Installation¶

Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0

With pip¶

PyTorch Transformers can be installed using pip as follows:

pip install transformers

From source¶

To install from source, clone the repository and install with:

git clone https://github.com/huggingface/transformers.git
cd transformers
pip install [--editable] .

Tests¶

An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the tests folder and examples tests in the examples folder.

Tests can be run using pytest (install pytest if needed with pip install pytest).

Run all the tests from the root of the cloned repository with the commands:

python -m pytest -sv ./transformers/tests/
python -m pytest -sv ./examples/

OpenAI GPT original tokenization workflow¶

If you want to reproduce the original tokenization process of the OpenAI GPT paper, you will need to install ftfy (use version 4.4.3 if you are using Python 2) and SpaCy:

pip install spacy ftfy==4.4.3
python -m spacy download en

If you don’t install ftfy and SpaCy, the OpenAI GPT tokenizer will default to tokenize using BERT’s BasicTokenizer followed by Byte-Pair Encoding (which should be fine for most usage, don’t worry).

Note on model downloads (Continuous Integration or large-scale deployments)¶

If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way faster, and cheaper. Feel free to contact us privately if you need any help.

Do you want to run a Transformer model on a mobile device?¶

You should check out our swift-coreml-transformers repo.

It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains GPT-2, DistilGPT-2, BERT, and DistilBERT) to CoreML models that run on iOS devices.

At some point in the future, you’ll be able to seamlessly move from pre-training or fine-tuning models in PyTorch to productizing them in CoreML, or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!