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Print dev score before averaging
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@ -172,10 +172,14 @@ def train(Language, gold_tuples, model_dir, dev_loc, n_iter=15, feat_set=u'basic
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except KeyboardInterrupt:
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print("Saving model...")
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break
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dev_uas = score_file(nlp, dev_loc).uas
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print("Dev before average", dev_uas)
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nlp.parser.model.end_training()
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print("Saved. Evaluating...")
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return nlp
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def _train_epoch(nlp, gold_tuples, eg_seen, itn, dev_loc, micro_eval):
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random.shuffle(gold_tuples)
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loss = 0
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