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The current implementation supports the sliding window attention mechanism and memory efficient cache management.
To enable sliding window attention, just make sure to have a `flash-attn` version that is compatible with sliding window attention (`>=2.3.0`).
The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (`self.config.sliding_window`), support batched generation only for `padding_side="left"` and use the absolute position of the current token to compute the positional embedding. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#sliding-window-attention | #sliding-window-attention | .md | 274_7 |
As the Mistral model has 7 billion parameters, that would require about 14GB of GPU RAM in half precision (float16), since each parameter is stored in 2 bytes. However, one can shrink down the size of the model using [quantization](../quantization.md). If the model is quantized to 4 bits (or half a byte per parameter),that requires only about 3.5GB of RAM.
Quantizing a model is as simple as passing a `quantization_config` to the model. Below, we'll leverage the BitsAndyBytes quantization (but refer to [this page](../quantization.md) for other quantization methods):
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
>>> # specify how to quantize the model
>>> quantization_config = BitsAndBytesConfig(
... load_in_4bit=True,
... bnb_4bit_quant_type="nf4",
... bnb_4bit_compute_dtype="torch.float16",
... )
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", quantization_config=True, device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
>>> prompt = "My favourite condiment is"
>>> messages = [
... {"role": "user", "content": "What is your favourite condiment?"},
... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
... {"role": "user", "content": "Do you have mayonnaise recipes?"}
... ]
>>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
>>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"
```
This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArthurZ) .
The original code can be found [here](https://github.com/mistralai/mistral-src). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#shrinking-down-mistral-using-quantization | #shrinking-down-mistral-using-quantization | .md | 274_8 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mistral. 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.
<PipelineTag pipeline="text-generation"/>
- A demo notebook to perform supervised fine-tuning (SFT) of Mistral-7B can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Mistral/Supervised_fine_tuning_(SFT)_of_an_LLM_using_Hugging_Face_tooling.ipynb). 🌎
- A [blog post](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) on how to fine-tune LLMs in 2024 using Hugging Face tooling. 🌎
- The [Alignment Handbook](https://github.com/huggingface/alignment-handbook) by Hugging Face includes scripts and recipes to perform supervised fine-tuning (SFT) and direct preference optimization with Mistral-7B. This includes scripts for full fine-tuning, QLoRa on a single GPU as well as multi-GPU fine-tuning.
- [Causal language modeling task guide](../tasks/language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#resources | #resources | .md | 274_9 |
This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MistralModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
allows sequence of up to 4096*32 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention window size. If not specified, will default to `4096`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import MistralModel, MistralConfig
>>> # Initializing a Mistral 7B style configuration
>>> configuration = MistralConfig()
>>> # Initializing a model from the Mistral 7B style configuration
>>> model = MistralModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | #mistralconfig | .md | 274_10 |
The bare Mistral Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MistralConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
Args:
config: MistralConfig
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralmodel | #mistralmodel | .md | 274_11 |
No docstring available for MistralForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforcausallm | #mistralforcausallm | .md | 274_12 |
The Mistral Model transformer with a sequence classification head on top (linear layer).
[`MistralForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MistralConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforsequenceclassification | #mistralforsequenceclassification | .md | 274_13 |
The Mistral Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MistralConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralfortokenclassification | #mistralfortokenclassification | .md | 274_14 |
The Mistral Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MistralConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforquestionanswering | #mistralforquestionanswering | .md | 274_15 |
No docstring available for FlaxMistralModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#flaxmistralmodel | #flaxmistralmodel | .md | 274_16 |
No docstring available for FlaxMistralForCausalLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#flaxmistralforcausallm | #flaxmistralforcausallm | .md | 274_17 |
No docstring available for TFMistralModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralmodel | #tfmistralmodel | .md | 274_18 |
No docstring available for TFMistralForCausalLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralforcausallm | #tfmistralforcausallm | .md | 274_19 |
No docstring available for TFMistralForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralforsequenceclassification | #tfmistralforsequenceclassification | .md | 274_20 |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/ | .md | 275_0 |
|
The Swin2SR model was proposed in [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
Swin2SR improves the [SwinIR](https://github.com/JingyunLiang/SwinIR/) model by incorporating [Swin Transformer v2](swinv2) 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".*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/swin2sr_architecture.png"
alt="drawing" width="600"/>
<small> Swin2SR architecture. Taken from the <a href="https://arxiv.org/abs/2209.11345">original paper.</a> </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/mv-lab/swin2sr). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | #overview | .md | 275_1 |
Demo notebooks for Swin2SR can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Swin2SR).
