38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
#!/usr/bin/env python
|
|
# -*- coding: utf-8 -*-
|
|
#
|
|
# Author: Jayant Jain <jayantjain1992@gmail.com>
|
|
# Copyright (C) 2017 Radim Rehurek <me@radimrehurek.com>
|
|
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
|
|
|
|
|
|
"""
|
|
Warnings
|
|
--------
|
|
.. deprecated:: 3.2.0
|
|
Use :mod:`gensim.models.fasttext` instead.
|
|
|
|
|
|
|
|
Python wrapper around word representation learning from FastText, a library for efficient learning
|
|
of word representations and sentence classification [1].
|
|
|
|
This module allows training a word embedding from a training corpus with the additional ability
|
|
to obtain word vectors for out-of-vocabulary words, using the fastText C implementation.
|
|
|
|
The wrapped model can NOT be updated with new documents for online training -- use gensim's
|
|
`Word2Vec` for that.
|
|
|
|
Example:
|
|
|
|
>>> from gensim.models.wrappers import FastText
|
|
>>> model = FastText.train('/Users/kofola/fastText/fasttext', corpus_file='text8')
|
|
>>> print model['forests'] # prints vector for given out-of-vocabulary word
|
|
|
|
.. [1] https://github.com/facebookresearch/fastText#enriching-word-vectors-with-subword-information
|
|
|
|
|
|
|
|
"""
|
|
from gensim.models.deprecated.fasttext_wrapper import FastText, FastTextKeyedVectors # noqa:F401
|
|
from gensim.models.deprecated.fasttext_wrapper import ft_hash, compute_ngrams # noqa:F401
|