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50 lines
1.5 KiB
Python
50 lines
1.5 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 v2.0.0+
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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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import spacy
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from spacy.language import Language
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@plac.annotations(
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vectors_loc=("Path to .vec file", "positional", None, str),
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lang=(
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"Optional language ID. If not set, blank Language() will be used.",
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"positional",
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None,
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str,
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),
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)
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def main(vectors_loc, lang=None):
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if lang is None:
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nlp = Language()
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else:
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# create empty language class – this is required if you're planning to
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# save the model to disk and load it back later (models always need a
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# "lang" setting). Use 'xx' for blank multi-language class.
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nlp = spacy.blank(lang)
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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.reset_vectors(width=int(nr_dim))
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for line in file_:
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line = line.rstrip().decode("utf8")
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pieces = line.rsplit(" ", int(nr_dim))
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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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