mirror of
https://github.com/explosion/spaCy.git
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82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
#!/usr/bin/env python
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from __future__ import print_function, unicode_literals, division
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import logging
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from pathlib import Path
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from collections import defaultdict
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from gensim.models import Word2Vec
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import plac
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import spacy
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logger = logging.getLogger(__name__)
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class Corpus(object):
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def __init__(self, directory, nlp):
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self.directory = directory
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self.nlp = nlp
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def __iter__(self):
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for text_loc in iter_dir(self.directory):
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with text_loc.open("r", encoding="utf-8") as file_:
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text = file_.read()
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# This is to keep the input to the blank model (which doesn't
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# sentencize) from being too long. It works particularly well with
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# the output of [WikiExtractor](https://github.com/attardi/wikiextractor)
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paragraphs = text.split('\n\n')
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for par in paragraphs:
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yield [word.orth_ for word in self.nlp(par)]
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def iter_dir(loc):
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dir_path = Path(loc)
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for fn_path in dir_path.iterdir():
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if fn_path.is_dir():
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for sub_path in fn_path.iterdir():
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yield sub_path
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else:
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yield fn_path
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@plac.annotations(
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lang=("ISO language code"),
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in_dir=("Location of input directory"),
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out_loc=("Location of output file"),
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n_workers=("Number of workers", "option", "n", int),
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size=("Dimension of the word vectors", "option", "d", int),
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window=("Context window size", "option", "w", int),
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min_count=("Min count", "option", "m", int),
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negative=("Number of negative samples", "option", "g", int),
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nr_iter=("Number of iterations", "option", "i", int),
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)
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def main(
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lang,
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in_dir,
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out_loc,
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negative=5,
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n_workers=4,
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window=5,
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size=128,
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min_count=10,
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nr_iter=5,
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):
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logging.basicConfig(
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format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO
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)
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nlp = spacy.blank(lang)
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corpus = Corpus(in_dir, nlp)
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model = Word2Vec(
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sentences=corpus,
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size=size,
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window=window,
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min_count=min_count,
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workers=n_workers,
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sample=1e-5,
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negative=negative,
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)
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model.save(out_loc)
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if __name__ == "__main__":
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plac.call(main)
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