A demo Space for image super-resolution with SwinSR can be found [here](https://huggingface.co/spaces/jjourney1125/swin2sr). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#resources | #resources | .md | 275_2 |
Constructs a Swin2SR image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
Methods: preprocess | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srimageprocessor | #swin2srimageprocessor | .md | 275_3 |
This is the configuration class to store the configuration of a [`Swin2SRModel`]. It is used to instantiate a Swin
Transformer v2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Swin Transformer v2
[caidas/swin2sr-classicalsr-x2-64](https://huggingface.co/caidas/swin2sr-classicalsr-x2-64) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, defaults to 64):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 1):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_channels_out (`int`, *optional*, defaults to `num_channels`):
The number of output channels. If not set, it will be set to `num_channels`.
embed_dim (`int`, *optional*, defaults to 180):
Dimensionality of patch embedding.
depths (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`):
Depth of each layer in the Transformer encoder.
num_heads (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`):
Number of attention heads in each layer of the Transformer encoder.
window_size (`int`, *optional*, defaults to 8):
Size of windows.
mlp_ratio (`float`, *optional*, defaults to 2.0):
Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to add absolute position embeddings to the patch embeddings.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
upscale (`int`, *optional*, defaults to 2):
The upscale factor for the image. 2/3/4/8 for image super resolution, 1 for denoising and compress artifact
reduction
img_range (`float`, *optional*, defaults to 1.0):
The range of the values of the input image.
resi_connection (`str`, *optional*, defaults to `"1conv"`):
The convolutional block to use before the residual connection in each stage.
upsampler (`str`, *optional*, defaults to `"pixelshuffle"`):
The reconstruction reconstruction module. Can be 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None.
Example:
```python
>>> from transformers import Swin2SRConfig, Swin2SRModel
>>> # Initializing a Swin2SR caidas/swin2sr-classicalsr-x2-64 style configuration
>>> configuration = Swin2SRConfig()
>>> # Initializing a model (with random weights) from the caidas/swin2sr-classicalsr-x2-64 style configuration
>>> model = Swin2SRModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | #swin2srconfig | .md | 275_4 |
The bare Swin2SR Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`Swin2SRConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srmodel | #swin2srmodel | .md | 275_5 |
Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`Swin2SRConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srforimagesuperresolution | #swin2srforimagesuperresolution | .md | 275_6 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/ | .md | 276_0 |
|
The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) 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](https://huggingface.co/aps). The original code can be found [here](https://github.com/facebookresearch/multimodal/tree/main/examples/flava). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#overview | #overview | .md | 276_1 |
[`FlavaConfig`] is the configuration class to store the configuration of a [`FlavaModel`]. It is used to
instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
and multimodal model configs. Instantiating a configuration with the defaults will yield a similar configuration to
that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`FlavaTextConfig`].
image_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`FlavaImageConfig`].
multimodal_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`FlavaMultimodalConfig`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and image projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The initial value of the *logit_scale* parameter. Default is used as per the original FLAVA/CLIP
implementation.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
ce_ignore_index (`int`, *optional*, defaults to -100):
Cross entropy index to ignore.
mim_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to MIM (Masked Image Modeling) unimodal loss
mlm_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to MLM (Masked Language Modeling) unimodal loss
global_contrastive_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to global contrastive cross-alignment loss.
itm_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to image-text matching multimodal loss.
mmm_image_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to MMM loss's image part.
mmm_text_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to MMM loss's text part.
global_backprop_contrastive (`bool`, *optional*, defaults to `True`):
Whether to use global backpropgation through all workers in contrastive loss.
skip_unmasked_multimodal_encoder (`bool`, *optional*, defaults to `True`):
Whether to skip running unmasked multimodal encoder whose outputs are not used by FLAVA losses.
return_loss (`bool`, *optional*, defaults to `True`):
Whether to return loss or not
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import FlavaConfig, FlavaModel, FlavaForPreTraining
>>> # Initializing a FlavaConfig with style configuration
>>> configuration = FlavaConfig()
>>> # Initializing a FlavaModel and FlavaForPreTraining model (with random weights) from the style configuration
>>> model = FlavaModel(configuration)
>>> model_pre = FlavaForPreTraining(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> configuration_pre = model_pre.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaconfig | #flavaconfig | .md | 276_2 |
This is the configuration class to store the configuration of a [`FlavaTextModel`]. It is used to instantiate an
FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
[facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`FlavaTextModel`].
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`FlavaTextModel`]. Note that even though
text encoder allows `token_type_ids`'s value as 2, for text-only pretraining and fine-tuning, only 1 is
used similar to RoBERTa.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048). For VL, max_length passed to model is 77.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
Example:
```python
>>> from transformers import FlavaTextConfig, FlavaTextModel
>>> # Initializing a FlavaTextModel with style configuration
>>> configuration = FlavaTextConfig()
>>> # Initializing a FlavaTextModel model (with random weights) from the style configuration
>>> model = FlavaTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavatextconfig | #flavatextconfig | .md | 276_3 |
This is the configuration class to store the configuration of a [`FlavaImageModel`]. It is used to instantiate an
FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
[facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
mask_token (`bool`, *optional*, defaults to `True`):
Whether to use a mask token or not. Used in MIM (Masked Image Modeling) loss for FLAVA.
vocab_size (`int`, *optional*, defaults to 8192):
Vocabulary size of the [`FlavaImageCodebook`] used in conjunction with [`FlavaImageModel`] for MIM (Masked
Image Modeling) loss for FLAVA.
