Industrial-strength Natural Language Processing (NLP) in Python
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 statistical models and word vectors, and currently supports tokenization for 50+ 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 2.2 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 v2.2||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.|
💬 Where to ask questions
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.
|🚨 Bug Reports||GitHub Issue Tracker|
|🎁 Feature Requests||GitHub Issue Tracker|
|👩💻 Usage Questions||Stack Overflow · Gitter Chat · Reddit User Group|
|🗯 General Discussion||Gitter Chat · Reddit User Group|
- 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 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 2.7, 3.5+ (only 64 bit)
- Package managers: pip · conda (via
Using pip, spaCy releases are available as source packages and binary wheels (as
pip install spacy
To install additional data tables for lemmatization in spaCy v2.2+ 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 spacy
Thanks to our great community, we've finally re-added conda support. You can now
install spaCy via
conda config --add channels conda-forge conda install spacy
For the feedstock including the build recipe and configuration, check out this 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
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 1.x to spaCy 2.x, see the migration 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.
|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 # out-of-the-box: download best-matching default model python -m spacy download en # 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
Loading and using models
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(u"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(u"This is a sentence.")
📖 For more info and examples, check out the models documentation.
Support for older versions
If you're using an older version (
v1.6.0 or below), you can still download and
install the old models from within spaCy using
python -m spacy.en.download all
python -m spacy.de.download all. The
.tar.gz archives are also
attached to the v1.6.0 release.
To download and install the models manually, unpack the archive, drop the
contained directory into
spacy/data and load the model via
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. See notes on Ubuntu, OS X and Windows for details.
# make sure you are using the latest pip python -m pip install -U pip git clone https://github.com/explosion/spaCy cd spaCy python -m venv .env source .env/bin/activate export PYTHONPATH=`pwd` pip install -r requirements.txt python setup.py build_ext --inplace
Compared to regular install via pip, requirements.txt additionally installs developer dependencies such as Cython. 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.
Install system-level dependencies via
sudo apt-get install build-essential python-dev git
macOS / OS X
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 the Visual C++ Build Tools or 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
Alternatively, you can find out where spaCy is installed and run
that directory. Don't forget to also install the test utilities via spaCy's
python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))" pip install -r path/to/requirements.txt python -m pytest <spacy-directory>
See the documentation for more details and examples.
Release history Release notifications
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size spacy-2.2.1-cp27-cp27mu-manylinux1_x86_64.whl (10.2 MB)||File type Wheel||Python version cp27||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1-cp35-cp35m-manylinux1_x86_64.whl (10.1 MB)||File type Wheel||Python version cp35||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1-cp35-cp35m-win_amd64.whl (9.2 MB)||File type Wheel||Python version cp35||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (14.3 MB)||File type Wheel||Python version cp36||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1-cp36-cp36m-manylinux1_x86_64.whl (10.2 MB)||File type Wheel||Python version cp36||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1-cp36-cp36m-win_amd64.whl (9.3 MB)||File type Wheel||Python version cp36||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (14.0 MB)||File type Wheel||Python version cp37||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1-cp37-cp37m-manylinux1_x86_64.whl (10.2 MB)||File type Wheel||Python version cp37||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1-cp37-cp37m-win_amd64.whl (9.3 MB)||File type Wheel||Python version cp37||Upload date||Hashes View hashes|
|Filename, size spacy-2.2.1.tar.gz (5.8 MB)||File type Source||Python version None||Upload date||Hashes View hashes|
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