Industrial-strength Natural Language Processing (NLP) with Python and Cython
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 pre-trained statistical models and word vectors, and currently supports tokenization for 20+ languages. It features the fastest syntactic parser in the world, 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 2.0 out now! Check out the new features here.
|spaCy 101||New to spaCy? Here’s everything you need to know!|
|Usage Guides||How to use spaCy and its features.|
|New in v2.0||New features, backwards incompatibilities and migration guide.|
|API Reference||The detailed reference for spaCy’s API.|
|Models||Download statistical language models for spaCy.|
|Resources||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. 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|
|Usage Questions||StackOverflow, Gitter Chat, Reddit User Group|
|General Discussion||Gitter Chat, Reddit User Group|
- Fastest syntactic parser in the world
- Named entity recognition
- Non-destructive tokenization
- Support for 20+ languages
- Pre-trained statistical models and word vectors
- 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
- State-of-the-art speed
- 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||CPython 2.6, 2.7, 3.3+. Only 64 bit.|
|Package managers||pip (source packages only), conda (via conda-forge)|
Using pip, spaCy releases are currently only available as source packages.
pip install spacy
When using pip it is generally recommended to install packages in a virtual environment to avoid modifying system state:
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-forge:
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 them:
pip install -U spacy 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.|
# out-of-the-box: download best-matching default model python -m spacy download en # download best-matching version of specific model for your spaCy installation python -m spacy download en_core_web_lg # pip install .tar.gz archive from path or URL pip install /Users/you/en_core_web_sm-2.0.0.tar.gz
Loading and using models
To load a model, use spacy.load() with the model’s shortcut link:
import spacy nlp = spacy.load('en') doc = nlp(u'This is a sentence.')
If you’ve installed a model via pip, you can also import it directly and then call its load() method:
import spacy import en_core_web_sm nlp = en_core_web_.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 or 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 spacy.load('en') or spacy.load('de').
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 recent pip/virtualenv versions python -m pip install -U pip venv git clone https://github.com/explosion/spaCy cd spaCy 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.
Instead of the above verbose commands, you can also use the following Fabric commands. All commands assume that your virtual environment is located in a directory .env. If you’re using a different directory, you can change it via the environment variable VENV_DIR, for example VENV_DIR=".custom-env" fab clean make.
|fab env||Create virtual environment and delete previous one, if it exists.|
|fab make||Compile the source.|
|fab clean||Remove compiled objects, including the generated C++.|
|fab test||Run basic tests, aborting after first failure.|
Install system-level dependencies via apt-get:
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 Visual Studio Express or higher 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. First, find out where spaCy is installed:
python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
Then run pytest on that directory. The flags --vectors, --slow and --model are optional and enable additional tests:
# make sure you are using recent pytest version python -m pip install -U pytest python -m pytest <spacy-directory>