Bag of Tricks for Efficient Text Classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
Abstract
This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute.- Anthology ID:
- E17-2068
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 427–431
- Language:
- URL:
- https://aclanthology.org/E17-2068
- DOI:
- Cite (ACL):
- Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017. Bag of Tricks for Efficient Text Classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 427–431, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Bag of Tricks for Efficient Text Classification (Joulin et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/E17-2068.pdf
- Code
- facebookresearch/fastText + additional community code
- Data
- AG News, CPED, DBpedia, YFCC100M, Yahoo! Answers, Yelp