floret is an extended version of fastText that can produce word representations for any word from a compact vector table. It combines:
- fastText's subwords to provide embeddings for any word
- Bloom embeddings ("hashing trick") for a compact vector table
To learn more about floret, check out our blog post on floret vectors.
For a hands-on introduction, experiment with English vectors in this example
notebook: intro_to_floret
git clone https://github.com/explosion/floret
cd floret
makeThis produces the main binary floret.
Install the python wrapper with pip:
pip install floretOr install from source in developer mode:
git clone https://github.com/explosion/floret
cd floret
pip install -r requirements.txt
pip install --no-build-isolation --editable .See the python docs.
floret adds two additional command line options to fasttext:
-mode fasttext (default) or floret (word and char ngrams hashed in buckets) [fasttext]
-hashCount floret mode only: number of hashes (1-4) per word/subword [1]
With -mode floret, the word entries are stored in the same table as the
subword embeddings (buckets), reducing the size of the saved vector data.
With -hashCount 2, each entry is stored as the sum of 2 rows in the internal
subwords hash table. floret supports 1-4 hashes per entry in the embeddings
table. By storing an entry in the embedding table as the sum of more than one
row, it is possible to greatly reduce the number of rows in the table with a
relatively small effect on the performance, both in terms of accuracy and
speed.
Here's how to train CBOW embeddings with subwords as 4-grams and 5-grams, 2 hashes per entry, and a compact table of 50K entries rather than the default of 2M entries.
floret cbow -dim 300 -minn 4 -maxn 5 -mode floret -hashCount 2 -bucket 50000 \
-input input.txt -output vectorsWith the -mode floret option, floret will save an additional vector table
with the file ending .floret. The format is very similar to .vec with a
header line followed by one line per vector. The word tokens are replaced with
the index of the row and the header is extended to contain all the relevant
training settings needed to load this table in spaCy.
To import this vector table in spaCy v3.2+:
spacy init vectors LANG vectors.floret spacy_vectors_dir --mode floretIn its original implementation, fastText stores words and subwords in two separate tables. The word table contains one entry per word in the vocabulary (typically ~1M entries) and the subwords are stored a separate fixed-size table by hashing each subword into one row in the table (default 2M entries). A relatively large table is used to reduce the number of collisions between subwords. However, for 1M words + 2M subwords with 300-dimensional vectors of 32-bit floats, you'd need around 3GB to store the resulting data, which is prohibitive for many use cases.
In addition, many libraries that import vectors only support the word table
(.vec), which limits the coverage to words above a certain frequency in the
training data. For languages with rich morphology, even a large vector table
may not provide good coverage for words seen during training and you are still
likely to encounter words that were not seen at all during training.
In order to store word and subword vectors in a more compact format, we turn to
an algorithm that's been used by spaCy all along: Bloom
embeddings. Bloom embeddings (also called the "hashing trick", or known as
HashEmbed within spaCy's ML
library thinc) can be used to store distinct
representations in a compact table by hashing each entry into multiple rows in
the table. By representing each entry as the sum of multiple rows, where it's
unlikely that two entries will collide on multiple hashes, most entries will
end up with a distinct representation.
With the settings -minn 4 -maxn 5 -mode floret -hashCount 2, the embedding
for the word apple is stored internally as the sum of 2 hashed rows for each
of the word, 4-grams and 5-grams. The word is padded with the BOW and EOW
characters < and >, creating the following word and subword entries:
<apple>
<app
appl
pple
ple>
<appl
apple
pple>
For compatibility with spaCy,
MurmurHash is used to hash the word and
char ngram strings. The final embedding for apple is then the sum of two rows
(-hashCount 2) per word and char ngram above.
With -mode floret, floret will save an additional vector table with the
ending .floret alongside the usual .bin and .vec files. The format is
very similar to .vec with a header line followed by one line per entry in the
vector table with the row index rather than a word token at the beginning of
each line. The header is extended to contain all the training settings required
to use this table in another application or library like spaCy.
