc]] DPRReaderTokenizer
DPRReaderTokenizerFast
[[autodoc]] DPRReaderTokenizerFast
DPR specific outputs
[[autodoc]] models.dpr.modeling_dpr.DPRContextEncoderOutput
[[autodoc]] models.dpr.modeling_dpr.DPRQuestionEncoderOutput
[[autodoc]] models.dpr.modeling_dpr.DPRReaderOutput
DPRContextEncoder
[[autodoc]] DPRContextEncoder
    - forward
DPRQuestionEncoder
[[autodoc]] DPRQuestionEncoder
    - forward
DPRReader
[[autodoc]] DPRReader
    - forward
TFDPRContextEncoder
[[autodoc]] TFDPRContextEncoder
    - call
TFDPRQuestionEncoder
[[autodoc]] TFDPRQuestionEncoder
    - call
TFDPRReader
[[autodoc]] TFDPRReader
    - call"