Example:
```python
>>> from transformers import FlavaImageConfig, FlavaImageModel
>>> # Initializing a FlavaImageModel with style configuration
>>> configuration = FlavaImageConfig()
>>> # Initializing a FlavaImageModel model (with random weights) from the style configuration
>>> model = FlavaImageModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimageconfig | #flavaimageconfig | .md | 276_4 |
This is the configuration class to store the configuration of a [`FlavaMultimodalModel`]. It is used to instantiate
an FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
[facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
use_cls_token (`bool`, *optional*, defaults to `True`):
Whether to use an extra CLS token for multimodal settings. Usually needed by the FLAVA model.
Example:
```python
>>> from transformers import FlavaMultimodalConfig, FlavaMultimodalModel
>>> # Initializing a FlavaMultimodalModel with style configuration
>>> configuration = FlavaMultimodalConfig()
>>> # Initializing a FlavaMultimodalModel model (with random weights) from the style configuration
>>> model = FlavaMultimodalModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavamultimodalconfig | #flavamultimodalconfig | .md | 276_5 |
No docstring available for FlavaImageCodebookConfig | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimagecodebookconfig | #flavaimagecodebookconfig | .md | 276_6 |
Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.
[`FlavaProcessor`] offers all the functionalities of [`FlavaImageProcessor`] and [`BertTokenizerFast`]. See the
[`~FlavaProcessor.__call__`] and [`~FlavaProcessor.decode`] for more information.
Args:
image_processor ([`FlavaImageProcessor`], *optional*): The image processor is a required input.
tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaprocessor | #flavaprocessor | .md | 276_7 |
No docstring available for FlavaFeatureExtractor | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavafeatureextractor | #flavafeatureextractor | .md | 276_8 |
Constructs a Flava image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
Size of the image after resizing. Can be overridden by the `size` parameter in `preprocess`.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in
`preprocess`.
do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to center crop the images. Can be overridden by the `do_center_crop` parameter in `preprocess`.
crop_size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
Size of image after the center crop `(crop_size["height"], crop_size["width"])`. Can be overridden by the
`crop_size` parameter in `preprocess`.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in `preprocess`.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in
`preprocess`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in `preprocess`.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
return_image_mask (`bool`, *optional*, defaults to `False`):
Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`.
input_size_patches (`int`, *optional*, defaults to 14):
Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden
by the `input_size_patches` parameter in `preprocess`.
total_mask_patches (`int`, *optional*, defaults to 75):
Total number of patches that should be masked. Can be overridden by the `total_mask_patches` parameter in
`preprocess`.
mask_group_min_patches (`int`, *optional*, defaults to 16):
Minimum number of patches that should be masked. Can be overridden by the `mask_group_min_patches`
parameter in `preprocess`.
mask_group_max_patches (`int`, *optional*):
Maximum number of patches that should be masked. Can be overridden by the `mask_group_max_patches`
parameter in `preprocess`.
mask_group_min_aspect_ratio (`float`, *optional*, defaults to 0.3):
Minimum aspect ratio of the mask window. Can be overridden by the `mask_group_min_aspect_ratio` parameter
in `preprocess`.
mask_group_max_aspect_ratio (`float`, *optional*):
Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter
in `preprocess`.
codebook_do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the input for codebook to a certain. Can be overridden by the `codebook_do_resize`
parameter in `preprocess`. `codebook_size`.
codebook_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
Resize the input for codebook to the given size. Can be overridden by the `codebook_size` parameter in
`preprocess`.
codebook_resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`):
Resampling filter to use if resizing the codebook image. Can be overridden by the `codebook_resample`
parameter in `preprocess`.
codebook_do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to crop the input for codebook at the center. If the input size is smaller than
`codebook_crop_size` along any edge, the image is padded with 0's and then center cropped. Can be
overridden by the `codebook_do_center_crop` parameter in `preprocess`.
codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
Desired output size for codebook input when applying center-cropping. Can be overridden by the
`codebook_crop_size` parameter in `preprocess`.
codebook_do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the input for codebook by the specified scale `codebook_rescale_factor`. Can be
overridden by the `codebook_do_rescale` parameter in `preprocess`.
codebook_rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Defines the scale factor to use if rescaling the codebook image. Can be overridden by the
`codebook_rescale_factor` parameter in `preprocess`.
codebook_do_map_pixels (`bool`, *optional*, defaults to `True`):
Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the
`codebook_do_map_pixels` parameter in `preprocess`.
codebook_do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the input for codebook with `codebook_image_mean` and `codebook_image_std`. Can
be overridden by the `codebook_do_normalize` parameter in `preprocess`.
codebook_image_mean (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0, 0, 0]`):
The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden
by the `codebook_image_mean` parameter in `preprocess`.
codebook_image_std (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can
be overridden by the `codebook_image_std` parameter in `preprocess`.
