Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone.

🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other.

Quick tour

To immediately use a model on a given text, we provide the pipeline API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts:

>>> from transformers import pipeline

# Allocate a pipeline for sentiment-analysis
>>> classifier = pipeline('sentiment-analysis')
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]

The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here the answer is "positive" with a confidence of 99.97%.

Many NLP tasks have a pre-trained pipeline ready to go. For example, we can easily extract question answers given context:

>>> from transformers import pipeline

# Allocate a pipeline for question-answering
>>> question_answerer = pipeline('question-answering')
>>> question_answerer({
...     'question': 'What is the name of the repository ?',
...     'context': 'Pipeline has been included in the huggingface/transformers repository'
... })
{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}

In addition to the answer, the pretrained model used here returned its confidence score, along with the start position and end position of the answer in the tokenized sentence. You can learn more about the tasks supported by the pipeline API in this tutorial.

To download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version:

>>> from transformers import AutoTokenizer, AutoModel

>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = AutoModel.from_pretrained("bert-base-uncased")

>>> inputs = tokenizer("Hello world!", return_tensors="pt")
>>> outputs = model(**inputs)

And here is the equivalent code for TensorFlow:

>>> from transformers import AutoTokenizer, TFAutoModel

>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")

>>> inputs = tokenizer("Hello world!", return_tensors="tf")
>>> outputs = model(**inputs)

The tokenizer is responsible for all the preprocessing the pretrained model expects, and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator.

The model itself is a regular Pytorch nn.Module or a TensorFlow tf.keras.Model (depending on your backend) which you can use normally. This tutorial explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our Trainer API to quickly fine-tune on a new dataset.

Why should I use transformers?

  1. Easy-to-use state-of-the-art models:

    • High performance on NLU and NLG tasks.
    • Low barrier to entry for educators and practitioners.
    • Few user-facing abstractions with just three classes to learn.
    • A unified API for using all our pretrained models.
  2. Lower compute costs, smaller carbon footprint:

    • Researchers can share trained models instead of always retraining.
    • Practitioners can reduce compute time and production costs.
    • Dozens of architectures with over 2,000 pretrained models, some in more than 100 languages.
  3. Choose the right framework for every part of a model's lifetime:

    • Train state-of-the-art models in 3 lines of code.
    • Move a single model between TF2.0/PyTorch frameworks at will.
    • Seamlessly pick the right framework for training, evaluation and production.
  4. Easily customize a model or an example to your needs:

    • We provide examples for each architecture to reproduce the results published by its original authors.
    • Model internals are exposed as consistently as possible.
    • Model files can be used independently of the library for quick experiments.

Why shouldn't I use transformers?

  • This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files.
  • The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library.
  • While we strive to present as many use cases as possible, the scripts in our examples folder are just that: examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.


With pip

This repository is tested on Python 3.6+, Flax 0.3.2+, PyTorch 1.3.1+ and TensorFlow 2.3+.

You should install 🤗 Transformers in a virtual environment. If you're unfamiliar with Python virtual environments, check out the user guide.

First, create a virtual environment with the version of Python you're going to use and activate it.

Then, you will need to install at least one of Flax, PyTorch or TensorFlow.
Please refer to TensorFlow installation page, PyTorch installation page and/or Flax installation page regarding the specific install command for your platform.

When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:

pip install transformers

If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must install the library from source.

With conda

Since Transformers version v4.0.0, we now have a conda channel: huggingface.

🤗 Transformers can be installed using conda as follows:

conda install -c huggingface transformers

Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.