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36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Load vectors for a language trained using fastText
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https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md
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Compatible with: spaCy v2.0.0a17+
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"""
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from __future__ import unicode_literals
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import plac
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import numpy
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from spacy.language import Language
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@plac.annotations(
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vectors_loc=("Path to vectors", "positional", None, str))
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def main(vectors_loc):
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nlp = Language() # start off with a blank Language class
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with open(vectors_loc, 'rb') as file_:
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header = file_.readline()
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nr_row, nr_dim = header.split()
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nlp.vocab.clear_vectors(int(nr_dim))
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for line in file_:
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line = line.decode('utf8')
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pieces = line.split()
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word = pieces[0]
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vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
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nlp.vocab.set_vector(word, vector) # add the vectors to the vocab
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# test the vectors and similarity
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text = 'class colspan'
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doc = nlp(text)
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print(text, doc[0].similarity(doc[1]))
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if __name__ == '__main__':
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plac.call(main)
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