fastText <https://fasttext.cc/>__ is a library for efficient learning
of word representations and sentence classification.
In this document we present how to use fastText in python.
Requirements <#requirements>__Installation <#installation>__Usage overview <#usage-overview>__Word representation model <#word-representation-model>__Text classification model <#text-classification-model>__IMPORTANT: Preprocessing data / encoding conventions <#important-preprocessing-data-encoding-conventions>__More examples <#more-examples>__API <#api>__train_unsupervised parameters <#train_unsupervised-parameters>__train_supervised parameters <#train_supervised-parameters>__model object <#model-object>__fastText <https://fasttext.cc/>__ builds on modern Mac OS and Linux
distributions. Since it uses C++11 features, it requires a compiler with
good C++11 support. You will need Python <https://www.python.org/>__
(version 2.7 or ≥ 3.4), NumPy <http://www.numpy.org/>__ &
SciPy <https://www.scipy.org/>__ and
pybind11 <https://github.com/pybind/pybind11>__.
To install the latest release, you can do :
.. code:: bash
$ pip install fasttext
or, to get the latest development version of fasttext, you can install from our github repository :
.. code:: bash
$ git clone https://github.com/facebookresearch/fastText.git
$ cd fastText
$ sudo pip install .
$ # or :
$ sudo python setup.py install
In order to learn word vectors, as described here <https://fasttext.cc/docs/en/references.html#enriching-word-vectors-with-subword-information>__,
we can use fasttext.train_unsupervised function like this:
.. code:: py
import fasttext
# Skipgram model :
model = fasttext.train_unsupervised('data.txt', model='skipgram')
# or, cbow model :
model = fasttext.train_unsupervised('data.txt', model='cbow')
where data.txt is a training file containing utf-8 encoded text.
The returned model object represents your learned model, and you can
use it to retrieve information.
.. code:: py
print(model.words) # list of words in dictionary
print(model['king']) # get the vector of the word 'king'
Saving and loading a model object
You can save your trained model object by calling the function
``save_model``.
.. code:: py
model.save_model("model_filename.bin")
and retrieve it later thanks to the function ``load_model`` :
.. code:: py
model = fasttext.load_model("model_filename.bin")
For more information about word representation usage of fasttext, you
can refer to our `word representations
tutorial <https://fasttext.cc/docs/en/unsupervised-tutorial.html>`__.
Text classification model
-------------------------
In order to train a text classifier using the method `described
here <https://fasttext.cc/docs/en/references.html#bag-of-tricks-for-efficient-text-classification>`__,
we can use ``fasttext.train_supervised`` function like this:
.. code:: py
import fasttext
model = fasttext.train_supervised('data.train.txt')
where ``data.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__``
Once the model is trained, we can retrieve the list of words and labels:
.. code:: py
print(model.words)
print(model.labels)
To evaluate our model by computing the precision at 1 (P@1) and the
recall on a test set, we use the ``test`` function:
.. code:: py
def print_results(N, p, r):
print("N\t" + str(N))
print("P@{}\t{:.3f}".format(1, p))
print("R@{}\t{:.3f}".format(1, r))
print_results(*model.test('test.txt'))
We can also predict labels for a specific text :
.. code:: py
model.predict("Which baking dish is best to bake a banana bread ?")
By default, ``predict`` returns only one label : the one with the
highest probability. You can also predict more than one label by
specifying the parameter ``k``:
.. code:: py
model.predict("Which baking dish is best to bake a banana bread ?", k=3)
If you want to predict more than one sentence you can pass an array of
strings :
.. code:: py
model.predict(["Which baking dish is best to bake a banana bread ?", "Why not put knives in the dishwasher?"], k=3)
Of course, you can also save and load a model to/from a file as `in the
word representation usage <#saving-and-loading-a-model-object>`__.
For more information about text classification usage of fasttext, you
can refer to our `text classification
tutorial <https://fasttext.cc/docs/en/supervised-tutorial.html>`__.
Compress model files with quantization
When you want to save a supervised model file, fastText can compress it in order to have a much smaller model file by sacrificing only a little bit performance.
.. code:: py
# with the previously trained `model` object, call :
model.quantize(input='data.train.txt', retrain=True)
# then display results and save the new model :
print_results(*model.test(valid_data))
model.save_model("model_filename.ftz")
model_filename.ftz will have a much smaller size than
model_filename.bin.
For further reading on quantization, you can refer to this paragraph from our blog post <https://fasttext.cc/blog/2017/10/02/blog-post.html#model-compression>__.
In general it is important to properly preprocess your data. In
particular our example scripts in the root folder <https://github.com/facebookresearch/fastText>__ do this.
fastText assumes UTF-8 encoded text. All text must be unicode for Python2 <https://docs.python.org/2/library/functions.html#unicode>__
and str for Python3 <https://docs.python.org/3.5/library/stdtypes.html#textseq>.
The passed text will be encoded as UTF-8 by pybind11 <https://pybind11.readthedocs.io/en/master/advanced/cast/strings.html?highlight=utf-8#strings-bytes-and-unicode-conversions>
before passed to the fastText C++ library. This means it is important to
use UTF-8 encoded text when building a model. On Unix-like systems you
can convert text using iconv <https://en.wikipedia.org/wiki/Iconv>__.
fastText will tokenize (split text into pieces) based on the following ASCII characters (bytes). In particular, it is not aware of UTF-8 whitespace. We advice the user to convert UTF-8 whitespace / word boundaries into one of the following symbols as appropiate.
