fastText is a library for efficient learning of word representations and sentence classification.
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
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).
This library has two main use cases: word representation learning and text classification. These were described in the two papers 1 and 2.
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.
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-word-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-word-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].
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 and recall at k (P@k and R@k) on a test set using:
$ ./fasttext test model.bin test.txt k
The argument k is optional, and is equal to 1 by default.
In order to obtain the k most likely labels for a piece of text, use:
$ ./fasttext predict model.bin test.txt k
where test.txt contains a piece of text to classify per line.
Doing so will print to the standard output the k most likely labels for each line.
The argument k is optional, and equal to 1 by default.
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.
If you want to compute vector representations of sentences or paragraphs, please use:
$ ./fasttext print-sentence-vectors model.bin < text.txt
This assumes that the text.txt file contains the paragraphs that you want to get vectors for.
The program will output one vector representation per line in the file.
You can also quantize a supervised model to reduce its memory usage with the following command:
$ ./fasttext quantize -output model
This will create a .ftz file with a smaller memory footprint. All the standard functionality, like test or predict work the same way on the quantized models:
$ ./fasttext test model.ftz test.txt
The quantization procedure follows the steps described in 3. You can
run the script quantization-example.sh for an example.
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:
-verbose verbosity level [2]
The following arguments for the dictionary are optional:
-minCount minimal number of word occurences [5]
-minCountLabel minimal number of label occurences [0]
-wordNgrams max length of word ngram [1]
-bucket number of buckets [2000000]
-minn min length of char ngram [3]
-maxn max length of char ngram [6]
-t sampling threshold [0.0001]
-label labels prefix [__label__]
The following arguments for training are optional:
-lr learning rate [0.05]
-lrUpdateRate change the rate of updates for the learning rate [100]
-dim size of word vectors [100]
-ws size of the context window [5]
-epoch number of epochs [5]
-neg number of negatives sampled [5]
-loss loss function {ns, hs, softmax} [ns]
-thread number of threads [12]
-pretrainedVectors pretrained word vectors for supervised learning []
-saveOutput whether output params should be saved [0]
The following arguments for quantization are optional:
-cutoff number of words and ngrams to retain [0]
-retrain finetune embeddings if a cutoff is applied [0]
-qnorm quantizing the norm separately [0]
-qout quantizing the classifier [0]
-dsub size of each sub-vector [2]
Defaults may vary by mode. (Word-representation modes skipgram and cbow use a default -minCount of 5.)
Please cite 1 if using this code for learning word representations or 2 if using for text classification.
[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}
}
[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}
}
[3] 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}
}
(* These authors contributed equally.)
You can find the preprocessed YFCC100M data used in [2] at https://research.facebook.com/research/fasttext/
Pre-trained word vectors for 294 languages are available here.
- Facebook page: https://www.facebook.com/groups/1174547215919768
- Google group: https://groups.google.com/forum/#!forum/fasttext-library
- Contact: [email protected], [email protected], [email protected], [email protected]
See the CONTRIBUTING file for information about how to help out.
fastText is BSD-licensed. We also provide an additional patent grant.