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76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
from __future__ import unicode_literals, print_function
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import json
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import pathlib
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import random
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import spacy
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from spacy.pipeline import DependencyParser
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from spacy.gold import GoldParse
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from spacy.tokens import Doc
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def train_parser(nlp, train_data, left_labels, right_labels):
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parser = DependencyParser(
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nlp.vocab,
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left_labels=left_labels,
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right_labels=right_labels)
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for itn in range(1000):
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random.shuffle(train_data)
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loss = 0
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for words, heads, deps in train_data:
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doc = Doc(nlp.vocab, words=words)
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gold = GoldParse(doc, heads=heads, deps=deps)
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loss += parser.update(doc, gold)
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parser.model.end_training()
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return parser
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def main(model_dir=None):
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if model_dir is not None:
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model_dir = pathlib.Path(model_dir)
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if not model_dir.exists():
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model_dir.mkdir()
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assert model_dir.is_dir()
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nlp = spacy.load('en', tagger=False, parser=False, entity=False, add_vectors=False)
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train_data = [
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(
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['They', 'trade', 'mortgage', '-', 'backed', 'securities', '.'],
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[1, 1, 4, 4, 5, 1, 1],
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['nsubj', 'ROOT', 'compound', 'punct', 'nmod', 'dobj', 'punct']
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),
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(
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['I', 'like', 'London', 'and', 'Berlin', '.'],
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[1, 1, 1, 2, 2, 1],
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['nsubj', 'ROOT', 'dobj', 'cc', 'conj', 'punct']
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)
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]
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left_labels = set()
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right_labels = set()
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for _, heads, deps in train_data:
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for i, (head, dep) in enumerate(zip(heads, deps)):
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if i < head:
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left_labels.add(dep)
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elif i > head:
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right_labels.add(dep)
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parser = train_parser(nlp, train_data, sorted(left_labels), sorted(right_labels))
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doc = Doc(nlp.vocab, words=['I', 'like', 'securities', '.'])
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parser(doc)
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for word in doc:
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print(word.text, word.dep_, word.head.text)
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if model_dir is not None:
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with (model_dir / 'config.json').open('w') as file_:
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json.dump(parser.cfg, file_)
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parser.model.dump(str(model_dir / 'model'))
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if __name__ == '__main__':
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main()
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# I nsubj like
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# like ROOT like
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# securities dobj like
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# . cc securities
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