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        "author_email": "contact@explosion.ai",
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        "description": "<a href=\"https://explosion.ai\"><img src=\"https://explosion.ai/assets/img/logo.svg\" width=\"125\" height=\"125\" align=\"right\" /></a>\n\n# spacy-transformers: Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy\n\nThis package provides [spaCy](https://github.com/explosion/spaCy) components and\narchitectures to use transformer models via\n[Hugging Face's `transformers`](https://github.com/huggingface/transformers) in\nspaCy. The result is convenient access to state-of-the-art transformer\narchitectures, such as BERT, GPT-2, XLNet, etc.\n\n> **This release requires [spaCy v3](https://spacy.io/usage/v3).** For\n> the previous version of this library, see the\n> [`v0.6.x` branch](https://github.com/explosion/spacy-transformers/tree/v0.6.x).\n\n[![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)\n[![PyPi](https://img.shields.io/pypi/v/spacy-transformers.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/spacy-transformers)\n[![GitHub](https://img.shields.io/github/release/explosion/spacy-transformers/all.svg?style=flat-square&logo=github)](https://github.com/explosion/spacy-transformers/releases)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)\n\n## Features\n\n- Use pretrained transformer models like **BERT**, **RoBERTa** and **XLNet** to\n  power your spaCy pipeline.\n- Easy **multi-task learning**: backprop to one transformer model from several\n  pipeline components.\n- Train using spaCy v3's powerful and extensible config system.\n- Automatic alignment of transformer output to spaCy's tokenization.\n- Easily customize what transformer data is saved in the `Doc` object.\n- Easily customize how long documents are processed.\n- Out-of-the-box serialization and model packaging.\n\n## \ud83d\ude80 Installation\n\nInstalling the package from pip will automatically install all dependencies,\nincluding PyTorch and spaCy. Make sure you install this package **before** you\ninstall the models. Also note that this package requires **Python 3.6+**,\n**PyTorch v1.5+** and **spaCy v3.0+**.\n\n```bash\npip install spacy[transformers]\n```\n\nFor GPU installation, find your CUDA version using `nvcc --version` and add the\n[version in brackets](https://spacy.io/usage/#gpu), e.g.\n`spacy[transformers,cuda92]` for CUDA9.2 or `spacy[transformers,cuda100]` for\nCUDA10.0.\n\nIf you are having trouble installing PyTorch, follow the\n[instructions](https://pytorch.org/get-started/locally/) on the official website\nfor your specific operating system and requirements, or try the following:\n\n```bash\npip install spacy-transformers -f https://download.pytorch.org/whl/torch_stable.html\n```\n\n## \ud83d\udcd6 Documentation\n\n> \u26a0\ufe0f **Important note:** This package has been extensively refactored to take\n> advantage of [spaCy v3.0](https://spacy.io). Previous versions that\n> were built for [spaCy v2.x](https://v2.spacy.io) worked considerably\n> differently. Please see previous tagged versions of this README for\n> documentation on prior versions.\n\n- \ud83d\udcd8\n  [Embeddings, Transformers and Transfer Learning](https://spacy.io/usage/embeddings-transformers):\n  How to use transformers in spaCy\n- \ud83d\udcd8 [Training Pipelines and Models](https://spacy.io/usage/training):\n  Train and update components on your own data and integrate custom models\n- \ud83d\udcd8\n  [Layers and Model Architectures](https://spacy.io/usage/layers-architectures):\n  Power spaCy components with custom neural networks\n- \ud83d\udcd7 [`Transformer`](https://spacy.io/api/transformer): Pipeline\n  component API reference\n- \ud83d\udcd7\n  [Transformer architectures](https://spacy.io/api/architectures#transformers):\n  Architectures and registered functions\n\n\n",
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