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
d31c911f83
cleanup |
||
|---|---|---|
| bin | ||
| contributors | ||
| corpora/en | ||
| examples | ||
| lang_data | ||
| spacy | ||
| tests | ||
| website | ||
| .gitignore | ||
| .travis.yml | ||
| LICENSE.txt | ||
| MANIFEST.in | ||
| README.md | ||
| bootstrap_python_env.sh | ||
| fabfile.py | ||
| requirements.txt | ||
| setup.py | ||
| wordnet_license.txt | ||
README.md
spaCy: Industrial-strength NLP
spaCy is a library for advanced natural language processing in Python and Cython.
Documentation and details: http://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
- OSX
- Linux
- Cygwin
Want to support:
- Visual Studio
Difficult to support:
- PyPy 2.7
- PyPy 3.4