spaCy: Industrial-strength NLP

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.

<img src="https://github.com/explosion/spaCy/actions/workflows/tests.yml/badge.svg" alt="tests" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/github/release/explosion/spacy.svg?style=flat-square&logo=github" alt="Current Release Version" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/pypi/v/spacy.svg?style=flat-square&logo=pypi&logoColor=white" alt="pypi Version" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/conda/vn/conda-forge/spacy.svg?style=flat-square&logo=conda-forge&logoColor=white" alt="conda Version" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/badge/wheels-%E2%9C%93-4c1.svg?longCache=true&style=flat-square&logo=python&logoColor=white" alt="Python wheels" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square" alt="Code style: black" class="ra0-md-img" loading="lazy" />


<img src="https://static.pepy.tech/personalized-badge/spacy?period=total&units=international_system&left_color=grey&right_color=orange&left_text=pip%20downloads" alt="PyPi downloads" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/conda/dn/conda-forge/spacy?label=conda%20downloads" alt="Conda downloads" class="ra0-md-img" loading="lazy" />

📖 Documentation

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 →

[spacy 101]: https://spacy.io/usage/spacy-101
[new in v3.0]: https://spacy.io/usage/v3
[usage guides]: https://spacy.io/usage/
[api reference]: https://spacy.io/api/
[gpu processing]: https://spacy.io/usage#gpu
[models]: https://spacy.io/models
[large language models]: https://spacy.io/usage/large-language-models
[universe]: https://spacy.io/universe
[spacy vs code extension]: https://github.com/explosion/spacy-vscode
[videos]: https://www.youtube.com/c/ExplosionAI
[live stream]: https://www.youtube.com/playlist?list=PLBmcuObd5An5_iAxNYLJa_xWmNzsYce8c
[online course]: https://course.spacy.io
[blog]: https://explosion.ai
[project templates]: https://github.com/explosion/projects
[changelog]: https://spacy.io/usage#changelog
[contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
[swag]: https://explosion.ai/merch

💬 Where to ask questions

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.

[github issue tracker]: https://github.com/explosion/spaCy/issues
[github discussions]: https://github.com/explosion/spaCy/discussions
[stack overflow]: https://stackoverflow.com/questions/tagged/spacy
[live stream]: https://www.youtube.com/playlist?list=PLBmcuObd5An5_iAxNYLJa_xWmNzsYce8c

Features

• 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.**

⏳ Install spaCy

For detailed installation instructions, see the
documentation.

• Operating system: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual

Studio)

• Python version: Python >=3.7, <3.13 (only 64 bit)
• Package managers: [pip] · [conda] (via conda-forge)

[pip]: https://pypi.org/project/spacy/
[conda]: https://anaconda.org/conda-forge/spacy

pip

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
<code class="ra0-md-code">spacy-lookups-data</code>
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

conda

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

Updating 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.**

📦 Download model packages

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 <code class="ra0-md-code">download</code>
command, or manually by pointing pip to a path or URL.

[available pipelines]: https://spacy.io/models
[models documentation]: https://spacy.io/usage/models
[training]: https://spacy.io/usage/training

# 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

Loading and using models

To load a model, use <code class="ra0-md-code">spacy.load()</code>
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.**

⚒ Compile from source

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.

For more details and instructions, see the documentation on
compiling spaCy from source and the
quickstart widget to get the right
commands for your platform and Python version.

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]

🚦 Run tests

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 <code class="ra0-md-code">requirements.txt</code>.

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
<code class="ra0-md-code">requirements.txt</code>:

pip install -r requirements.txt
python -m pytest --pyargs spacy