Methods: preprocess | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimageprocessor | #flavaimageprocessor | .md | 276_9 |
The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`FlavaConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Parameters:
image_codebook ([`nn.Module`]): If passed, the image codebook will be set to this. Otherwise. it will
be initialized using the image_codebook_config defined in the config first as the first parameter.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaforpretraining | #flavaforpretraining | .md | 276_10 |
The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`FlavaConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
- get_text_features
- get_image_features | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavamodel | #flavamodel | .md | 276_11 |
The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used
to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use
`get_codebook_indices` to get image tokens for an image.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`FlavaImageCodebookConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
- get_codebook_indices
- get_codebook_probs | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimagecodebook | #flavaimagecodebook | .md | 276_12 |
The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`FlavaTextConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavatextmodel | #flavatextmodel | .md | 276_13 |
The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`FlavaImageConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimagemodel | #flavaimagemodel | .md | 276_14 |
The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`FlavaMultimodalConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavamultimodalmodel | #flavamultimodalmodel | .md | 276_15 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/ | .md | 277_0 |
|
DiNAT was proposed in [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001)
by Ali Hassani and Humphrey Shi.
It extends [NAT](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). *
<img
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dilated-neighborhood-attention-pattern.jpg"
alt="drawing" width="600"/>
<small> Neighborhood Attention with different dilation values.
Taken from the <a href="https://arxiv.org/abs/2209.15001">original paper</a>.</small>
This model was contributed by [Ali Hassani](https://huggingface.co/alihassanijr).
The original code can be found [here](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#overview | #overview | .md | 277_1 |
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](https://github.com/SHI-Labs/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](https://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. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#usage-tips | #usage-tips | .md | 277_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DiNAT.
<PipelineTag pipeline="image-classification"/>
- [`DinatForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_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. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#resources | #resources | .md | 277_3 |
This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Dinat
[shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embed_dim (`int`, *optional*, defaults to 64):
Dimensionality of patch embedding.
depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 5]`):
Number of layers in each level of the encoder.
num_heads (`List[int]`, *optional*, defaults to `[2, 4, 8, 16]`):
Number of attention heads in each layer of the Transformer encoder.
kernel_size (`int`, *optional*, defaults to 7):
Neighborhood Attention kernel size.
dilations (`List[List[int]]`, *optional*, defaults to `[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]`):
Dilation value of each NA layer in the Transformer encoder.
mlp_ratio (`float`, *optional*, defaults to 3.0):
Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
layer_scale_init_value (`float`, *optional*, defaults to 0.0):
The initial value for the layer scale. Disabled if <=0.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
Example:
```python
>>> from transformers import DinatConfig, DinatModel
>>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration
>>> configuration = DinatConfig()
>>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration
>>> model = DinatModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#dinatconfig | #dinatconfig | .md | 277_4 |
The bare Dinat Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`DinatConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#dinatmodel | #dinatmodel | .md | 277_5 |
Dinat Model transformer with an image classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`DinatConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#dinatforimageclassification | #dinatforimageclassification | .md | 277_6 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/ | .md | 278_0 |
|
The Wav2Vec2-Conformer was added to an updated version of [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) 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](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md#pre-trained-models).
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec).
Note: Meta (FAIR) released a new version of [Wav2Vec2-BERT 2.0](https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert) - it's pretrained on 4.5M hours of audio. We especially recommend using it for fine-tuning tasks, e.g. as per [this guide](https://huggingface.co/blog/fine-tune-w2v2-bert). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#overview | #overview | .md | 278_1 |
- 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](https://arxiv.org/abs/2005.08100).
- 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`. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#usage-tips | #usage-tips | .md | 278_2 |
- [Audio classification task guide](../tasks/audio_classification)
- [Automatic speech recognition task guide](../tasks/asr) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#resources | #resources | .md | 278_3 |
This is the configuration class to store the configuration of a [`Wav2Vec2ConformerModel`]. It is used to
instantiate an Wav2Vec2Conformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2Conformer
[facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large)
architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*):
Vocabulary size of the Wav2Vec2Conformer model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`Wav2Vec2ConformerModel`]. Vocabulary size of the
model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward
method of [`Wav2Vec2ConformerModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for activations inside the fully connected layer.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`Wav2Vec2ConformerForCTC`].
layerdrop (`float`, *optional*, defaults to 0.1):
The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
details.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for quantized feature encoder states.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
num_codevectors_per_group (`int`, *optional*, defaults to 320):
Number of entries in each quantization codebook (group).
num_codevector_groups (`int`, *optional*, defaults to 2):
Number of codevector groups for product codevector quantization.
contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
The temperature *kappa* in the contrastive loss.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for the output of the feature encoder that's used by the quantizer.
num_negatives (`int`, *optional*, defaults to 100):
Number of negative samples for the contrastive loss.
codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the quantized feature vectors.
proj_codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the final projection of both the quantized and the transformer features.
diversity_loss_weight (`int`, *optional*, defaults to 0.1):
The weight of the codebook diversity loss component.
ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`Wav2Vec2ConformerForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`Wav2Vec2ConformerForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`Wav2Vec2ConformerForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
*XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
xvector_output_dim (`int`, *optional*, defaults to 512):
Dimensionality of the *XVector* embedding vectors.
add_adapter (`bool`, *optional*, defaults to `False`):
Whether a convolutional network should be stacked on top of the Wav2Vec2Conformer Encoder. Can be very
useful for warm-starting Wav2Vec2Conformer for SpeechEncoderDecoder models.
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
num_adapter_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
position_embeddings_type (`str`, *optional*, defaults to `"relative"`):
Can be specified to `relative` or `rotary` for relative or rotary position embeddings respectively. If left
`None` no relative position embedding is applied.
rotary_embedding_base (`int`, *optional*, defaults to 10000):
If `"rotary"` position embeddings are used, defines the size of the embedding base.
max_source_positions (`int`, *optional*, defaults to 5000):
if `"relative"` position embeddings are used, defines the maximum source input positions.
conv_depthwise_kernel_size (`int`, *optional*, defaults to 31):
Kernel size of convolutional depthwise 1D layer in Conformer blocks.
conformer_conv_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all convolutional layers in Conformer blocks.
Example:
```python
>>> from transformers import Wav2Vec2ConformerConfig, Wav2Vec2ConformerModel
>>> # Initializing a Wav2Vec2Conformer facebook/wav2vec2-conformer-rel-pos-large style configuration
>>> configuration = Wav2Vec2ConformerConfig()
>>> # Initializing a model (with random weights) from the facebook/wav2vec2-conformer-rel-pos-large style configuration
>>> model = Wav2Vec2ConformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerconfig | #wav2vec2conformerconfig | .md | 278_4 |
models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput
Output type of [`Wav2Vec2ConformerForPreTraining`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
projected_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
projected quantized states.
projected_quantized_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
target vectors for contrastive loss.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
contrastive_loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
The contrastive loss (L_m) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .
diversity_loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
The diversity loss (L_d) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) . | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformer-specific-outputs | #wav2vec2conformer-specific-outputs | .md | 278_5 |
The bare Wav2Vec2Conformer Model transformer outputting raw hidden-states without any specific head on top.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#nn.Module) sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
Parameters:
config ([`Wav2Vec2ConformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformermodel | #wav2vec2conformermodel | .md | 278_6 |
Wav2Vec2Conformer Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#nn.Module) sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
Parameters:
config ([`Wav2Vec2ConformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforctc | #wav2vec2conformerforctc | .md | 278_7 |
Wav2Vec2Conformer Model with a sequence classification head on top (a linear layer over the pooled output) for
tasks like SUPERB Keyword Spotting.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#nn.Module) sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
Parameters:
config ([`Wav2Vec2ConformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforsequenceclassification | #wav2vec2conformerforsequenceclassification | .md | 278_8 |
Wav2Vec2Conformer Model with a frame classification head on top for tasks like Speaker Diarization.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#nn.Module) sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
Parameters:
config ([`Wav2Vec2ConformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforaudioframeclassification | #wav2vec2conformerforaudioframeclassification | .md | 278_9 |
Wav2Vec2Conformer Model with an XVector feature extraction head on top for tasks like Speaker Verification.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#nn.Module) sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
Parameters:
config ([`Wav2Vec2ConformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforxvector | #wav2vec2conformerforxvector | .md | 278_10 |
Wav2Vec2Conformer Model with a quantizer and `VQ` head on top.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#nn.Module) sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
Parameters:
config ([`Wav2Vec2ConformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforpretraining | #wav2vec2conformerforpretraining | .md | 278_11 |
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|
The MarkupLM model was proposed in [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document
Understanding](https://arxiv.org/abs/2110.08518) 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](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](https://x-lance.github.io/WebSRC/), a dataset for Web-Based Structural Reading Comprehension (a bit like SQuAD but for web pages)
- [SWDE](https://www.researchgate.net/publication/221299838_From_one_tree_to_a_forest_a_unified_solution_for_structured_web_data_extraction), a dataset
for information extraction from web pages (basically named-entity recognition 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.*
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/markuplm). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#overview | #overview | .md | 279_1 |
- 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](#usage-markuplmprocessor) for more info.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/markuplm_architecture.jpg"
alt="drawing" width="600"/>
<small> MarkupLM architecture. Taken from the <a href="https://arxiv.org/abs/2110.08518">original paper.</a> </small> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#usage-tips | #usage-tips | .md | 279_2 |
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.
```python
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](https://www.crummy.com/software/BeautifulSoup/bs4/doc/), 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.
```python
>>> from transformers import MarkupLMProcessor
>>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
>>> html_string = """
... <!DOCTYPE html>
... <html>
... <head>
... <title>Hello world</title>
... </head>
... <body>
... <h1>Welcome</h1>
... <p>Here is my website.</p>
... </body>
... </html>"""
>>> # 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`.
```python
>>> 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](https://paperswithcode.com/dataset/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`.
```python
>>> 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].
```python
>>> from transformers import MarkupLMProcessor
>>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
>>> html_string = """
... <!DOCTYPE html>
... <html>
... <head>
... <title>Hello world</title>
... </head>
... <body>
... <h1>Welcome</h1>
... <p>My name is Niels.</p>
... </body>
... </html>"""
>>> 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`.
