mirror of https://github.com/explosion/spaCy.git
* Fix train.py
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@ -37,17 +37,12 @@ def add_noise(c, noise_level):
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return c.lower()
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def score_model(scorer, nlp, raw_text, annot_tuples, train_tags=None):
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def score_model(scorer, nlp, raw_text, annot_tuples):
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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else:
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tokens = nlp.tokenizer(raw_text)
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if train_tags is not None:
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key = hash(tokens.string)
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nlp.tagger.tag_from_strings(tokens, train_tags[key])
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else:
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nlp.tagger(tokens)
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nlp.entity(tokens)
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nlp.parser(tokens)
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gold = GoldParse(tokens, annot_tuples)
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@ -93,7 +88,7 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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beam_width=beam_width)
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Config.write(ner_model_dir, 'config', features='ner', seed=seed,
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labels=Language.EntityTransitionSystem.get_labels(gold_tuples),
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beam_width=1)
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beam_width=0)
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if n_sents > 0:
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gold_tuples = gold_tuples[:n_sents]
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@ -112,7 +107,7 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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for annot_tuples, ctnt in sents:
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if len(annot_tuples[1]) == 1:
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continue
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score_model(scorer, nlp, raw_text, annot_tuples, train_tags)
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score_model(scorer, nlp, raw_text, annot_tuples)
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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else:
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@ -188,7 +183,7 @@ def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbos
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feat_set='basic' if not debug else 'debug',
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gold_preproc=gold_preproc, n_sents=n_sents,
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corruption_level=corruption_level, n_iter=n_iter,
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train_tags=taggings, beam_width=beam_width)
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beam_width=beam_width)
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if out_loc:
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write_parses(English, dev_loc, model_dir, out_loc)
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scorer = evaluate(English, list(read_json_file(dev_loc)),
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