spacy-transformers: Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

This package provides spaCy components and architectures to use transformer models via Hugging Face’s transformers in spaCy. The result is convenient access to state-of-the-art transformer architectures, such as BERT, GPT-2, XLNet, etc.

This release requires spaCy v3. For the previous version of this library, see the v0.6.x branch.

Features

Use pretrained transformer models like BERT, RoBERTa and XLNet to power your spaCy pipeline.
Easy multi-task learning: backprop to one transformer model from several pipeline components.
Train using spaCy v3’s powerful and extensible config system.
Automatic alignment of transformer output to spaCy’s tokenization.
Easily customize what transformer data is saved in the Doc object.
Easily customize how long documents are processed.
Out-of-the-box serialization and model packaging.

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Installation

Installing the package from pip will automatically install all dependencies, including PyTorch and spaCy. Make sure you install this package before you install the models. Also note that this package requires Python 3.6+, PyTorch v1.5+ and spaCy v3.0+.

pip install spacy[transformers]

For GPU installation, find your CUDA version using nvcc --version and add the version in brackets, e.g. spacy[transformers,cuda92] for CUDA9.2 or spacy[transformers,cuda100] for CUDA10.0.

If you are having trouble installing PyTorch, follow the instructions on the official website for your specific operation system and requirements, or try the following:

pip install spacy-transformers -f https://download.pytorch.org/whl/torch_stable.html

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Documentation

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Important note: This package has been extensively refactored to take advantage of spaCy v3.0. Previous versions that were built for spaCy v2.x worked considerably differently. Please see previous tagged versions of this README for documentation on prior versions.

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Embeddings, Transformers and Transfer Learning: How to use transformers in spaCy
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Training Pipelines and Models: Train and update components on your own data and integrate custom models
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Layers and Model Architectures: Power spaCy components with custom neural networks
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Transformer: Pipeline component API reference
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Transformer architectures: Architectures and registered functions