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F r o m W i k i p e d i a , t h e f r e e e n c y c l o p e d i a
Not to be confused with
Scapy .
spaCy ( spay-SEE ) is an open-source software library for advanced natural language processing , written in the programming languages Python and Cython .[3] [4] The library is published under the MIT license and its main developers are Matthew Honnibal and Ines Montani , the founders of the software company Explosion.
Unlike NLTK , which is widely used for teaching and research, spaCy focuses on providing software for production usage.[5] [6] spaCy also supports deep learning workflows that allow connecting statistical models trained by popular machine learning libraries like TensorFlow , PyTorch or MXNet through its own machine learning library Thinc.[7] [8] Using Thinc as its backend, spaCy features convolutional neural network models for part-of-speech tagging , dependency parsing , text categorization and named entity recognition (NER) . Prebuilt statistical neural network models to perform these tasks are available for 23 languages, including English, Portuguese, Spanish, Russian and Chinese, and there is also a multi-language NER model . Additional support for tokenization for more than 65 languages allows users to train custom models on their own datasets as well.[9]
History [ edit ]
Version 1.0 was released on October 19, 2016, and included preliminary support for deep learning workflows by supporting custom processing pipelines.[10] It further included a rule matcher that supported entity annotations, and an officially documented training API.
Version 2.0 was released on November 7, 2017, and introduced convolutional neural network models for 7 different languages.[11] It also supported custom processing pipeline components and extension attributes, and featured a built-in trainable text classification component.
Version 3.0 was released on February 1, 2021, and introduced state-of-the-art transformer -based pipelines.[12] It also introduced a new configuration system and training workflow, as well as type hints and project templates. This version dropped support for Python 2 .
Main features [ edit ]
Extensions and visualizers [ edit ]
Dependency parse tree visualization generated with the displaCy visualizer
spaCy comes with several extensions and visualizations that are available as free, open-source libraries:
References [ edit ]
^ Choi et al. (2015). It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool .
^ "Google's new artificial intelligence can't understand these sentences. Can you?" . Washington Post . Retrieved 2016-12-18 .
^ "Facts & Figures - spaCy" . spacy.io . Retrieved 2020-04-04 .
^ Bird, Steven; Klein, Ewan; Loper, Edward; Baldridge, Jason (2008). "Multidisciplinary instruction with the Natural Language Toolkit" (PDF) . Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics, ACL : 62. doi :10.3115/1627306.1627317 . ISBN 9781932432145 . S2CID 16932735 .
^ "PyTorch, TensorFlow & MXNet" . thinc.ai . Retrieved 2020-04-04 .
^ "explosion/thinc" . GitHub . Retrieved 2016-12-30 .
^ "Models & Languages | spaCy Usage Documentation" . spacy.io . Retrieved 2020-03-10 .
^ "explosion/spaCy" . GitHub . Retrieved 2021-02-08 .
^ "explosion/spaCy" . GitHub . Retrieved 2021-02-08 .
^ "explosion/spaCy" . GitHub . Retrieved 2021-02-08 .
^ "Models & Languages - spaCy" . spacy.io . Retrieved 2021-02-08 .
^ "Models & Languages | spaCy Usage Documentation" . spacy.io . Retrieved 2021-02-08 .
^ "Benchmarks | spaCy Usage Documentation" . spacy.io . Retrieved 2021-02-08 .
^ Trask et al. (2015). sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings .
External links [ edit ]
R e t r i e v e d f r o m " https://en.wikipedia.org/w/index.php?title=SpaCy&oldid=1220749910 "
C a t e g o r i e s :
● F r e e s c i e n c e s o f t w a r e
● N a t u r a l l a n g u a g e p r o c e s s i n g t o o l k i t s
● P y t h o n ( p r o g r a m m i n g l a n g u a g e ) l i b r a r i e s
● 2 0 1 5 s o f t w a r e
H i d d e n c a t e g o r i e s :
● A r t i c l e s w i t h s h o r t d e s c r i p t i o n
● S h o r t d e s c r i p t i o n i s d i f f e r e n t f r o m W i k i d a t a
● T h i s p a g e w a s l a s t e d i t e d o n 2 5 A p r i l 2 0 2 4 , a t 1 7 : 5 8 ( U T C ) .
● T e x t i s a v a i l a b l e u n d e r t h e C r e a t i v e C o m m o n s A t t r i b u t i o n - S h a r e A l i k e L i c e n s e 4 . 0 ;
a d d i t i o n a l t e r m s m a y a p p l y . B y u s i n g t h i s s i t e , y o u a g r e e t o t h e T e r m s o f U s e a n d P r i v a c y P o l i c y . W i k i p e d i a ® i s a r e g i s t e r e d t r a d e m a r k o f t h e W i k i m e d i a F o u n d a t i o n , I n c . , a n o n - p r o f i t o r g a n i z a t i o n .
● P r i v a c y p o l i c y
● A b o u t W i k i p e d i a
● D i s c l a i m e r s
● C o n t a c t W i k i p e d i a
● C o d e o f C o n d u c t
● D e v e l o p e r s
● S t a t i s t i c s
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● M o b i l e v i e w