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Update train_parser example
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@ -18,7 +18,7 @@ def train_parser(nlp, train_data, left_labels, right_labels):
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random.shuffle(train_data)
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random.shuffle(train_data)
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loss = 0
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loss = 0
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for words, heads, deps in train_data:
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for words, heads, deps in train_data:
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doc = nlp.make_doc(words)
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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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gold = GoldParse(doc, heads=heads, deps=deps)
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loss += parser.update(doc, gold)
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loss += parser.update(doc, gold)
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parser.model.end_training()
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parser.model.end_training()
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@ -33,7 +33,6 @@ def main(model_dir=None):
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assert model_dir.isdir()
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assert model_dir.isdir()
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nlp = spacy.load('en', tagger=False, parser=False, entity=False, vectors=False)
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nlp = spacy.load('en', tagger=False, parser=False, entity=False, vectors=False)
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nlp.make_doc = lambda words: Doc(nlp.vocab, zip(words, [True]*len(words)))
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train_data = [
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train_data = [
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(
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(
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@ -57,7 +56,7 @@ def main(model_dir=None):
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right_labels.add(dep)
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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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parser = train_parser(nlp, train_data, sorted(left_labels), sorted(right_labels))
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doc = nlp.make_doc(['I', 'like', 'securities', '.'])
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doc = Doc(nlp.vocab, words=['I', 'like', 'securities', '.'])
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parser(doc)
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parser(doc)
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for word in 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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print(word.text, word.dep_, word.head.text)
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