```python
>>> 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'])
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#usage-markuplmprocessor | #usage-markuplmprocessor | .md | 279_3 |
- [Demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM)
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#resources | #resources | .md | 279_4 |
This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
MarkupLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the MarkupLM
[microsoft/markuplm-base](https://huggingface.co/microsoft/markuplm-base) architecture.
Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
documentation from [`BertConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`MarkupLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed into [`MarkupLMModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
max_tree_id_unit_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the tree id unit embedding might ever use. Typically set this to something large
just in case (e.g., 1024).
max_xpath_tag_unit_embeddings (`int`, *optional*, defaults to 256):
The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large
just in case (e.g., 256).
max_xpath_subs_unit_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something
large just in case (e.g., 1024).
tag_pad_id (`int`, *optional*, defaults to 216):
The id of the padding token in the xpath tags.
subs_pad_id (`int`, *optional*, defaults to 1001):
The id of the padding token in the xpath subscripts.
xpath_tag_unit_hidden_size (`int`, *optional*, defaults to 32):
The hidden size of each tree id unit. One complete tree index will have
(50*xpath_tag_unit_hidden_size)-dim.
max_depth (`int`, *optional*, defaults to 50):
The maximum depth in xpath.
Examples:
```python
>>> from transformers import MarkupLMModel, MarkupLMConfig
>>> # Initializing a MarkupLM microsoft/markuplm-base style configuration
>>> configuration = MarkupLMConfig()
>>> # Initializing a model from the microsoft/markuplm-base style configuration
>>> model = MarkupLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
Methods: all | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmconfig | #markuplmconfig | .md | 279_5 |
Constructs a MarkupLM feature extractor. This can be used to get a list of nodes and corresponding xpaths from HTML
strings.
This feature extractor inherits from [`~feature_extraction_utils.PreTrainedFeatureExtractor`] which contains most
of the main methods. Users should refer to this superclass for more information regarding those methods.
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmfeatureextractor | #markuplmfeatureextractor | .md | 279_6 |
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to
turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and
`xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
Methods: build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmtokenizer | #markuplmtokenizer | .md | 279_7 |
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
[`MarkupLMTokenizerFast`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`,
`token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which
contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
Methods: all | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmtokenizerfast | #markuplmtokenizerfast | .md | 279_8 |
Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single
processor.
[`MarkupLMProcessor`] offers all the functionalities you need to prepare data for the model.
It first uses [`MarkupLMFeatureExtractor`] to extract nodes and corresponding xpaths from one or more HTML strings.
Next, these are provided to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`], which turns them into token-level
`input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_subs_seq`.
Args:
feature_extractor (`MarkupLMFeatureExtractor`):
An instance of [`MarkupLMFeatureExtractor`]. The feature extractor is a required input.
tokenizer (`MarkupLMTokenizer` or `MarkupLMTokenizerFast`):
An instance of [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]. The tokenizer is a required input.
parse_html (`bool`, *optional*, defaults to `True`):
Whether or not to use `MarkupLMFeatureExtractor` to parse HTML strings into nodes and corresponding xpaths.
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmprocessor | #markuplmprocessor | .md | 279_9 |
The bare MarkupLM Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MarkupLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmmodel | #markuplmmodel | .md | 279_10 |
MarkupLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MarkupLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmforsequenceclassification | #markuplmforsequenceclassification | .md | 279_11 |
MarkupLM Model with a `token_classification` head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MarkupLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmfortokenclassification | #markuplmfortokenclassification | .md | 279_12 |
MarkupLM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MarkupLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmforquestionanswering | #markuplmforquestionanswering | .md | 279_13 |
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|
The BEiT model was proposed in [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) 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](https://arxiv.org/abs/2010.11929)), BEiT models are pre-trained to
predict visual tokens from the codebook of OpenAI's [DALL-E model](https://arxiv.org/abs/2102.12092) 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%).*
This model was contributed by [nielsr](https://huggingface.co/nielsr). The JAX/FLAX version of this model was
contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/beit). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#overview | #overview | .md | 280_1 |
- BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They
outperform both the [original model (ViT)](vit) as well as [Data-efficient Image Transformers (DeiT)](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](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer) (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](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/BEiT).
- 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](https://huggingface.co/models?search=microsoft/beit).
- The available checkpoints are either (1) pre-trained on [ImageNet-22k](http://www.image-net.org/) (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](http://www.image-net.org/challenges/LSVRC/2012/) (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.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/beit_architecture.jpg"
alt="drawing" width="600"/>
<small> BEiT pre-training. Taken from the <a href="https://arxiv.org/abs/2106.08254">original paper.</a> </small> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#usage-tips | #usage-tips | .md | 280_2 |
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
page for more information.
SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set
`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
```
from transformers import BeitForImageClassification
model = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224", attn_implementation="sdpa", torch_dtype=torch.float16)
...