The header contains the space-separated settings:
bucket dim minn maxn hashCount hashSeed BOW EOW
A demo .floret table with -bucket 10 -dim 10 -minn 2 -maxn3 -hashCount 2:
10 10 2 3 2 2166136261 < >
0 -2.2611 3.9302 2.6676 -11.233 0.093715 -10.52 -9.6463 -0.11853 2.101 -0.10145
1 -3.12 -1.7981 10.7 -6.171 4.4527 10.967 9.073 6.2056 -6.1199 -2.0402
2 9.5689 5.6721 -8.4832 -1.2249 2.1871 -3.0264 -2.391 -5.3308 -3.2847 -4.0382
3 3.6268 4.2759 -1.7007 1.5002 5.5266 1.8716 -12.063 0.26314 2.7645 2.4929
4 -11.683 -7.7068 2.1102 2.214 7.2202 0.69799 3.2173 -5.382 -2.0838 5.0314
5 -4.3024 8.0241 2.0714 -1.0174 -0.28369 1.7622 7.8797 -1.7795 6.7541 5.6703
6 8.3574 -5.225 8.6529 8.5605 -8.9465 3.767 -5.4636 -1.4635 -0.98947 -0.58025
7 -10.01 3.3894 -4.4487 1.1669 -11.904 6.5158 4.3681 0.79913 -6.9131 -8.687
8 -5.4576 7.1019 -8.8259 1.7189 4.955 -8.9157 -3.8905 -0.60086 -2.1233 5.892
9 8.0678 -4.4142 3.6236 4.5889 -2.7611 2.4455 0.67096 -4.2822 2.0875 4.6274
This table can be imported into a spaCy pipeline using spacy init vectors in
spaCy v3.2+ with the option --mode floret:
spacy init vectors LANG vectors.floret spacy_vectors_dir --mode floretThe fastText and floret binary formats (.bin) are not compatible, so it's
important to load a .bin file with the same program used to train it.
See the fastText documentation for details about all
other commands and options. floret supports all existing fasttext commands
and does not modify any fasttext defaults.
The original fastText README is provided below for reference.
fastText is a library for efficient learning of word representations and sentence classification.
- Recent state-of-the-art English word vectors.
- Word vectors for 157 languages trained on Wikipedia and Crawl.
- Models for language identification and various supervised tasks.
- The preprocessed YFCC100M data used in [2].
You can find answers to frequently asked questions on our website.
We also provide a cheatsheet full of useful one-liners.
We are continuously building and testing our library, CLI and Python bindings under various docker images using circleci.
Generally, fastText builds on modern Mac OS and Linux distributions. Since it uses some C++11 features, it requires a compiler with good C++11 support. These include :
- (g++-4.7.2 or newer) or (clang-3.3 or newer)
Compilation is carried out using a Makefile, so you will need to have a working make. If you want to use cmake you need at least version 2.8.9.
One of the oldest distributions we successfully built and tested the CLI under is Debian jessie.
For the word-similarity evaluation script you will need:
- Python 2.6 or newer
- NumPy & SciPy
For the python bindings (see the subdirectory python) you will need:
- Python version 2.7 or >=3.4
- NumPy & SciPy
- pybind11
One of the oldest distributions we successfully built and tested the Python bindings under is Debian jessie.
If these requirements make it impossible for you to use fastText, please open an issue and we will try to accommodate you.
We discuss building the latest stable version of fastText.
You can find our latest stable release in the usual place.
There is also the master branch that contains all of our most recent work, but comes along with all the usual caveats of an unstable branch. You might want to use this if you are a developer or power-user.
$ wget https://github.com/facebookresearch/fastText/archive/v0.9.2.zip
$ unzip v0.9.2.zip
$ cd fastText-0.9.2
$ 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).
For now this is not part of a release, so you will need to clone the master branch.
$ git clone https://github.com/facebookresearch/fastText.git
$ cd fastText
$ mkdir build && cd build && cmake ..
$ make && make install
This will create the fasttext binary and also all relevant libraries (shared, static, PIC).
For now this is not part of a release, so you will need to clone the master branch.
$ git clone https://github.com/facebookresearch/fastText.git
$ cd fastText
$ pip install .
For further information and introduction see python/README.md
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
or use predict-prob to also get the probability for each label
$ ./fasttext predict-prob 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 occurrences [1]
-minCountLabel minimal number of label occurrences [0]
-wordNgrams max length of word ngram [1]
-bucket number of buckets [2000000]
-minn min length of char ngram [0]
-maxn max length of char ngram [0]
-t sampling threshold [0.0001]
-label labels prefix [__label__]
The following arguments for training are optional:
-lr learning rate [0.1]
-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} [softmax]
-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{bojanowski2017enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={Transactions of the Association for Computational Linguistics},
volume={5},
year={2017},
issn={2307-387X},
pages={135--146}
}
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification
@InProceedings{joulin2017bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
booktitle={Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
month={April},
year={2017},
publisher={Association for Computational Linguistics},
pages={427--431},
}
[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.)