💫 Industrial-strength Natural Language Processing (NLP) in Python
Go to file
Henning Peters 1c0c2f565b Update .travis.yml 2016-02-09 19:34:24 +01:00
appveyor@9f94a16f0e Adding submodule spaCy-appveyor-toolkit 2015-10-25 20:22:49 +03:00
bin fix cythonize 2016-02-05 16:17:23 +01:00
contributors Add contributor. 2015-10-07 17:55:46 -07:00
corpora/en * Add wordnet 2015-09-21 19:06:48 +10:00
examples * Fix parallel_parse script 2016-02-07 02:56:16 +01:00
include * Add header files to repo, to prevent cross-compilation problems 2016-02-06 22:57:11 +01:00
lang_data * Set the German lemma rules to be an empty JSON object 2016-02-02 22:30:51 +01:00
services * Add displacy service 2015-10-28 17:36:11 +01:00
spacy * Increment version 2016-02-07 13:48:58 +01:00
website * Add sense2vec-reddit draft 2016-02-09 14:43:05 +01:00
.appveyor.yml adapt travis/appveyor to latest sputnik 2016-01-16 09:56:12 +01:00
.gitignore Added Windows file to .gitignore 2015-10-13 10:58:30 +03:00
.gitmodules Switching to henningpeters/spaCy-appveyor-toolkit 2015-10-26 00:16:35 +03:00
.travis.yml Update .travis.yml 2016-02-09 19:34:24 +01:00
LICENSE.txt * Change from AGPL to MIT 2015-09-28 07:37:12 +10:00
MANIFEST.in fix windows readme 2015-12-21 21:58:53 +01:00
README-MSVC.txt fix windows readme 2015-12-21 21:58:53 +01:00
README.md Update README.md 2016-02-08 18:48:16 +01:00
bootstrap_python_env.sh * Add bootstrap script 2015-03-16 14:01:36 -04:00
buildbot.json switch to buildbot.json 2016-02-09 16:11:08 +01:00
fabfile.py Merge branch 'master' of https://github.com/honnibal/spaCy 2015-12-28 18:03:06 +01:00
package.json fix package.json 2016-01-14 15:24:51 +01:00
requirements.txt * Fix requirement of thinc 2016-02-05 11:50:11 +01:00
setup.py add sun data 2016-02-09 16:42:55 +01:00
tox.ini refactor setup.py 2015-12-13 23:32:23 +01:00
wordnet_license.txt * Add WordNet license file 2015-02-01 16:11:53 +11:00

README.md

Travis CI status Appveyor status

spaCy: Industrial-strength NLP

spaCy is a library for advanced natural language processing in Python and Cython.

Documentation and details: https://spacy.io/

spaCy is built on the very latest research, but it isn't researchware. It was designed from day 1 to be used in real products. It's commercial open-source software, released under the MIT license.

Features

  • Labelled dependency parsing (91.8% accuracy on OntoNotes 5)
  • Named entity recognition (82.6% accuracy on OntoNotes 5)
  • Part-of-speech tagging (97.1% accuracy on OntoNotes 5)
  • Easy to use word vectors
  • All strings mapped to integer IDs
  • Export to numpy data arrays
  • Alignment maintained to original string, ensuring easy mark up calculation
  • Range of easy-to-use orthographic features.
  • No pre-processing required. spaCy takes raw text as input, warts and newlines and all.

Top Peformance

  • 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

  • CPython 2.7
  • CPython 3.4
  • CPython 3.5
  • OSX
  • Linux
  • Windows (Cygwin, MinGW, Visual Studio)

Difficult to support:

  • PyPy 2.7
  • PyPy 3.4