mirror of https://github.com/explosion/spaCy.git
aiartificial-intelligencecythondata-sciencedeep-learningentity-linkingmachine-learningnamed-entity-recognitionnatural-language-processingneural-networkneural-networksnlpnlp-librarypythonspacystarred-explosion-repostarred-repotext-classificationtokenization
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README.rst
.. image:: https://travis-ci.org/spacy-io/spaCy.svg?branch=master :target: https://travis-ci.org/spacy-io/spaCy ============================== 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 2.6, 2.7, 3.3, 3.4, 3.5 (only 64 bit) * OSX * Linux * Windows (Cygwin, MinGW, Visual Studio)