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¶

Here is how to quickly install transformers from source:

pip install git+https://github.com/huggingface/transformers

Note that this will install not the latest released version, but the bleeding edge master version, which you may want to use in case a bug has been fixed since the last official release and a new release hasn’t been yet rolled out.

While we strive to keep master operational at all times, if you notice some issues, they usually get fixed within a few hours or a day and and you’re more than welcome to help us detect any problems by opening an Issue and this way, things will get fixed even sooner.

Again, you can run:

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

to check 🤗 Transformers is properly installed.

Editable install¶

If you want to constantly use the bleeding edge master version of the source code, or if you want to contribute to the library and need to test the changes in the code you’re making, you will need an editable install. This is done by cloning the repository and installing with the following commands:

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

This command performs a magical link between the folder you cloned the repository to and your python library paths, and it’ll look inside this folder in addition to the normal library-wide paths. So if normally your python packages get installed into:

~/anaconda3/envs/main/lib/python3.7/site-packages/

now this editable install will reside where you clone the folder to, e.g. ~/transformers/ and python will search it too.

Do note that you have to keep that transformers folder around and not delete it to continue using the transfomers library.

Now, let’s get to the real benefit of this installation approach. Say, you saw some new feature has been just committed into master. If you have already performed all the steps above, to update your transformers to include all the latest commits, all you need to do is to cd into that cloned repository folder and update the clone to the latest version:

cd ~/transformers/
git pull

There is nothing else to do. Your python environment will find the bleeding edge version of transformers on the next run.

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 10,000) from huggingface.co (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, we’d love to help with this.

Offline mode¶

It’s possible to run 🤗 Transformers in a firewalled or a no-network environment.

Setting environment variable TRANSFORMERS_OFFLINE=1 will tell 🤗 Transformers to use local files only and will not try to look things up.

Most likely you may want to couple this with HF_DATASETS_OFFLINE=1 that performs the same for 🤗 Datasets if you’re using the latter.

Here is an example of how this can be used on a filesystem that is shared between a normally networked and a firewalled to the external world instances.

On the instance with the normal network run your program which will download and cache models (and optionally datasets if you use 🤗 Datasets). For example:

python examples/seq2seq/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...

and then with the same filesystem you can now run the same program on a firewalled instance:

HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
python examples/seq2seq/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...

and it should succeed without any hanging waiting to timeout.

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!