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7d5afad Nov 17, 2017
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@ines @honnibal @yogendrasoni
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#!/usr/bin/env python
# coding: utf8
"""Load vectors for a language trained using fastText
https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md
Compatible with: spaCy v2.0.0+
"""
from __future__ import unicode_literals
import plac
import numpy
import spacy
from spacy.language import Language
@plac.annotations(
vectors_loc=("Path to .vec file", "positional", None, str),
lang=("Optional language ID. If not set, blank Language() will be used.",
"positional", None, str))
def main(vectors_loc, lang=None):
if lang is None:
nlp = Language()
else:
# create empty language class – this is required if you're planning to
# save the model to disk and load it back later (models always need a
# "lang" setting). Use 'xx' for blank multi-language class.
nlp = spacy.blank(lang)
with open(vectors_loc, 'rb') as file_:
header = file_.readline()
nr_row, nr_dim = header.split()
nlp.vocab.reset_vectors(width=int(nr_dim))
for line in file_:
line = line.rstrip().decode('utf8')
pieces = line.rsplit(' ', int(nr_dim))
word = pieces[0]
vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
nlp.vocab.set_vector(word, vector) # add the vectors to the vocab
# test the vectors and similarity
text = 'class colspan'
doc = nlp(text)
print(text, doc[0].similarity(doc[1]))
if __name__ == '__main__':
plac.call(main)