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