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Industrial-strength Natural Language Processing (NLP) with Python and Cython

Project description

spaCy is a library for advanced natural language processing in Python and Cython. spaCy is built on the very latest research, but it isn’t researchware. It was designed from day one to be used in real products. spaCy currently supports English and German, as well as tokenization for Chinese, Spanish, Italian, French, Portuguese, Dutch, Swedish, Finnish, Hungarian, Bengali and Hebrew. It’s commercial open-source software, released under the MIT license.

💫 Version 1.7 out now! Read the release notes here.

Build Status Current Release Version pypi Version conda Version spaCy on Gitter spaCy on Twitter

📖 Documentation

Usage Workflows How to use spaCy and its features.
API Reference The detailed reference for spaCy’s API.
Troubleshooting Common problems and solutions for beginners.
Tutorials End-to-end examples, with code you can modify and run.
Showcase & Demos Demos, libraries and products from the spaCy community.
Contribute How to contribute to the spaCy project and code base.

💬 Where to ask questions

Bug reports GitHub issue tracker
Usage questions StackOverflow, Gitter chat, Reddit user group
General discussion Gitter chat, Reddit user group
Commercial support


  • Non-destructive tokenization
  • Syntax-driven sentence segmentation
  • Pre-trained word vectors
  • Part-of-speech tagging
  • Named entity recognition
  • Labelled dependency parsing
  • Convenient string-to-int mapping
  • Export to numpy data arrays
  • GIL-free multi-threading
  • Efficient binary serialization
  • Easy deep learning integration
  • Statistical models for English and German
  • State-of-the-art speed
  • Robust, rigorously evaluated accuracy

See facts, figures and benchmarks.

Top Performance

  • Fastest in the world: <50ms per document. No faster system has ever been announced.
  • Accuracy within 1% of the current state of the art on all tasks performed (parsing, named entity recognition, part-of-speech tagging). The only more accurate systems are an order of magnitude slower or more.


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)

Install spaCy

Installation requires a working build environment. See notes on Ubuntu, macOS/OS X and Windows for details.


Using pip, spaCy releases are currently only available as source packages.

pip install -U spacy

When using pip it is generally recommended to install packages in a virtualenv to avoid modifying system state:

virtualenv .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.

Download models

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. They’re versioned and can be defined as a dependency in your requirements.txt. Models can be installed from a download URL or a local directory, manually or via pip. Their data can be located anywhere on your file system. To make a model available to spaCy, all you need to do is create a “shortcut link”, an internal alias that tells spaCy where to find the data files for a specific model name.

spaCy Models Available models, latest releases and direct download.
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_md

# pip install .tar.gz archive from path or URL
pip install /Users/you/en_core_web_md-1.2.0.tar.gz
pip install

# set up shortcut link to load installed package as "en_default"
python -m spacy link en_core_web_md en_default

# set up shortcut link to load local model as "my_amazing_model"
python -m spacy link /Users/you/data my_amazing_model

Loading and using models

To load a model, use spacy.load() with the model’s shortcut link:

import spacy
nlp = spacy.load('en_default')
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 with no arguments. This should also work for older models in previous versions of spaCy.

import spacy
import en_core_web_md

nlp = en_core_web_md.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 all or python -m 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 enviroment 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 virtualenv
git clone
cd spaCy

virtualenv .env
source .env/bin/activate
pip install -r requirements.txt
pip install -e .

Compared to regular install via pip requirements.txt additionally installs developer dependencies such as Cython.

Instead of the above verbose commands, you can also use the following Fabric commands:

fab env Create virtualenv 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.

All commands assume that your virtualenv 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


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).

Run tests

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> --vectors --models --slow

🛠 Changelog

Version Date Description
v1.7.5 2017-04-07 Bug fixes and new CLI commands
v1.7.3 2017-03-26 Alpha support for Hebrew, new CLI commands and bug fixes
v1.7.2 2017-03-20 Small fixes to beam parser and model linking
v1.7.1 2017-03-19 Fix data download for system installation
v1.7.0 2017-03-18 New 50 MB model, CLI, better downloads and lots of bug fixes
v1.6.0 2017-01-16 Improvements to tokenizer and tests
v1.5.0 2016-12-27 Alpha support for Swedish and Hungarian
v1.4.0 2016-12-18 Improved language data and alpha Dutch support
v1.3.0 2016-12-03 Improve API consistency
v1.2.0 2016-11-04 Alpha tokenizers for Chinese, French, Spanish, Italian and Portuguese
v1.1.0 2016-10-23 Bug fixes and adjustments
v1.0.0 2016-10-18 Support for deep learning workflows and entity-aware rule matcher
v0.101.0 2016-05-10 Fixed German model
v0.100.7 2016-05-05 German support
v0.100.6 2016-03-08 Add support for GloVe vectors
v0.100.5 2016-02-07 Fix incorrect use of header file
v0.100.4 2016-02-07 Fix OSX problem introduced in 0.100.3
v0.100.3 2016-02-06 Multi-threading, faster loading and bugfixes
v0.100.2 2016-01-21 Fix data version lock
v0.100.1 2016-01-21 Fix install for OSX
v0.100 2016-01-19 Revise, better model downloads, bug fixes
v0.99 2015-11-08 Improve span merging, internal refactoring
v0.98 2015-11-03 Smaller package, bug fixes
v0.97 2015-10-23 Load the StringStore from a json list, instead of a text file
v0.96 2015-10-19 Hotfix to .merge method
v0.95 2015-10-18 Bug fixes
v0.94 2015-10-09 Fix memory and parse errors
v0.93 2015-09-22 Bug fixes to word vectors

Release history Release notifications | RSS feed

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