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 withpretrained statistical models and word vectors, and currently supports tokenization for 60+ languages. It features state-of-the-art speed, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. It's commercial open-source software, released under the MIT license.
💫 Version 2.3 out now!Check out the release notes here.
🌙 Version 3.0 (nightly) 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 v2.3 | New features, backwards incompatibilities and migration guide. |
API Reference | The detailed reference for spaCy's API. |
Models | Download statistical language models for spaCy. |
Universe | Libraries, extensions, demos, books and courses. |
Changelog | Changes and version history. |
Contribute | How to contribute to the spaCy project and code base. |
The spaCy project is maintained by @honnibal and@ines, along with core contributors@svlandeg and@adrianeboyd. 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 |
👩💻 Usage Questions | GitHub Discussions · Stack Overflow |
🗯 General Discussion | GitHub Discussions |
Non-destructive tokenization
Named entity recognition
Support for 50+ languages
pretrained statistical models and word vectors
State-of-the-art speed
Easy deep learning integration
Part-of-speech tagging
Labelled dependency parsing
Syntax-driven sentence segmentation
Built in visualizers for syntax and NER
Convenient string-to-hash mapping
Export to numpy data arrays
Efficient binary serialization
Easy model packaging and deployment
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, Visual Studio)
Python version: Python 2.7, 3.5+ (only 64 bit)
Using pip, spaCy releases are available as source packages and binary wheels (as
of v2.0.13
). Before you install spaCy and its dependencies, make sure thatpip
, setuptools
and wheel
are up to date.
pip install -U pip setuptools wheel pip install spacy
For installation on python 2.7 or 3.5 where binary wheels are not provided for
the most recent versions of the dependencies, you can prefer older binary
wheels over newer source packages with --prefer-binary
:
pip install spacy --prefer-binary
To install additional data tables for lemmatization and normalization inspaCy v2.2+ you can run pip install spacy[lookups]
or installspacy-lookups-data
separately. The lookups package is needed to create blank models with
lemmatization data for v2.2+ plus normalization data for v2.3+, 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
Thanks to our great community, we've finally re-added conda support. You can now
install spaCy via conda-forge
:
conda install -c conda-forge spacy
For the feedstock including the build recipe and configuration, check outthis repository. Improvements and pull requests to the recipe and setup are always appreciated.
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 modelswith the new version.
📖 For details on upgrading from spaCy 1.x to spaCy 2.x, see themigration guide.
As of v1.7.0, models 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 Models | Detailed model descriptions, accuracy figures and benchmarks. |
Models Documentation | Detailed usage instructions. |
# download best-matching version of specific model for your spaCy installation python -m spacy download en_core_web_sm # pip install .tar.gz archive from path or URL pip install /Users/you/en_core_web_sm-2.2.0.tar.gz pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz
To load a model, use spacy.load()
with the model name, a shortcut link 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 itsload()
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 themodels documentation.
The other way to install spaCy is to clone itsGitHub 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 andgit installed. The compiler part is the trickiest. How to do that depends on your system. See notes on Ubuntu, OS X and Windows for details.
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 .
To install with extras:
pip install .[lookups,cuda102]
To install all dependencies required for development:
pip install -r requirements.txt
Compared to regular install via pip, requirements.txtadditionally installs developer dependencies such as Cython. For more details and instructions, see the documentation oncompiling spaCy from source and thequickstart widget to get the right commands for your platform and Python version.
Install system-level dependencies via apt-get
:
sudo apt-get install build-essential python-dev git
Install a recent version of XCode, including the so-called "Command Line Tools". macOS and OS X ship with Python and git preinstalled.
Install a version of theVisual C++ Build Toolsor Visual Studio Express that matches the version that was used to compile your Python interpreter. For official distributions these are VS 2008 (Python 2.7), VS 2010 (Python 3.4) and VS 2015 (Python 3.5).
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 requirements.txt
.
Alternatively, you can run pytest
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.txt python -m pytest --pyargs spacy
See the documentation for more details and examples.
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