FastText

This article is about the text classification library. For the teletext feature, see Fastext.
fastText
DeveloperFacebook's AI Research (FAIR) lab[1]
ReleaseNovember 9, 2015; 10 years ago (2015-11-09)
Stable release
0.9.2[2] / April 28, 2020; 6 years ago (2020-04-28)
Written inC++, Python
PlatformLinux, macOS, Windows
TypeMachine learning library
LicenseMIT License
Websitefasttext.cc
Repositorygithub.com/facebookresearch/fastText

fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research (FAIR) lab.[3][4][5][6] The model allows one to create an unsupervised learning or supervised 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

References

  1. ^ Mannes, John. "Facebook's fastText library is now optimized for mobile". TechCrunch. Retrieved 12 January 2018.
  2. ^ Onur Çelebi (2020-04-28). "facebookresearch/fastText/releases/tag/v0.9.2". Facebook. Retrieved 2020-11-21.
  3. ^ Mannes, John. "Facebook's fastText library is now optimized for mobile". TechCrunch. Retrieved 12 January 2018.
  4. ^ Ryan, Kevin J. "Facebook's New Open Source Software Can Learn 1 Billion Words in 10 Minutes". Inc. Retrieved 12 January 2018.
  5. ^ Low, Cherlynn. "Facebook is open-sourcing its AI bot-building research". Engadget. Retrieved 12 January 2018.
  6. ^ Mannes, John. "Facebook's Artificial Intelligence Research lab releases open source fastText on GitHub". TechCrunch. Retrieved 12 January 2018.
  7. ^ Sabin, Dyani. "Facebook Makes A.I. Program Available in 294 Languages". Inverse. Retrieved 12 January 2018.
  8. ^ "Wiki word vectors". fastText. Retrieved 26 November 2020.
  9. ^ "References · fastText". fasttext.cc. Retrieved 2021-09-08.
  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].
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