transformers

State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0

🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 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 then share them with the community on our model hub. At the same time, each python module defining an architecture can be used as a standalone and modified to enable quick research experiments.

🤗 Transformers is backed by the two most popular deep learning libraries, TensorFlow, with a seamless integration between them, allowing you to train your models with one then load it for inference with the other.

Online demos

You can test most of our models directly on their pages from the inference API to use those models.

Here are a few examples:

• Masked word completion with BERT
• Name Entity Recognition with Electra
• Text generation with GPT-2
• Natural Langugage Inference with RoBERTa
• Summarization with BART
• Question answering with DistilBERT
• Translation with T5

Write With Transformer, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities.

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 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 include pipeline into the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9978193640708923}]

The second line of code downloads and caches the pretrained model used by the pipeline, the third line evaluates it on the given text. Here the answer is "positive" with a confidence of 99.8%.

This is another example of pipeline used for that can extract question answers from some 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 have been included in the huggingface/transformers repository'
... })
{'score': 0.5135612454720828, 'start': 35, 'end': 59, 'answer': 'huggingface/transformers'}

On top of the answer, the pretrained model used here returned its confidence score, along with the start position and its end position 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, you just need to use those three lines of codes (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)

or 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 one (or list) of texts (as we can see on the fourth line of both code examples). It will output a dictionary you can directly pass to your model (which is done on the fifth line).

The model itself is a regular this tutorial explains how to integrate such a model in classic PyTorch or TensorFlow training loop, or how to use our Trainer API to quickly fine-tune the 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, production.

4. Easily customize a model or an example to your needs:

   • Examples for each architecture to reproduce the results by the official authors of said architecture.
   • Expose the models internal as consistently as possible.
   • Model files can be used independently of the library for quick experiments.

• 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 in 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

Installation

With pip

This repository is tested on Python 3.6+, PyTorch 1.0.0+ (PyTorch 1.3.1+ for examples) and TensorFlow 2.0.

You should install 🤗 Transformers in a 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 TensorFlow 2.0, PyTorch or Flax. Please refer to Flax installation page.

When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:

pip install transformers

If you'd like to play with the examples, 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 TensorFlow, PyTorch or Flax to see how to install them with conda.

Models architectures

organizations.

@inproceedings{wolf-etal-2020-transformers,
    title = "Transformers: State-of-the-Art Natural Language Processing",
    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
    pages = "38--45"
}