Installation

🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.

You should install 🤗 Transformers in a virtual environment. If you’re unfamiliar with Python virtual environments, check out the user guide. Create a virtual environment with the version of Python you’re going to use and activate it.

Now, if you want to use 🤗 Transformers, you can install it with pip. If you’d like to play with the examples, you must install it from source.

Installation with pip

First you need to install one of, or both, TensorFlow 2.0 and PyTorch. Please refer to TensorFlow installation page, PyTorch installation page and/or Flax installation page regarding the specific install command for your platform.

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

pip install transformers

Alternatively, for CPU-support only, you can install 🤗 Transformers and PyTorch in one line with:

pip install transformers[torch]

or 🤗 Transformers and TensorFlow 2.0 in one line with:

pip install transformers[tf-cpu]

or 🤗 Transformers and Flax in one line with:

pip install transformers[flax]

To check 🤗 Transformers is properly installed, run the following command:

python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"

It should download a pretrained model then print something like

[{'label': 'POSITIVE', 'score': 0.9998704791069031}]

(Note that TensorFlow will print additional stuff before that last statement.)

Installing from source

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

git clone https://github.com/huggingface/transformers.git
cd transformers
pip install -e .

Again, you can run

python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"

to check 🤗 Transformers is properly installed.

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.

Caching models

This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location with cache_dir=... when you use methods like from_pretrained, these models will automatically be downloaded in the folder given by the shell environment variable TRANSFORMERS_CACHE. The default value for it will be the Hugging Face cache home followed by /transformers/. This is (by order of priority):

  • shell environment variable HF_HOME

  • shell environment variable XDG_CACHE_HOME + /huggingface/

  • default: ~/.cache/huggingface/

So if you don’t have any specific environment variable set, the cache directory will be at ~/.cache/huggingface/transformers/.

Note: If you have set a shell environment variable for one of the predecessors of this library (PYTORCH_TRANSFORMERS_CACHE or PYTORCH_PRETRAINED_BERT_CACHE), those will be used if there is no shell environment variable for TRANSFORMERS_CACHE.

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 pretraining or fine-tuning models in PyTorch or TensorFlow 2.0 to productizing them in CoreML, or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch or TensorFlow 2.0. Super exciting!