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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 60+ 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.

๐Ÿ’ซ Version 2.3 out now! Check out the release notes here.

๐ŸŒ™ Version 3.0 (nightly) out now! Check out the release notes here.

Azure Pipelines Travis Build Status Current Release Version pypi Version conda Version Python wheels PyPi downloads Conda downloads Model downloads Code style: black spaCy on Twitter

๐Ÿ“– Documentation

Documentation
spaCy 101 New to spaCy? Here's everything you need to know!
Usage Guides How to use spaCy and its features.
New in v2.3 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.

๐Ÿ’ฌ 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.

Type Platforms
๐Ÿšจ Bug Reports GitHub Issue Tracker
๐ŸŽ Feature Requests GitHub Issue Tracker
๐Ÿ‘ฉโ€๐Ÿ’ป Usage Questions Stack Overflow ยท Gitter Chat ยท Reddit User Group
๐Ÿ—ฏ General Discussion Gitter Chat ยท Reddit User Group

Features

  • 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

๐Ÿ“– For more details, see the facts, figures and benchmarks.

Install spaCy

For detailed installation instructions, see the documentation.

  • Operating system: macOS / OS X ยท Linux ยท Windows (Cygwin, MinGW, Visual Studio)
  • Python version: Python 2.7, 3.5+ (only 64 bit)
  • Package managers: pip ยท conda (via conda-forge)

pip

Using pip, spaCy releases are available as source packages and binary wheels (as of v2.0.13). Before you install spaCy and its dependencies, make sure that pip and setuptools are up to date.

pip install -U pip setuptools
pip install spacy

For installation on python 3.5 where binary wheels are not provided for the most recent versions of the dependencies, you can prefer older binary wheels over newer source packages with --prefer-binary:

pip install spacy --prefer-binary

To install additional data tables for lemmatization and normalization in spaCy v2.2+ you can run pip install spacy[lookups] or install spacy-lookups-data separately. The lookups package is needed to create blank models with lemmatization data for v2.2+ plus normalization data for v2.3+, 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

conda

Thanks to our great community, we've finally re-added conda support. You can now install spaCy via conda-forge:

conda install -c conda-forge 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.

Updating spaCy

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
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.

๐Ÿ“– For details on upgrading from spaCy 1.x to spaCy 2.x, see the migration guide.

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. Models can be installed using spaCy's download command, or manually by pointing pip to a path or URL.

Documentation
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

# 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("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("This is a sentence.")

๐Ÿ“– For more info and examples, check out the models documentation.

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.

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 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).

Run tests

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 requirements.txt.

Alternatively, you can find out where spaCy is installed and run pytest on that directory. Don't forget to also install the test utilities via spaCy's requirements.txt:

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.


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