```
For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
On a local benchmark (NVIDIA GeForce RTX 2060-8GB, PyTorch 2.5.1, OS Ubuntu 20.04) with `float16` and
`microsoft/beit-base-patch16-224` model, we saw the following improvements during training and inference: | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 280_3 |
| num_training_steps | batch_size | image_size | is_cuda | Time per batch (eager - s) | Time per batch (sdpa - s) | Speedup (%) | Eager peak mem (MB) | SDPA peak mem (MB) | Mem saving (%) |
|--------------------|------------|--------------|---------|----------------------------|---------------------------|-------------|----------------------|--------------------|----------------|
| 50 | 2 | (1048, 640) | True | 0.984 | 0.746 | 31.975 | 6738.915 | 4319.886 | 55.998 | | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#training | #training | .md | 280_4 |
| Image batch size | Eager (s/iter) | Eager CI, % | Eager memory (MB) | SDPA (s/iter) | SDPA CI, % | SDPA memory (MB) | SDPA speedup | SDPA memory saved (%) |
|-------------------:|-----------------:|:--------------|--------------------:|----------------:|:-------------|-------------------:|---------------:|----------------------:|
| 1 | 0.012 | ±0.3% | 3.76657e+08 | 0.011 | ±0.5% | 3.75739e+08 | 1.05 | 0.244 |
| 4 | 0.013 | ±0.1% | 4.03147e+08 | 0.011 | ±0.2% | 3.90554e+08 | 1.178 | 3.225 |
| 16 | 0.045 | ±0.1% | 4.96697e+08 | 0.035 | ±0.1% | 4.51232e+08 | 1.304 | 10.076 |
| 32 | 0.088 | ±0.1% | 6.24417e+08 | 0.066 | ±0.1% | 5.33488e+08 | 1.325 | 17.044 | | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#inference | #inference | .md | 280_5 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BEiT.
<PipelineTag pipeline="image-classification"/>
- [`BeitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)
**Semantic segmentation**
- [Semantic segmentation task guide](../tasks/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. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#resources | #resources | .md | 280_6 |
models.beit.modeling_beit.BeitModelOutputWithPooling
Class for outputs of [`BeitModel`].
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
*config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
will be returned.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
[[autodoc]] models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling:
modeling_flax_beit requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
Please note that you may need to restart your runtime after installation. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beit-specific-outputs | #beit-specific-outputs | .md | 280_7 |
This is the configuration class to store the configuration of a [`BeitModel`]. It is used to instantiate an BEiT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the BEiT
[microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) architecture.
Args:
vocab_size (`int`, *optional*, defaults to 8192):
Vocabulary size of the BEiT model. Defines the number of different image tokens that can be used during
pre-training.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
use_mask_token (`bool`, *optional*, defaults to `False`):
Whether to use a mask token for masked image modeling.
use_absolute_position_embeddings (`bool`, *optional*, defaults to `False`):
Whether to use BERT-style absolute position embeddings.
use_relative_position_bias (`bool`, *optional*, defaults to `False`):
Whether to use T5-style relative position embeddings in the self-attention layers.
use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`):
Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
layer_scale_init_value (`float`, *optional*, defaults to 0.1):
Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate per sample (when applied in the main path of residual layers).
use_mean_pooling (`bool`, *optional*, defaults to `True`):
Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
CLS token, before applying the classification head.
pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
Pooling scales used in Pooling Pyramid Module applied on the last feature map.
use_auxiliary_head (`bool`, *optional*, defaults to `True`):
Whether to use an auxiliary head during training.
auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
Weight of the cross-entropy loss of the auxiliary head.
auxiliary_channels (`int`, *optional*, defaults to 256):
Number of channels to use in the auxiliary head.
auxiliary_num_convs (`int`, *optional*, defaults to 1):
Number of convolutional layers to use in the auxiliary head.
auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
Whether to concatenate the output of the auxiliary head with the input before the classification layer.
semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
The index that is ignored by the loss function of the semantic segmentation model.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
add_fpn (`bool`, *optional*, defaults to `False`):
Whether to add a FPN as part of the backbone. Only relevant for [`BeitBackbone`].
reshape_hidden_states (`bool`, *optional*, defaults to `True`):
Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in
case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size,
seq_len, hidden_size)`. Only relevant for [`BeitBackbone`].
Example:
```python
>>> from transformers import BeitConfig, BeitModel
>>> # Initializing a BEiT beit-base-patch16-224-pt22k style configuration
>>> configuration = BeitConfig()
>>> # Initializing a model (with random weights) from the beit-base-patch16-224-pt22k style configuration
>>> model = BeitModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitconfig | #beitconfig | .md | 280_8 |
No docstring available for BeitFeatureExtractor
Methods: __call__
- post_process_semantic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitfeatureextractor | #beitfeatureextractor | .md | 280_9 |
Constructs a BEiT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in the
`preprocess` method.
crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
Can be overridden by the `crop_size` parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
The mean to use if normalizing the image. This is a float or list of floats of length of the number of
channels of the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
The standard deviation to use if normalizing the image. This is a float or list of floats of length of the
number of channels of the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
do_reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the
`preprocess` method.
