Repository files navigation

spaCy is a library foradvanced Natural Language Processing in Python andCython. It's built on the very latest research, and was designed from day one tobe used in real products.

spaCy comes withpretrained pipelines and currentlysupports tokenization and training for70+ languages. It featuresstate-of-the-art speed andneural network models for tagging, parsing,named entity recognition,text classification and more, multi-tasklearning with pretrainedtransformers like BERT, as well as aproduction-readytraining system and easymodel packaging, deployment and workflow management. spaCy is commercialopen-source software, released under theMIT 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, live every week.
🛠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 thespaCy 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 thatmore people can benefit from it.

• Support for70+ languages
• Trained pipelines for different languages and tasks
• Multi-task learning with pretrainedtransformers like BERT
• Support for pretrainedword vectors and embeddings
• State-of-the-art speed
• Production-readytraining system
• Linguistically-motivatedtokenization
• Components for namedentity recognition, part-of-speech-tagging,dependency parsing, sentence segmentation,text classification,lemmatization, morphological analysis, entity linking and more
• Easily extensible withcustom components and attributes
• Support for custom models inPyTorch,TensorFlow and other frameworks
• Built invisualizers for syntax and NER
• Easymodel packaging, deployment and workflow management
• Robust, rigorously evaluated accuracy

📖For more details, see thefacts, figures and benchmarks.

For detailed installation instructions, see thedocumentation.

• Operating system: macOS / OS X · Linux · Windows (Cygwin, MinGW, VisualStudio)
• Python version: Python >=3.7, <3.13 (only 64 bit)
• Package managers:pip ·conda (viaconda-forge)

Using pip, spaCy releases are available as source packages and binary wheels.Before you install spaCy and its dependencies, make sure that yourpip,setuptools andwheel are up to date.

pip install -U pip setuptools wheelpip install spacy

To install additional data tables for lemmatization and normalization you canrunpip install spacy[lookups] or installspacy-lookups-dataseparately. The lookups package is needed to create blank models withlemmatization data, and to lemmatize in languages that don't yet come withpretrained models and aren't powered by third-party libraries.

When using pip it is generally recommended to install packages in a virtualenvironment to avoid modifying system state:

python -m venv .envsource .env/bin/activatepip install -U pip setuptools wheelpip install spacy

You can also install spaCy fromconda via theconda-forge channel. For thefeedstock including the build recipe and configuration, check outthis repository.

conda install -c conda-forge spacy

Some updates to spaCy may require downloading new statistical models. If you'rerunning spaCy v2.0 or higher, you can use thevalidate command to check ifyour installed models are compatible and if not, print details on how to updatethem:

pip install -U spacypython -m spacy validate

If you've trained your own models, keep in mind that your training and runtimeinputs must match. After updating spaCy, we recommendretraining your modelswith the new version.

📖For details on upgrading from spaCy 2.x to spaCy 3.x, see themigration guide.

Trained pipelines for spaCy can be installed asPython packages. This meansthat they're a component of your application, just like any other module. Modelscan be installed using spaCy'sdownloadcommand, or manually by pointing pip to a path or URL.

# Download best-matching version of specific model for your spaCy installationpython -m spacy download en_core_web_sm# pip install .tar.gz archive or .whl from path or URLpip install /Users/you/en_core_web_sm-3.0.0.tar.gzpip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whlpip 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 load a model, usespacy.load()with the model name or a path to the model data directory.

importspacynlp=spacy.load("en_core_web_sm")doc=nlp("This is a sentence.")

You can alsoimport a model directly via its full name and then call itsload() method with no arguments.

importspacyimporten_core_web_smnlp=en_core_web_sm.load()doc=nlp("This is a sentence.")

📖For more info and examples, check out themodels documentation.

The other way to install spaCy is to clone itsGitHub repository and build it fromsource. That is the common way if you want to make changes to the code base.You'll need to make sure that you have a development environment consisting of aPython distribution including header files, a compiler,pip,virtualenv andgit installed. The compiler part is the trickiest. How todo that depends on your system.

For more details and instructions, see the documentation oncompiling spaCy from source and thequickstart widget to get the rightcommands for your platform and Python version.

git clone https://github.com/explosion/spaCycd spaCypython -m venv .envsource .env/bin/activate# make sure you are using the latest pippython -m pip install -U pip setuptools wheelpip install -r requirements.txtpip install --no-build-isolation --editable.

To install with extras:

pip install --no-build-isolation --editable .[lookups,cuda102]

spaCy comes with anextensive test suite. In order to run thetests, you'll usually want to clone the repository and build spaCy from source.This will also install the required development dependencies and test utilitiesdefined in therequirements.txt.

Alternatively, you can runpytest on the tests from within the installedspacy package. Don't forget to also install the test utilities via spaCy'srequirements.txt:

pip install -r requirements.txtpython -m pytest --pyargs spacy