fastText

fastText is a library for efficient learning of word representations and sentence classification.

Requirements

fastText builds on modern Mac OS and Linux distributions. Since it uses C++11 features, it requires a compiler with good C++11 support. These include :

• (gcc-4.6.3 or newer) or (clang-3.3 or newer)

Compilation is carried out using a Makefile, so you will need to have a working make. For the word-similarity evaluation script you will need:

• python 2.6 or newer
• numpy & scipy

Building fastText

In order to build fastText, use the following:

$ git clone https://github.com/facebookresearch/fastText.git
$ cd fastText
$ make

This will produce object files for all the classes as well as the main binary fasttext. If you do not plan on using the default system-wide compiler, update the two macros defined at the beginning of the Makefile (CC and INCLUDES).

Building with Docker

If you inted to build with Docker, a Docker file is available here fastText-Docker.

Example use cases

This library has two main use cases: word representation learning and text classification. These were described in the two papers 1 and 2.

Word representation learning

In order to learn word vectors, as described in 1, do:

$ ./fasttext skipgram -input data.txt -output model

where data.txt is a training file containing utf-8 encoded text. By default the word vectors will take into account character n-grams from 3 to 6 characters. At the end of optimization the program will save two files: model.bin and model.vec. model.vec is a text file containing the word vectors, one per line. model.bin is a binary file containing the parameters of the model along with the dictionary and all hyper parameters. The binary file can be used later to compute word vectors or to restart the optimization.

Obtaining word vectors for out-of-vocabulary words

The previously trained model can be used to compute word vectors for out-of-vocabulary words. Provided you have a text file queries.txt containing words for which you want to compute vectors, use the following command:

$ ./fasttext print-vectors model.bin < queries.txt

This will output word vectors to the standard output, one vector per line. This can also be used with pipes:

$ cat queries.txt | ./fasttext print-vectors model.bin

See the provided scripts for an example. For instance, running:

$ ./word-vector-example.sh

will compile the code, download data, compute word vectors and evaluate them on the rare words similarity dataset RW [Thang et al. 2013].

Text classification

This library can also be used to train supervised text classifiers, for instance for sentiment analysis. In order to train a text classifier using the method described in 2, use:

$ ./fasttext supervised -input train.txt -output model

where train.txt is a text file containing a training sentence per line along with the labels. By default, we assume that labels are words that are prefixed by the string __label__. This will output two files: model.bin and model.vec. Once the model was trained, you can evaluate it by computing the precision at 1 (P@1) on a test set using:

$ ./fasttext test model.bin test.txt

In order to obtain the most likely label for a piece of text, use:

$ ./fasttext predict model.bin test.txt

where test.txt contains a piece of text to classify per line. Doing so will output to the standard output the most likely label per line. See classification-example.sh for an example use case. In order to reproduce results from the paper 2, run classification-results.sh, this will download all the datasets and reproduce the results from Table 1.

Full documentation

Invoke a command without arguments to list available arguments and their default values:

$ ./fasttext supervised
Empty input or output path.

The following arguments are mandatory:
  -input      training file path
  -output     output file path

The following arguments are optional:
  -lr         learning rate [0.05]
  -dim        size of word vectors [100]
  -ws         size of the context window [5]
  -epoch      number of epochs [5]
  -minCount   minimal number of word occurences [1]
  -neg        number of negatives sampled [5]
  -wordNgrams max length of word ngram [1]
  -loss       loss function {ns, hs, softmax} [ns]
  -bucket     number of buckets [2000000]
  -minn       min length of char ngram [3]
  -maxn       max length of char ngram [6]
  -thread     number of threads [12]
  -verbose    how often to print to stdout [10000]
  -t          sampling threshold [0.0001]
  -label      labels prefix [__label__]

Defaults may vary by mode. (Word-representation modes skipgram and cbow use a default -minCount of 5.)

References

Please cite 1 if using this code for learning word representations or 2 if using for text classification.

Enriching Word Vectors with Subword Information

[1] P. Bojanowski*, E. Grave*, A. Joulin, T. Mikolov, Enriching Word Vectors with Subword Information

@article{bojanowski2016enriching,
  title={Enriching Word Vectors with Subword Information},
  author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1607.04606},
  year={2016}
}

Bag of Tricks for Efficient Text Classification

[2] 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}
}

(* These authors contributed equally.)

Join the fastText community

• Facebook page: https://www.facebook.com/groups/1174547215919768
• Google group: https://groups.google.com/forum/#!forum/fasttext-library
• Contact: egrave@fb.com, bojanowski@fb.com, ajoulin@fb.com, tmikolov@fb.com

See the CONTRIBUTING file for information about how to help out.

License

fastText is BSD-licensed. We also provide an additional patent grant.