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* Restore shuffling, and remove print statements from train.py
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@ -57,9 +57,6 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
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for raw_text, segmented_text, annot_tuples, ctnt in gold_tuples:
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for raw_text, segmented_text, annot_tuples, ctnt in gold_tuples:
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# Eval before train
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# Eval before train
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tokens = nlp(raw_text, merge_mwes=False)
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tokens = nlp(raw_text, merge_mwes=False)
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#print segmented_text
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#for annot in zip(*annot_tuples):
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# print annot
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gold = GoldParse(tokens, annot_tuples)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=False)
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scorer.score(tokens, gold, verbose=False)
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@ -76,7 +73,7 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
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nlp.tagger.train(tokens, gold.tags)
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nlp.tagger.train(tokens, gold.tags)
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print '%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.ents_f, scorer.tags_acc)
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print '%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.ents_f, scorer.tags_acc)
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#random.shuffle(gold_tuples)
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random.shuffle(gold_tuples)
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nlp.parser.model.end_training()
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nlp.parser.model.end_training()
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nlp.entity.model.end_training()
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nlp.entity.model.end_training()
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nlp.tagger.model.end_training()
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nlp.tagger.model.end_training()
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