Methods: preprocess
- post_process_semantic_segmentation
<frameworkcontent>
<pt> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitimageprocessor | #beitimageprocessor | .md | 280_10 |
The bare Beit Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`BeitConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitmodel | #beitmodel | .md | 280_11 |
Beit Model transformer with a 'language' modeling head on top. BEiT does masked image modeling by predicting
visual tokens of a Vector-Quantize Variational Autoencoder (VQ-VAE), whereas other vision models like ViT and DeiT
predict RGB pixel values. As a result, this class is incompatible with [`AutoModelForMaskedImageModeling`], so you
will need to use [`BeitForMaskedImageModeling`] directly if you wish to do masked image modeling with BEiT.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`BeitConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitformaskedimagemodeling | #beitformaskedimagemodeling | .md | 280_12 |
Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
hidden states of the patch tokens) e.g. for ImageNet.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`BeitConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitforimageclassification | #beitforimageclassification | .md | 280_13 |
Beit Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`BeitConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitforsemanticsegmentation | #beitforsemanticsegmentation | .md | 280_14 |
No docstring available for FlaxBeitModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#flaxbeitmodel | #flaxbeitmodel | .md | 280_15 |
No docstring available for FlaxBeitForMaskedImageModeling
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#flaxbeitformaskedimagemodeling | #flaxbeitformaskedimagemodeling | .md | 280_16 |
No docstring available for FlaxBeitForImageClassification
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#flaxbeitforimageclassification | #flaxbeitforimageclassification | .md | 280_17 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/ | .md | 281_0 |
|
The RoCBert model was proposed in [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) 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](https://huggingface.co/weiweishi). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#overview | #overview | .md | 281_1 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#resources | #resources | .md | 281_2 |
This is the configuration class to store the configuration of a [`RoCBertModel`]. It is used to instantiate a
RoCBert model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the RoCBert
[weiweishi/roc-bert-base-zh](https://huggingface.co/weiweishi/roc-bert-base-zh) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the RoCBert model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`RoCBertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`RoCBertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
enable_pronunciation (`bool`, *optional*, defaults to `True`):
Whether or not the model use pronunciation embed when training.
enable_shape (`bool`, *optional*, defaults to `True`):
Whether or not the model use shape embed when training.
pronunciation_embed_dim (`int`, *optional*, defaults to 768):
Dimension of the pronunciation_embed.
pronunciation_vocab_size (`int`, *optional*, defaults to 910):
Pronunciation Vocabulary size of the RoCBert model. Defines the number of different tokens that can be
represented by the `input_pronunciation_ids` passed when calling [`RoCBertModel`].
shape_embed_dim (`int`, *optional*, defaults to 512):
Dimension of the shape_embed.
shape_vocab_size (`int`, *optional*, defaults to 24858):
Shape Vocabulary size of the RoCBert model. Defines the number of different tokens that can be represented
by the `input_shape_ids` passed when calling [`RoCBertModel`].
concat_input (`bool`, *optional*, defaults to `True`):
Defines the way of merging the shape_embed, pronunciation_embed and word_embed, if the value is true,
output_embed = torch.cat((word_embed, shape_embed, pronunciation_embed), -1), else output_embed =
(word_embed + shape_embed + pronunciation_embed) / 3
Example:
```python
>>> from transformers import RoCBertModel, RoCBertConfig
>>> # Initializing a RoCBert weiweishi/roc-bert-base-zh style configuration
>>> configuration = RoCBertConfig()
>>> # Initializing a model from the weiweishi/roc-bert-base-zh style configuration
>>> model = RoCBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
Methods: all | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertconfig | #rocbertconfig | .md | 281_3 |
Args:
Construct a RoCBert tokenizer. Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizer`] which
contains most of the main methods. Users should refer to this superclass for more information regarding those
methods.
vocab_file (`str`):
File containing the vocabulary.
word_shape_file (`str`):
File containing the word => shape info.
word_pronunciation_file (`str`):
File containing the word => pronunciation info.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic tokenization before WordPiece.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
Methods: build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocberttokenizer | #rocberttokenizer | .md | 281_4 |
The bare RoCBert Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoCBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to be initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertmodel | #rocbertmodel | .md | 281_5 |
RoCBert Model with contrastive loss and masked_lm_loss during the pretraining.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoCBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertforpretraining | #rocbertforpretraining | .md | 281_6 |
RoCBert Model with a `language modeling` head on top for CLM fine-tuning.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoCBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertforcausallm | #rocbertforcausallm | .md | 281_7 |
RoCBert Model with a `language modeling` head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoCBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertformaskedlm | #rocbertformaskedlm | .md | 281_8 |
RoCBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoCBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertforsequenceclassification | #rocbertforsequenceclassification | .md | 281_9 |
RoCBert Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoCBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertformultiplechoice | #rocbertformultiplechoice | .md | 281_10 |
RoCBert Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoCBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertfortokenclassification | #rocbertfortokenclassification | .md | 281_11 |
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