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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, PyTorch and TensorFlow, with a seamless integration between them, allowing you to train your models with one then load it for inference with the other.
You can test most of our models directly on their pages from the model hub. We also offer private model hosting, versioning, & an inference API to use those models.
Here are a few examples:
Write With Transformer, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities.
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 Pytorch nn.Module or a TensorFlow tf.keras.Model (depending on your backend) which you can use normally. For instance, 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.
Easy-to-use state-of-the-art models:
Lower compute costs, smaller carbon footprint:
Choose the right framework for every part of a model's lifetime:
Easily customize a model or an example to your needs:
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 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 TensorFlow 2.0, PyTorch or Flax. Please refer to TensorFlow installation page, PyTorch installation page regarding the specific install command for your platform and/or 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 or need the bleeding edge of the code and can't wait for a new release, you must install the library from source.
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.
All the model checkpoints provided by 🤗 Transformers are seamlessly integrated from the huggingface.co model hub where they are uploaded directly by users and organizations.
Current number of checkpoints:
🤗 Transformers currently provides the following architectures (see here for a high-level summary of each them):
templates folder of the repository. Be sure to check the contributing guidelines and contact the maintainers or open an issue to collect feedbacks before starting your PR.To check if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to this table
These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations. You can find more details on the performances in the Examples section of the documentation.
| Section | Description |
|---|---|
| Documentation | Full API documentation and tutorials |
| Task summary | Tasks supported by 🤗 Transformers |
| Preprocessing tutorial | Using the Tokenizer class to prepare data for the models |
| Training and fine-tuning | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the Trainer API |
| Quick tour: Fine-tuning/usage scripts | Example scripts for fine-tuning models on a wide range of tasks |
| Model sharing and uploading | Upload and share your fine-tuned models with the community |
| Migration | Migrate to 🤗 Transformers from pytorch-transformers or pytorch-pretrained-bert |
We now have a paper you can cite for the 🤗 Transformers library:
@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"
}
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