Quick tour

Get up and running with 🤗 Transformers! Start using the pipeline() for rapid inference, and quickly load a pretrained model and tokenizer with an AutoClass to solve your text, vision or audio task.

Pipeline

pipeline() is the easiest way to use a pretrained model for a given task.

The pipeline() supports many common tasks out-of-the-box:

Text:

• Sentiment analysis: classify the polarity of a given text.
• Text generation (in English): generate text from a given input.
• Name entity recognition (NER): label each word with the entity it represents (person, date, location, etc.).
• Question answering: extract the answer from the context, given some context and a question.
• Fill-mask: fill in the blank given a text with masked words.
• Summarization: generate a summary of a long sequence of text or document.
• Translation: translate text into another language.
• Feature extraction: create a tensor representation of the text.

Image:

• Image classification: classify an image.
• Image segmentation: classify every pixel in an image.
• Object detection: detect objects within an image.

Audio:

• Audio classification: assign a label to a given segment of audio.
• Automatic speech recognition (ASR): transcribe audio data into text.

Pipeline usage

In the following example, you will use the pipeline() for sentiment analysis.

Install the following dependencies if you haven’t already:

pip install torch

pip install tensorflow

Import pipeline() and specify the task you want to complete:

>>> from transformers import pipeline

>>> classifier = pipeline("sentiment-analysis")

The pipeline downloads and caches a default pretrained model and tokenizer for sentiment analysis. Now you can use the classifier on your target text:

>>> classifier("We are very happy to show you the 🤗 Transformers library.")
[{'label': 'POSITIVE', 'score': 0.9998}]

For more than one sentence, pass a list of sentences to the pipeline() which returns a list of dictionaries:

>>> results = classifier(["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."])
>>> for result in results:
...     print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
label: POSITIVE, with score: 0.9998
label: NEGATIVE, with score: 0.5309

The pipeline() can also iterate over an entire dataset. Start by installing the 🤗 Datasets library:

pip install datasets 

Create a pipeline() with the task you want to solve for and the model you want to use.

>>> import torch
>>> from transformers import pipeline

>>> speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")

Next, load a dataset (see the 🤗 Datasets Quick Start for more details) you’d like to iterate over. For example, let’s load the MInDS-14 dataset:

>>> from datasets import load_dataset, Audio

>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")

We need to make sure that the sampling rate of the dataset matches the sampling rate facebook/wav2vec2-base-960h was trained on.

>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))

Audio files are automatically loaded and resampled when calling the "audio" column. Let’s extract the raw waveform arrays of the first 4 samples and pass it as a list to the pipeline:

>>> result = speech_recognizer(dataset[:4]["audio"])
>>> print([d["text"] for d in result])
['I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT', "FONDERING HOW I'D SET UP A JOIN TO HET WITH MY WIFE AND WHERE THE AP MIGHT BE", "I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE APSO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AND I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS", 'HOW DO I TURN A JOIN A COUNT']

For a larger dataset where the inputs are big (like in speech or vision), you will want to pass along a generator instead of a list that loads all the inputs in memory. See the pipeline documentation for more information.

Use another model and tokenizer in the pipeline

The pipeline() can accommodate any model from the Model Hub, making it easy to adapt the pipeline() for other use-cases. For example, if you’d like a model capable of handling French text, use the tags on the Model Hub to filter for an appropriate model. The top filtered result returns a multilingual BERT model fine-tuned for sentiment analysis. Great, let’s use this model!

>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"

>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification

>>> model = AutoModelForSequenceClassification.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)

>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

>>> model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)

Then you can specify the model and tokenizer in the pipeline(), and apply the classifier on your target text:

>>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
>>> classifier("Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.")
[{'label': '5 stars', 'score': 0.7273}]

If you can’t find a model for your use-case, you will need to fine-tune a pretrained model on your data. Take a look at our fine-tuning tutorial to learn how. Finally, after you’ve fine-tuned your pretrained model, please consider sharing it (see tutorial here) with the community on the Model Hub to democratize NLP for everyone! 🤗

AutoClass

Under the hood, the AutoModelForSequenceClassification and AutoTokenizer classes work together to power the pipeline(). An AutoClass is a shortcut that automatically retrieves the architecture of a pretrained model from it’s name or path. You only need to select the appropriate AutoClass for your task and it’s associated tokenizer with AutoTokenizer.

