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fastText

From Wikipedia, the free encyclopedia
Programming library
This article is about the text classification library. For the teletext feature, seeFastext.
fastText
DeveloperFacebook's AI Research (FAIR) lab[1]
Initial releaseNovember 9, 2015; 10 years ago (2015-11-09)
Stable release
0.9.2[2] / April 28, 2020; 5 years ago (2020-04-28)
Written inC++,Python
PlatformLinux,macOS,Windows
TypeMachine learninglibrary
LicenseMIT License
Websitefasttext.cc
Repositorygithub.com/facebookresearch/fastText

fastText is a library for learning ofword embeddings and text classification created byFacebook's AI Research (FAIR) lab.[3][4][5][6] The model allows one to create anunsupervised learning orsupervised learning algorithm for obtaining vector representations for words. Facebook makes available pretrained models for 294 languages.[7][8] Several papers describe the techniques used by fastText.[9][10][11][12] The GitHub repository was archived on March 19, 2024.

See also

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References

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  1. ^Mannes, John."Facebook's fastText library is now optimized for mobile".TechCrunch. Retrieved12 January 2018.
  2. ^Onur Çelebi (2020-04-28)."facebookresearch/fastText/releases/tag/v0.9.2".Facebook. Retrieved2020-11-21.
  3. ^Mannes, John."Facebook's fastText library is now optimized for mobile".TechCrunch. Retrieved12 January 2018.
  4. ^Ryan, Kevin J."Facebook's New Open Source Software Can Learn 1 Billion Words in 10 Minutes".Inc. Retrieved12 January 2018.
  5. ^Low, Cherlynn."Facebook is open-sourcing its AI bot-building research".Engadget. Retrieved12 January 2018.
  6. ^Mannes, John."Facebook's Artificial Intelligence Research lab releases open source fastText on GitHub".TechCrunch. Retrieved12 January 2018.
  7. ^Sabin, Dyani."Facebook Makes A.I. Program Available in 294 Languages".Inverse. Retrieved12 January 2018.
  8. ^"Wiki word vectors".fastText. Retrieved26 November 2020.
  9. ^"References · fastText".fasttext.cc. Retrieved2021-09-08.
  10. ^Bojanowski, Piotr; Grave, Edouard; Joulin, Armand; Mikolov, Tomas (2017-06-19). "Enriching Word Vectors with Subword Information".arXiv:1607.04606 [cs.CL].
  11. ^Joulin, Armand; Grave, Edouard; Bojanowski, Piotr; Mikolov, Tomas (2016-08-09). "Bag of Tricks for Efficient Text Classification".arXiv:1607.01759 [cs.CL].
  12. ^Joulin, Armand; Grave, Edouard; Bojanowski, Piotr; Douze, Matthijs; Jégou, Hérve; Mikolov, Tomas (2016-12-12). "FastText.zip: Compressing text classification models".arXiv:1612.03651 [cs.CL].

External links

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General terms
Text analysis
Text segmentation
Automatic summarization
Machine translation
Distributional semantics models
Language resources,
datasets and corpora
Types and
standards
Data
Automatic identification
and data capture
Topic model
Computer-assisted
reviewing
Natural language
user interface
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