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 3.6 out now! Check out the release notes here.
|⭐️ 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.|
|📦 Models||Download trained pipelines for spaCy.|
|🌌 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.|
|📺 Videos||Our YouTube channel with video tutorials, talks and more.|
|🛠 Changelog||Changes and version history.|
|💝 Contribute||How to contribute to the spaCy project and code base.|
|Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! Learn more →|
|Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll 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.
|🚨 Bug Reports||GitHub Issue Tracker|
|🎁 Feature Requests & Ideas||GitHub Discussions|
|👩💻 Usage Questions||GitHub Discussions · Stack Overflow|
|🗯 General Discussion||GitHub Discussions|
- Support for 70+ languages
- Trained pipelines for different languages and tasks
- Multi-task learning with pretrained transformers like BERT
- Support for pretrained word vectors and embeddings
- State-of-the-art speed
- Production-ready training system
- Linguistically-motivated tokenization
- Components for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more
- Easily extensible with custom components and attributes
- Support for custom models in PyTorch, TensorFlow and other frameworks
- Built in visualizers for syntax and NER
- Easy model packaging, deployment and workflow management
- Robust, rigorously evaluated accuracy
📖 For more details, see the facts, figures and benchmarks.
For detailed installation instructions, see the documentation.
- Operating system: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual Studio)
- Python version: Python 3.6+ (only 64 bit)
- Package managers: pip · conda (via
Using pip, spaCy releases are available as source packages and binary wheels.
Before you install spaCy and its dependencies, make sure that your
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
pip install spacy[lookups] or install
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
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
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
command, or manually by pointing pip to a path or URL.
|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 load a model, use
with the model name or a path to the model data directory.
import spacy nlp = spacy.load("en_core_web_sm") doc = nlp("This is a sentence.")
You can also
import a model directly via its full name and then call its
load() method with no arguments.
import spacy import en_core_web_sm nlp = en_core_web_sm.load() doc = nlp("This is a sentence.")
📖 For more info and examples, check out the models documentation.
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 make changes to the code base. You'll need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, virtualenv and git installed. The compiler part is the trickiest. How to do that depends on your system.
|Ubuntu||Install system-level dependencies via
|Mac||Install a recent version of XCode, including the so-called "Command Line Tools". macOS and OS X ship with Python and git preinstalled.|
|Windows||Install a version of the Visual C++ Build Tools or Visual Studio Express that matches the version that was used to compile your Python interpreter.|
git clone https://github.com/explosion/spaCy cd spaCy python -m venv .env source .env/bin/activate # make sure you are using the latest pip python -m pip install -U pip setuptools wheel pip install -r requirements.txt pip install --no-build-isolation --editable .
To install with extras:
pip install --no-build-isolation --editable .[lookups,cuda102]
spaCy comes with an extensive test suite. In order to run the
tests, you'll usually want to clone the repository and build spaCy from source.
This will also install the required development dependencies and test utilities
defined in the
Alternatively, you can run
pytest on the tests from within the installed
spacy package. Don't forget to also install the test utilities via spaCy's
pip install -r requirements.txt python -m pytest --pyargs spacy