models.wrappers.fasttext – Wrapper for FastText implementation from Facebook¶Warning
Deprecated since version 3.2.0: Use 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
