The project's README from github.com/explosion/spaCy. As one markdown file: README.md.
spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products.
spaCy comes with pretrained pipelines and currently supports tokenization and training for 70+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.
💫 Version 3.8 out now! Check out the release notes here.
| Documentation | |
|---|---|
| ⭐️ spaCy 101 | New to spaCy? Here's everything you need to know! |
| 📚 Usage Guides | How to use spaCy and its features. |
| 🚀 New in v3.0 | New features, backwards incompatibilities and migration guide. |
| 🪐 Project Templates | End-to-end workflows you can clone, modify and run. |
| 🎛 API Reference | The detailed reference for spaCy's API. |
| ⏩ GPU Processing | Use spaCy with CUDA-compatible GPU processing. |
| 📦 Models | Download trained pipelines for spaCy. |
| 🦙 Large Language Models | Integrate LLMs into spaCy pipelines. |
| 🌌 Universe | Plugins, extensions, demos and books from the spaCy ecosystem. |
| ⚙️ spaCy VS Code Extension | Additional tooling and features for working with spaCy's config files. |
| 👩🏫 Online Course | Learn spaCy in this free and interactive online course. |
| 📰 Blog | Read about current spaCy and Prodigy development, releases, talks and more from Explosion. |
| 📺 Videos | Our YouTube channel with video tutorials, talks and more. |
| 🔴 Live Stream | Join Matt as he works on spaCy and chat about NLP. |
| 🛠 Changelog | Changes and version history. |
| 💝 Contribute | How to contribute to the spaCy project and code base. |
| 👕 Swag | Support us and our work with unique, custom-designed swag! |
| Custom NLP consulting, implementation and strategic advice by spaCy’s core development team. Streamlined, production-ready, predictable and maintainable. Send us an email or take our 5-minute questionnaire, and well'be in touch! Learn more → |
The spaCy project is maintained by the spaCy team. Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly, so that more people can benefit from it.
| Type | Platforms |
|---|---|
| 🚨 Bug Reports | GitHub Issue Tracker |
| 🎁 Feature Requests & Ideas | GitHub Discussions · Live Stream |
| 👩💻 Usage Questions | GitHub Discussions · Stack Overflow |
| 🗯 General Discussion | GitHub Discussions · Live Stream |
📖 For more details, see the facts, figures and benchmarks.
For detailed installation instructions, see the documentation.
conda-forge)Using pip, spaCy releases are available as source packages and binary wheels.
Before you install spaCy and its dependencies, make sure that your pip,
setuptools and wheel are up to date.
pip install -U pip setuptools wheel
pip install spacy
To install additional data tables for lemmatization and normalization you can
run pip install spacy[lookups] or install
spacy-lookups-data
separately. The lookups package is needed to create blank models with
lemmatization data, and to lemmatize in languages that don't yet come with
pretrained models and aren't powered by third-party libraries.
When using pip it is generally recommended to install packages in a virtual environment to avoid modifying system state:
python -m venv .env
source .env/bin/activate
pip install -U pip setuptools wheel
pip install spacy
You can also install spaCy from conda via the conda-forge channel. For the
feedstock including the build recipe and configuration, check out
this repository.
conda install -c conda-forge spacy
Some updates to spaCy may require downloading new statistical models. If you're
running spaCy v2.0 or higher, you can use the validate command to check if
your installed models are compatible and if not, print details on how to update
them:
pip install -U spacy
python -m spacy validate
If you've trained your own models, keep in mind that your training and runtime inputs must match. After updating spaCy, we recommend retraining your models with the new version.
📖 For details on upgrading from spaCy 2.x to spaCy 3.x, see the migration guide.
Trained pipelines for spaCy can be installed as Python packages. This means
that they're a component of your application, just like any other module. Models
can be installed using spaCy's download
command, or manually by pointing pip to a path or URL.
| Documentation | |
|---|---|
| Available Pipelines | Detailed pipeline descriptions, accuracy figures and benchmarks. |
| Models Documentation | Detailed usage and installation instructions. |
| Training | How to train your own pipelines on your data. |
# Download best-matching version of specific model for your spaCy installation
python -m spacy download en_core_web_sm
# pip install .tar.gz archive or .whl from path or URL
pip install /Users/you/en_core_web_sm-3.0.0.tar.gz
pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz
To loa