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 49+ 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.

💫 Version 2.1 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.
New in v2.1 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.

💬 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

  • Non-destructive tokenization
  • Named entity recognition
  • Support for 50+ languages
  • Pre-trained 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

📖 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, 3.5+ (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

This version

2.1.7

Download files

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

Source Distribution

spacy-2.1.7.tar.gz (30.7 MB view details)

Uploaded Source

Built Distributions

spacy-2.1.7-cp37-cp37m-win_amd64.whl (30.0 MB view details)

Uploaded CPython 3.7mWindows x86-64

spacy-2.1.7-cp37-cp37m-manylinux1_x86_64.whl (30.8 MB view details)

Uploaded CPython 3.7m

spacy-2.1.7-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 (34.4 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-2.1.7-cp36-cp36m-win_amd64.whl (30.0 MB view details)

Uploaded CPython 3.6mWindows x86-64

spacy-2.1.7-cp36-cp36m-manylinux1_x86_64.whl (30.8 MB view details)

Uploaded CPython 3.6m

spacy-2.1.7-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 (34.7 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-2.1.7-cp35-cp35m-win_amd64.whl (29.9 MB view details)

Uploaded CPython 3.5mWindows x86-64

spacy-2.1.7-cp35-cp35m-manylinux1_x86_64.whl (30.8 MB view details)

Uploaded CPython 3.5m

spacy-2.1.7-cp27-cp27mu-manylinux1_x86_64.whl (30.8 MB view details)

Uploaded CPython 2.7mu

File details

Details for the file spacy-2.1.7.tar.gz.

File metadata

  • Download URL: spacy-2.1.7.tar.gz
  • Upload date:
  • Size: 30.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/39.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.6.6

File hashes

Hashes for spacy-2.1.7.tar.gz
Algorithm Hash digest
SHA256 10b208898231153c67da4f2a34462eba201d0014b131de25565cb56b6582934c
MD5 df629bf0953114945f78006fa0dc8d7e
BLAKE2b-256 f104f25cdc3cb6d143ef397c23718026aff606c3e558cbd4939e9e4cb0a4b515

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: spacy-2.1.7-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 30.0 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-2.1.7-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 e09fe6dcd92e9fe6614502b1a0bb9281b6fd0f029275fd0634444324368655d5
MD5 9d36e1d95af5dec0c63e4c1be25d262a
BLAKE2b-256 df825eb3f927817bd5730da0c1bfd9754153c6b37f713ba1ae64770068779b71

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: spacy-2.1.7-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 30.8 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-2.1.7-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 3c3c4cb4b436ef87b7b6e58c8a9e0a109ecdcabf514222a08c178ea171748616
MD5 7b1eeaacfedf26f6759adb0fa5489ab7
BLAKE2b-256 f42b7792103163be4d7d862dce29042554b0d158d3b71fed81e048b97063c395

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-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-2.1.7-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 ff386d92ae33a7c92fbe1553bbd41c0444ba0867679578452e7017f474c36b03
MD5 9c40ee7064ed44a0c09717b2a07a18da
BLAKE2b-256 8f26b00c034b2d0b4495b17eaba8ad3011a7aa1b57f25343bad290c54dd03e48

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: spacy-2.1.7-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 30.0 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-2.1.7-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 18019d5b4544f2a635bdd35b784cc24dd3f42a905d44e05ccd4c21cb359a683c
MD5 74e22e751d3f16a462cd89a219051f2c
BLAKE2b-256 3e40d8184ad1887125843a79a619c0be5e922d38232728b1f9bf4906c6e4c42a

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: spacy-2.1.7-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 30.8 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-2.1.7-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 caa676dcc40d8f54e1265a71f4f1c079d0b4588d2f0daff58e2528c1fc6b0d3a
MD5 919bb04c2c755d1a15514ffb5a63804c
BLAKE2b-256 7f61257772ee66e7586c914d61440978bd234c8cec2011cfa6682ba9dd54d8e5

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-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-2.1.7-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 bf18fc7f17e49a4840ac21f1fa57a1171dc46d4e021272c78e882b8e4fadda26
MD5 31e8468c084188ea5babe2d97911f06d
BLAKE2b-256 f006247a99e61a7ee5704c03cb37c522a1657f07838744f1cf800bd3a8fa595d

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-cp35-cp35m-win_amd64.whl.

File metadata

  • Download URL: spacy-2.1.7-cp35-cp35m-win_amd64.whl
  • Upload date:
  • Size: 29.9 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-2.1.7-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 f7881c3a734565b763dde1cfa88e4877e27e5132bd46f29414f17d388cd1efff
MD5 153dc8056b1e3dbd5da2cfb9e74c9695
BLAKE2b-256 e79545d296f430f343195fce1d8feaff0bd299348708994f9b874103d34496f8

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: spacy-2.1.7-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 30.8 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-2.1.7-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 bcf74c94014c4ffddae47a06770f1f33d5d840df23fe5eb0f47e3a0447b08237
MD5 d41794b91e9e29fdd338e3a7e450547d
BLAKE2b-256 0a10b56f9b9702bc0a78844b307c1009db612c0e907d4902d011be53bfad41ac

See more details on using hashes here.

File details

Details for the file spacy-2.1.7-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

  • Download URL: spacy-2.1.7-cp27-cp27mu-manylinux1_x86_64.whl
  • Upload date:
  • Size: 30.8 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-2.1.7-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 eb0f972363122605f3f205e1d1e39aacf2d0105d6a74af113ee394e232d6f98f
MD5 62a49ae2459b8f11936c49c253ff41b2
BLAKE2b-256 15b77ec6fc45946aa65b3d3fadff02193b9542dff09b8aa39bfba24bf054d212

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