Let’s return to our example and see how you can use the AutoClass to replicate the results of the pipeline().

AutoTokenizer

A tokenizer is responsible for preprocessing text into a format that is understandable to the model. First, the tokenizer will split the text into words called tokens. There are multiple rules that govern the tokenization process, including how to split a word and at what level (learn more about tokenization here). The most important thing to remember though is you need to instantiate the tokenizer with the same model name to ensure you’re using the same tokenization rules a model was pretrained with.

Load a tokenizer with AutoTokenizer:

>>> from transformers import AutoTokenizer

>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)

Next, the tokenizer converts the tokens into numbers in order to construct a tensor as input to the model. This is known as the model’s vocabulary.

Pass your text to the tokenizer:

>>> encoding = tokenizer("We are very happy to show you the 🤗 Transformers library.")
>>> print(encoding)
{'input_ids': [101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103, 100, 58263, 13299, 119, 102],
 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

The tokenizer will return a dictionary containing:

• input_ids: numerical representions of your tokens.
• atttention_mask: indicates which tokens should be attended to.

Just like the pipeline(), the tokenizer will accept a list of inputs. In addition, the tokenizer can also pad and truncate the text to return a batch with uniform length:

>>> pt_batch = tokenizer(
...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
...     padding=True,
...     truncation=True,
...     max_length=512,
...     return_tensors="pt",
... )

>>> tf_batch = tokenizer(
...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
...     padding=True,
...     truncation=True,
...     max_length=512,
...     return_tensors="tf",
... )

Read the preprocessing tutorial for more details about tokenization.

AutoModel

>>> from transformers import AutoModelForSequenceClassification

>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)

>>> pt_outputs = pt_model(**pt_batch)

>>> from torch import nn

>>> pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
>>> print(pt_predictions)
tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
        [0.2084, 0.1826, 0.1969, 0.1755, 0.2365]], grad_fn=<SoftmaxBackward0>)

>>> from transformers import TFAutoModelForSequenceClassification

>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)

>>> tf_outputs = tf_model(tf_batch)

>>> import tensorflow as tf

>>> tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
>>> tf_predictions

Models are a standard torch.nn.Module or a tf.keras.Model so you can use them in your usual training loop. However, to make things easier, 🤗 Transformers provides a Trainer class for PyTorch that adds functionality for distributed training, mixed precision, and more. For TensorFlow, you can use the fit method from Keras. Refer to the training tutorial for more details.

Save a model

>>> pt_save_directory = "./pt_save_pretrained"
>>> tokenizer.save_pretrained(pt_save_directory)
>>> pt_model.save_pretrained(pt_save_directory)

>>> pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained")

>>> tf_save_directory = "./tf_save_pretrained"
>>> tokenizer.save_pretrained(tf_save_directory)
>>> tf_model.save_pretrained(tf_save_directory)

>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained("./tf_save_pretrained")

One particularly cool 🤗 Transformers feature is the ability to save a model and reload it as either a PyTorch or TensorFlow model. The from_pt or from_tf parameter can convert the model from one framework to the other:

>>> from transformers import AutoModel

>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)

>>> from transformers import TFAutoModel

>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)

Custom model builds

You can modify the model’s configuration class to change how a model is built. The configuration specifies a model’s attributes, such as the number of hidden layers or attention heads. You start from scratch when you initialize a model from a custom configuration class. The model attributes are randomly initialized, and you’ll need to train the model before you can use it to get meaningful results.

Start by importing AutoConfig, and then load the pretrained model you want to modify. Within AutoConfig.from_pretrained(), you can specify the attribute you want to change, such as the number of attention heads:

>>> from transformers import AutoConfig

>>> my_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12)

>>> from transformers import AutoModel

>>> my_model = AutoModel.from_config(my_config)

>>> from transformers import TFAutoModel

>>> my_model = TFAutoModel.from_config(my_config)

Take a look at the Create a custom architecture guide for more information about building custom configurations.

What's next?

Now that you’ve completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you’re interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!