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* Update train.py, to support paragraphs where there's no raw_text
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@ -39,6 +39,18 @@ 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):
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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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nlp.tagger(tokens)
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nlp.entity(tokens)
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nlp.parser(tokens)
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else:
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tokens = nlp(raw_text, merge_mwes=False)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=False)
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def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0,
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gold_preproc=False, n_sents=0, corruption_level=0):
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dep_model_dir = path.join(model_dir, 'deps')
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@ -70,23 +82,20 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
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scorer = Scorer()
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loss = 0
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for raw_text, annot_tuples, ctnt in gold_tuples:
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if corruption_level != 0:
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raw_text = ''.join(add_noise(c, corruption_level) for c in raw_text)
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tokens = nlp(raw_text, merge_mwes=False)
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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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tokens = nlp.tokenizer(raw_text)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=False)
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assert not gold_preproc
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sents = [nlp.tokenizer(raw_text)]
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for tokens in sents:
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gold = GoldParse(tokens, annot_tuples)
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nlp.tagger(tokens)
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try:
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loss += nlp.parser.train(tokens, gold)
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except AssertionError:
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# TODO: Do something about non-projective sentences
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pass
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nlp.entity.train(tokens, gold)
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nlp.tagger.train(tokens, gold.tags)
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nlp.tagger(tokens)
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try:
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loss += nlp.parser.train(tokens, gold)
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except AssertionError:
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# TODO: Do something about non-projective sentences
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pass
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nlp.entity.train(tokens, gold)
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nlp.tagger.train(tokens, gold.tags)
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random.shuffle(gold_tuples)
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print '%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
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scorer.tags_acc,
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@ -135,13 +144,16 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
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)
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def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
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debug=False, corruption_level=0.0):
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train(English, read_json_file(train_loc), model_dir,
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print 'reading gold'
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gold_train = list(read_json_file(train_loc))
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print 'done'
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train(English, gold_train, model_dir,
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feat_set='basic' if not debug else 'debug',
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gold_preproc=False, n_sents=n_sents,
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corruption_level=corruption_level, n_iter=n_iter)
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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, read_json_file(dev_loc),
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scorer = evaluate(English, list(read_json_file(dev_loc)),
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model_dir, gold_preproc=False, verbose=verbose)
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print 'TOK', 100-scorer.token_acc
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print 'POS', scorer.tags_acc
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