Skip to main content

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

Project description

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 pre-trained statistical models and word vectors, and currently supports tokenization for 45+ 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 release notes here.

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

📖 Documentation

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

💬 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.

Type Platforms
🚨 Bug Reports GitHub Issue Tracker
🎁 Feature Requests GitHub Issue Tracker
👩‍💻 Usage Questions Stack Overflow · Gitter Chat · Reddit User Group
🗯 General Discussion Gitter Chat · Reddit User Group

Features

  • Fastest syntactic parser in the world
  • Named entity recognition
  • Non-destructive tokenization
  • Support for 45+ 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.

Install spaCy

For detailed installation instructions, see the documentation.

  • Operating system: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual Studio)
  • Python version: Python 2.7, 3.4+ (only 64 bit)
  • Package managers: pip · conda (via conda-forge)

pip

Using pip, spaCy releases are available as source packages and binary wheels (as of v2.0.13).

pip install spacy

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 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.

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.

📖 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

# out-of-the-box: download best-matching default model
python -m spacy download en

# pip install .tar.gz archive from path or URL
pip install /Users/you/en_core_web_sm-2.1.0.tar.gz
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.1.0/en_core_web_sm-2.1.0.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(u"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(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 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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

spacy-2.1.0.tar.gz (27.7 MB view details)

Uploaded Source

Built Distributions

spacy-2.1.0-cp37-cp37m-win_amd64.whl (26.9 MB view details)

Uploaded CPython 3.7mWindows x86-64

spacy-2.1.0-cp37-cp37m-manylinux1_x86_64.whl (27.7 MB view details)

Uploaded CPython 3.7m

spacy-2.1.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (31.1 MB view details)

Uploaded CPython 3.7mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

spacy-2.1.0-cp36-cp36m-win_amd64.whl (26.9 MB view details)

Uploaded CPython 3.6mWindows x86-64

spacy-2.1.0-cp36-cp36m-manylinux1_x86_64.whl (27.7 MB view details)

Uploaded CPython 3.6m

spacy-2.1.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (31.3 MB view details)

Uploaded CPython 3.6mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

spacy-2.1.0-cp35-cp35m-win_amd64.whl (26.8 MB view details)

Uploaded CPython 3.5mWindows x86-64

spacy-2.1.0-cp35-cp35m-manylinux1_x86_64.whl (27.6 MB view details)

Uploaded CPython 3.5m

spacy-2.1.0-cp27-cp27mu-manylinux1_x86_64.whl (27.7 MB view details)

Uploaded CPython 2.7mu

File details

Details for the file spacy-2.1.0.tar.gz.

File metadata

  • Download URL: spacy-2.1.0.tar.gz
  • Upload date:
  • Size: 27.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.31.1 CPython/3.6.6

File hashes

Hashes for spacy-2.1.0.tar.gz
Algorithm Hash digest
SHA256 e3dbde5b560fb9dd3706bd6838e66e28119b6aa17bcb0711d53e95c830bcf0a7
MD5 65d77a41bab2e45a5e4c581dfd49b038
BLAKE2b-256 ce5e8f21b3f32ea3566764d1c90f4360703be7d1739ed7b51cbf89bed00fa331

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: spacy-2.1.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 26.9 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6

File hashes

Hashes for spacy-2.1.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 ff413e92188fb551bd1efad705b93d64ce5f622518f79b7f37cbe35d9abdb1fd
MD5 c38455e6f3459a1b8dcdfc266682ef61
BLAKE2b-256 1ede09c45b3921d4b63daf6e7feab3e6571217835325c199a2e840267a83a766

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: spacy-2.1.0-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 27.7 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6

File hashes

Hashes for spacy-2.1.0-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 c5475a21c5fa666dd9a2be9af290b5658081693c00f7da29dcf2961552a39f83
MD5 1ae4cb1b3467290eebe5f8f3856495e0
BLAKE2b-256 780fca790def675011f25bce8775cf9002b5085cd2288f85e891f70b32c18752

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for spacy-2.1.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 8bd802978c736d248bdc367c09b02b6d9dee63812347425ddf835c2bc094429f
MD5 bb4a3f6cbf606fcdb7082db0de68e467
BLAKE2b-256 7c56e4bdca055c9e30f87cf225c6b6a593fabada11bda4f57e29dfabd92fce88

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: spacy-2.1.0-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 26.9 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6

File hashes

Hashes for spacy-2.1.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 bc764a066d961a5878c999db123375ce14207ee962c2c82082ab71514f928fd7
MD5 71997b914174ebe75493a81edc65fa9d
BLAKE2b-256 4f8c4a94c53770c3acab856a16c581ca95b077ff43b6436b69bf1462288f9384

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: spacy-2.1.0-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 27.7 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6

File hashes

Hashes for spacy-2.1.0-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 8e6ed82691a7a71300145092f89d61da7aac36da3ad036f9c3aebad0ff3287d7
MD5 8cd3f6f6f057eb2bdfec8100bdf85ee2
BLAKE2b-256 62394bde5da5f18ab0bdd525760c4fe38808b4bb03907a2aea094000d831afe1

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for spacy-2.1.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 1e41e9bcf13a590a6f6351e29b73fc0b6229bc53d51397951860b8373c0b24c2
MD5 f920abd505e7b09f15871ef1a9efe3c4
BLAKE2b-256 3f15ddc4c0e4b21a3cfe18e4f48d615f15535e34254ec0c281002c664d6aa6ec

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp35-cp35m-win_amd64.whl.

File metadata

  • Download URL: spacy-2.1.0-cp35-cp35m-win_amd64.whl
  • Upload date:
  • Size: 26.8 MB
  • Tags: CPython 3.5m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6

File hashes

Hashes for spacy-2.1.0-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 233b063ecd6346fa7df05f920a9f24a7ac6861569d834835272ace533d1fdc50
MD5 8ab4e5f76a9521be36910f1fdcd4d893
BLAKE2b-256 50e0131ca15cc410ead59d3e74c9deaf4a3212be78b70a5214ef1ab246fbfab4

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: spacy-2.1.0-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 27.6 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6

File hashes

Hashes for spacy-2.1.0-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 a0b9d00c71ac574d87c99018a44f8d5989aa31a4fc2c81db4c112659b8968cad
MD5 30df286506136cb1f78566ede8d019d7
BLAKE2b-256 5b6ef158d69b4f7ac298fb72d48abacf133c40f78beda8446cde1a973d8abedc

See more details on using hashes here.

File details

Details for the file spacy-2.1.0-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

  • Download URL: spacy-2.1.0-cp27-cp27mu-manylinux1_x86_64.whl
  • Upload date:
  • Size: 27.7 MB
  • Tags: CPython 2.7mu
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6

File hashes

Hashes for spacy-2.1.0-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 bd61d1e8cd913628dd1a7a1eaeb50cb43bfabc00fdf65b1be8ad091a43267cdf
MD5 7b04d8a5fb6c0fff9df9bf331d5ab59a
BLAKE2b-256 c3b8770103a07019b57d6729bf12772947a990df54215047817a85d917547f95

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page