Menu
Home Explore People Places Arts History Plants & Animals Science Life & Culture Technology
On this page
fastText
Programming library

fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research (FAIR) lab. 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. Several papers describe the techniques used by fastText. The GitHub repository has been archived on March 19, 2024.

We don't have any images related to fastText yet.
We don't have any YouTube videos related to fastText yet.
We don't have any PDF documents related to fastText yet.
We don't have any Books related to fastText yet.
We don't have any archived web articles related to fastText yet.

See also

References

  1. Mannes, John. "Facebook's fastText library is now optimized for mobile". TechCrunch. Retrieved 12 January 2018. https://techcrunch.com/2017/05/02/facebooks-fasttext-library-is-now-optimized-for-mobile/ ↩

  2. Ryan, Kevin J. "Facebook's New Open Source Software Can Learn 1 Billion Words in 10 Minutes". Inc. Retrieved 12 January 2018. https://www.inc.com/kevin-j-ryan/facebook-open-source-fasttext-learns-1-billion-words-in-10-minutes.html ↩

  3. Low, Cherlynn. "Facebook is open-sourcing its AI bot-building research". Engadget. Retrieved 12 January 2018. https://www.engadget.com/2016/08/18/facebook-open-sourcing-fasttext/ ↩

  4. Mannes, John. "Facebook's Artificial Intelligence Research lab releases open source fastText on GitHub". TechCrunch. Retrieved 12 January 2018. https://techcrunch.com/2016/08/18/facebooks-artificial-intelligence-research-lab-releases-open-source-fasttext-on-github/ ↩

  5. Sabin, Dyani. "Facebook Makes A.I. Program Available in 294 Languages". Inverse. Retrieved 12 January 2018. https://www.inverse.com/article/31075-facebook-machine-learning-language-fasttext ↩

  6. "Wiki word vectors". fastText. Retrieved 26 November 2020. https://fasttext.cc/docs/en/pretrained-vectors.html ↩

  7. "References · fastText". fasttext.cc. Retrieved 2021-09-08. https://fasttext.cc/index.html ↩

  8. Bojanowski, Piotr; Grave, Edouard; Joulin, Armand; Mikolov, Tomas (2017-06-19). "Enriching Word Vectors with Subword Information". arXiv:1607.04606 [cs.CL]. /wiki/ArXiv_(identifier) ↩

  9. Joulin, Armand; Grave, Edouard; Bojanowski, Piotr; Mikolov, Tomas (2016-08-09). "Bag of Tricks for Efficient Text Classification". arXiv:1607.01759 [cs.CL]. /wiki/ArXiv_(identifier) ↩

  10. 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]. /wiki/ArXiv_(identifier) ↩