spaCy: Industrial-strength NLP

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 and Bengali. It's commercial open-source software, released under the MIT license.

💫 Version 1.6 out now! Read the release notes here.

📖 Documentation

Usage Workflows	How to use spaCy and its features.
API Reference	The detailed reference for spaCy's API.
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	contact@explosion.ai

Features

• 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.

Supports

Operating system	macOS / OS X, Linux, Windows (Cygwin, MinGW, Visual Studio)
Python version	CPython 2.6, 2.7, 3.3, 3.4, 3.5. 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.

pip

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

conda

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

After installation you need to download a language model. Models for English (en) and German (de) are available.

python -m spacy.en.download all
python -m spacy.de.download all

The download command fetches about 1 GB of data which it installs within the spacy package directory.

Sometimes new releases require a new language model. Then you will have to upgrade to a new model, too. You can also force re-downloading and installing a new language model:

python -m spacy.en.download --force

Download model to custom location

You can specify where spacy.en.download and spacy.de.download download the language model to using the --data-path or -d argument:

python -m spacy.en.download all --data-path /some/dir

If you choose to download to a custom location, you will need to tell spaCy where to load the model from in order to use it. You can do this either by calling spacy.util.set_data_path() before calling spacy.load(), or by passing a path argument to the spacy.en.English or spacy.de.German constructors.

Download models manually

As of v1.6, the models and word vectors are also available as direct downloads from GitHub, attached to the releases as .tar.gz archives.

To install the models manually, first find the default data path. You can use spacy.util.get_data_path() to find the directory where spaCy will look for its models, or change the default data path with spacy.util.set_data_path(). Then simply unpack the archive and place the contained folder in that directory. You can now load the models via spacy.load().

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 https://github.com/explosion/spaCy
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

Ubuntu

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.

Windows

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 --model --slow

🛠 Changelog

Version	Date	Description
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 setup.py, 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