Metadata-Version: 2.1
Name: spacy-transformers
Version: 1.0.0rc3.dev2
Summary: spaCy pipelines for pre-trained BERT and other transformers
Home-page: https://spacy.io
Author: Explosion
Author-email: contact@explosion.ai
License: MIT
Description: <a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
        
        # spacy-transformers: Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
        
        This package provides [spaCy](https://github.com/explosion/spaCy) components and
        architectures to use transformer models via
        [Hugging Face's `transformers`](https://github.com/huggingface/transformers) in
        spaCy. The result is convenient access to state-of-the-art transformer
        architectures, such as BERT, GPT-2, XLNet, etc.
        
        > 🌙 **This release is a pre-release and requires
        > [spaCy v3](https://nightly.spacy.io/usage/v3) (nightly).** For the previous
        > version of this library, see the
        > [`v0.6.x` branch](https://github.com/explosion/spacy-transformers/tree/v0.6.x).
        
        [![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/18/master.svg?logo=azure-pipelines&style=flat-square)](https://dev.azure.com/explosion-ai/public/_build?definitionId=18)
        [![PyPi](https://img.shields.io/pypi/v/spacy-transformers.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/spacy-transformers)
        [![GitHub](https://img.shields.io/github/release/explosion/spacy-transformers/all.svg?style=flat-square&logo=github)](https://github.com/explosion/spacy-transformers/releases)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)
        
        ## 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.
        
        ## 🚀 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+**.
        
        ```bash
        pip install spacy-nightly[transformers] --pre
        ```
        
        For GPU installation, find your CUDA version using `nvcc --version` and add the
        [version in brackets](https://spacy.io/usage/#gpu), e.g.
        `spacy-nightly[transformers,cuda92]` for CUDA9.2 or
        `spacy-nightly[transformers,cuda100]` for CUDA10.0.
        
        If you are having trouble installing PyTorch, follow the
        [instructions](https://pytorch.org/get-started/locally/) on the official website
        for your specific operation system and requirements, or try the following:
        
        ```bash
        pip install spacy-transformers --pre -f https://download.pytorch.org/whl/torch_stable.html
        ```
        
        ## 📖 Documentation
        
        > ⚠️ **Important note:** This package has been extensively refactored to take
        > advantage of [spaCy v3.0](https://nightly.spacy.io). 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.
        
        - 📘
          [Embeddings, Transformers and Transfer Learning](https://nightly.spacy.io/usage/embeddings-transformers):
          How to use transformers in spaCy
        - 📘 [Training Pipelines and Models](https://nightly.spacy.io/usage/training):
          Train and update components on your own data and integrate custom models
        - 📘
          [Layers and Model Architectures](https://nightly.spacy.io/usage/layers-architectures):
          Power spaCy components with custom neural networks
        - 📗 [`Transformer`](https://nightly.spacy.io/api/transformer): Pipeline
          component API reference
        - 📗
          [Transformer architectures](https://nightly.spacy.io/api/architectures#transformers):
          Architectures and registered functions
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: cuda
Provides-Extra: cuda80
Provides-Extra: cuda90
Provides-Extra: cuda91
Provides-Extra: cuda92
Provides-Extra: cuda100
Provides-Extra: cuda101
Provides-Extra: cuda102
Provides-Extra: cuda110
Provides-Extra: cuda111
