SpaCy

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. ⭐️ [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 → [spacy 101]: [new in v3.0]: [usage guides]: [api reference]: [gpu processing]: [models]: [large language models]: [universe]: [spacy vs code extension]: [videos]: [live stream]: [online course]: [blog]: [project templates]: [changelog]: [contribute]: [swag]: 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. 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. 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. 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. 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: 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. [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. You can also import a model directly via its full name and then call its load() method with no arguments. The other way to install spaCy is to clone its GitHub repository and build it from source. That is the common way if you want to