# Supervised models · fastText

This page gathers several pre-trained supervised models on several datasets.

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Description

The regular models are trained using the procedure described in [1]. They can be reproduced using the classification-results.sh script within our github repository. The quantized models are build by using the respective supervised settings and adding the following flags to the quantize subcommand.

    -qnorm -retrain -cutoff 100000

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Table of models

Each entry describes the test accuracy and size of the model. You can click on a table cell to download the corresponding model.

dataset | ag news | amazon review full | amazon review polarity | dbpedia
--- | --- | --- | --- | ---
regular | 0.924 / 387MB | 0.603 / 462MB | 0.946 / 471MB | 0.986 / 427MB
compressed | 0.92 / 1.6MB | 0.599 / 1.6MB | 0.93 / 1.6MB | 0.984 / 1.7MB

dataset | sogou news | yahoo answers | yelp review polarity | yelp review full
--- | --- | --- | --- | ---
regular | 0.969 / 402MB | 0.724 / 494MB | 0.957 / 409MB | 0.639 / 412MB
compressed | 0.968 / 1.4MB | 0.717 / 1.6MB | 0.957 / 1.5MB | 0.636 / 1.5MB

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References

If you use these models, please cite the following paper:

[1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification

    @article{joulin2016bag,
      title={Bag of Tricks for Efficient Text Classification},
      author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
      journal={arXiv preprint arXiv:1607.01759},
      year={2016}
    }

[2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, FastText.zip: Compressing text classification models

    @article{joulin2016fasttext,
      title={FastText.zip: Compressing text classification models},
      author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{\'e}gou, H{\'e}rve and Mikolov, Tomas},
      journal={arXiv preprint arXiv:1612.03651},
      year={2016}
    }

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