mirror of
https://github.com/huggingface/transformers.git
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35401fe50f
* Switch to plain unittest for skipping slow tests.
Add a RUN_SLOW environment variable for running them.
* Switch to plain unittest for PyTorch dependency.
* Switch to plain unittest for TensorFlow dependency.
* Avoid leaking open files in the test suite.
This prevents spurious warnings when running tests.
* Fix unicode warning on Python 2 when running tests.
The warning was:
UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
* Support running PyTorch tests on a GPU.
Reverts 27e015bd.
* Tests no longer require pytest.
* Make tests pass on cuda
68 lines
2.8 KiB
Markdown
68 lines
2.8 KiB
Markdown
# Installation
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Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
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## With pip
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PyTorch Transformers can be installed using pip as follows:
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``` bash
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pip install transformers
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```
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## From source
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To install from source, clone the repository and install with:
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``` bash
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git clone https://github.com/huggingface/transformers.git
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cd transformers
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pip install [--editable] .
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```
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## Tests
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An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
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Tests can be run using `unittest` or `pytest` (install pytest if needed with `pip install pytest`).
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Run all the tests from the root of the cloned repository with the commands:
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```bash
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python -m unittest discover -s transformers/tests -p "*test.py" -t .
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python -m unittest discover -s examples -p "*test.py" -t examples
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```
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or
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``` bash
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python -m pytest -sv ./transformers/tests/
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python -m pytest -sv ./examples/
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```
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By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to `yes` to run them.
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## OpenAI GPT original tokenization workflow
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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`:
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``` bash
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pip install spacy ftfy==4.4.3
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python -m spacy download en
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```
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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).
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## Note on model downloads (Continuous Integration or large-scale deployments)
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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.
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## Do you want to run a Transformer model on a mobile device?
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You should check out our [swift-coreml-transformers](https://github.com/huggingface/swift-coreml-transformers) repo.
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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.
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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,
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or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!
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