The newline character is used to delimit lines of text. In particular,
the EOS token is appended to a line of text if a newline character is
encountered. The only exception is if the number of tokens exceeds the
MAX_LINE_SIZE constant as defined in the Dictionary header <https://github.com/facebookresearch/fastText/blob/master/src/dictionary.h>.
This means if you have text that is not separate by newlines, such as
the fil9 dataset <http://mattmahoney.net/dc/textdata>, it will be
broken into chunks with MAX_LINE_SIZE of tokens and the EOS token is
not appended.
The length of a token is the number of UTF-8 characters by considering
the leading two bits of a byte <https://en.wikipedia.org/wiki/UTF-8#Description>__ to identify
subsequent bytes of a multi-byte sequence <https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc>.
Knowing this is especially important when choosing the minimum and
maximum length of subwords. Further, the EOS token (as specified in the
Dictionary header <https://github.com/facebookresearch/fastText/blob/master/src/dictionary.h>)
is considered a character and will not be broken into subwords.
In order to have a better knowledge of fastText models, please consider
the main
README <https://github.com/facebookresearch/fastText/blob/master/README.md>__
and in particular the tutorials on our website <https://fasttext.cc/docs/en/supervised-tutorial.html>__.
You can find further python examples in the doc folder <https://github.com/facebookresearch/fastText/tree/master/python/doc/examples>__.
As with any package you can get help on any Python function using the help function.
For example
::
+>>> import fasttext
+>>> help(fasttext.FastText)
Help on module fasttext.FastText in fasttext:
NAME
fasttext.FastText
DESCRIPTION
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
FUNCTIONS
load_model(path)
Load a model given a filepath and return a model object.
tokenize(text)
Given a string of text, tokenize it and return a list of tokens
[...]
train_unsupervised parameters.. code:: python
input # training file path (required)
model # unsupervised fasttext model {cbow, skipgram} [skipgram]
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 [5]
minn # min length of char ngram [3]
maxn # max length of char ngram [6]
neg # number of negatives sampled [5]
wordNgrams # max length of word ngram [1]
loss # loss function {ns, hs, softmax, ova} [ns]
bucket # number of buckets [2000000]
thread # number of threads [number of cpus]
lrUpdateRate # change the rate of updates for the learning rate [100]
t # sampling threshold [0.0001]
verbose # verbose [2]
train_supervised parameters.. code:: python
input # training file path (required)
lr # learning rate [0.1]
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]
minCountLabel # minimal number of label occurences [1]
minn # min length of char ngram [0]
maxn # max length of char ngram [0]
neg # number of negatives sampled [5]
wordNgrams # max length of word ngram [1]
loss # loss function {ns, hs, softmax, ova} [softmax]
bucket # number of buckets [2000000]
thread # number of threads [number of cpus]
lrUpdateRate # change the rate of updates for the learning rate [100]
t # sampling threshold [0.0001]
label # label prefix ['__label__']
verbose # verbose [2]
pretrainedVectors # pretrained word vectors (.vec file) for supervised learning []
model objecttrain_supervised, train_unsupervised and load_model
functions return an instance of _FastText class, that we generaly
name model object.
This object exposes those training arguments as properties : lr,
dim, ws, epoch, minCount, minCountLabel, minn,
maxn, neg, wordNgrams, loss, bucket, thread,
lrUpdateRate, t, label, verbose, pretrainedVectors.
So model.wordNgrams will give you the max length of word ngram used
for training this model.
In addition, the object exposes several functions :
.. code:: python
get_dimension # Get the dimension (size) of a lookup vector (hidden layer).
# This is equivalent to `dim` property.
get_input_vector # Given an index, get the corresponding vector of the Input Matrix.
get_input_matrix # Get a copy of the full input matrix of a Model.
get_labels # Get the entire list of labels of the dictionary
# This is equivalent to `labels` property.
get_line # Split a line of text into words and labels.
get_output_matrix # Get a copy of the full output matrix of a Model.
get_sentence_vector # Given a string, get a single vector represenation. This function
# assumes to be given a single line of text. We split words on
# whitespace (space, newline, tab, vertical tab) and the control
# characters carriage return, formfeed and the null character.
get_subword_id # Given a subword, return the index (within input matrix) it hashes to.
get_subwords # Given a word, get the subwords and their indicies.
get_word_id # Given a word, get the word id within the dictionary.
get_word_vector # Get the vector representation of word.
get_words # Get the entire list of words of the dictionary
# This is equivalent to `words` property.
is_quantized # whether the model has been quantized
predict # Given a string, get a list of labels and a list of corresponding probabilities.
quantize # Quantize the model reducing the size of the model and it's memory footprint.
save_model # Save the model to the given path
test # Evaluate supervised model using file given by path
test_label # Return the precision and recall score for each label.
The properties words, labels return the words and labels from
the dictionary :
.. code:: py
model.words # equivalent to model.get_words()
model.labels # equivalent to model.get_labels()
The object overrides __getitem__ and __contains__ functions in
order to return the representation of a word and to check if a word is
in the vocabulary.
.. code:: py
model['king'] # equivalent to model.get_word_vector('king')
'king' in model # equivalent to `'king' in model.get_words()`
Facebook page <https://www.facebook.com/groups/1174547215919768>__Stack overflow <https://stackoverflow.com/questions/tagged/fasttext>__Google group <https://groups.google.com/forum/#!forum/fasttext-library>__GitHub <https://github.com/facebookresearch/fastText>__.. |CircleCI| image:: https://circleci.com/gh/facebookresearch/fastText/tree/master.svg?style=svg :target: https://circleci.com/gh/facebookresearch/fastText/tree/master
FAQs
fasttext Python bindings
We found that fasttext demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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