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.
| 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. |
| 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 |
- Non-destructive tokenization
- Syntax-d CE4C riven 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.
- 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, 3.4, 3.5. Only 64 bit. |
| Package managers | pip (source packages only), conda (via conda-forge) |
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 spacyWhen 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 spacyThanks 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 spacyFor the feedstock including the build recipe and configuration, check out this repository. Improvements and pull requests to the recipe and setup are always appreciated.
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 allThe 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 --forceYou 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/dirIf 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.
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().
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 makeInstall system-level dependencies via apt-get:
sudo apt-get install build-essential python-dev gitInstall 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> --vectors --model --slow| 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 |