FastText
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Developer(s) | Facebook's AI Research (FAIR) lab[1] |
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Initial release | November 9, 2015; 8 years ago (2015-11-09) |
Stable release | 0.9.2[2] / April 28, 2020; 4 years ago (2020-04-28) |
Repository | github |
Written in | C++, Python |
Platform | Linux, macOS, Windows |
Type | Machine learning library |
License | MIT License |
Website | 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]
See also
- Word2vec
- GloVe
- Neural Network
- Natural Language Processing
References
- ^ Mannes, John. "Facebook's fastText library is now optimized for mobile". TechCrunch. Retrieved 12 January 2018.
- ^ Onur Çelebi (2020-04-28). "facebookresearch/fastText/releases/tag/v0.9.2". Facebook. Retrieved 2020-11-21.
- ^ Mannes, John. "Facebook's fastText library is now optimized for mobile". TechCrunch. Retrieved 12 January 2018.
- ^ Ryan, Kevin J. "Facebook's New Open Source Software Can Learn 1 Billion Words in 10 Minutes". Inc. Retrieved 12 January 2018.
- ^ Low, Cherlynn. "Facebook is open-sourcing its AI bot-building research". Engadget. Retrieved 12 January 2018.
- ^ Mannes, John. "Facebook's Artificial Intelligence Research lab releases open source fastText on GitHub". TechCrunch. Retrieved 12 January 2018.
- ^ Sabin, Dyani. "Facebook Makes A.I. Program Available in 294 Languages". Inverse. Retrieved 12 January 2018.
- ^ "Wiki word vectors". fastText. Retrieved 26 November 2020.
- ^ "References · fastText". fasttext.cc. Retrieved 2021-09-08.
- ^ Bojanowski, Piotr; Grave, Edouard; Joulin, Armand; Mikolov, Tomas (2017-06-19). "Enriching Word Vectors with Subword Information". arXiv:1607.04606 [cs.CL].
- ^ Joulin, Armand; Grave, Edouard; Bojanowski, Piotr; Mikolov, Tomas (2016-08-09). "Bag of Tricks for Efficient Text Classification". arXiv:1607.01759 [cs.CL].
- ^ 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
- fastText
- https://research.fb.com/downloads/fasttext/
- v
- t
- e
- Argument mining
- Collocation extraction
- Concept mining
- Coreference resolution
- Deep linguistic processing
- Distant reading
- Information extraction
- Named-entity recognition
- Ontology learning
- Parsing
- Semantic parsing
- Syntactic parsing
- Part-of-speech tagging
- Semantic analysis
- Semantic role labeling
- Semantic decomposition
- Semantic similarity
- Sentiment analysis
Text segmentation |
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datasets and corpora
Types and standards | |
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Data |
and data capture
reviewing
user interface
- Formal semantics
- Hallucination
- Natural Language Toolkit
- spaCy
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