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* Add verbose flag for Scorer, for debugging, and fix ent_strings bug
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@ -215,7 +215,8 @@ def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
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nlp = Language()
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ent_strings = [None] * (max(nlp.entity.moves.label_ids.values()) + 1)
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for label, i in nlp.entity.moves.label_ids.items():
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ent_strings[i] = label
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if i >= 0:
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ent_strings[i] = label
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print "Itn.\tUAS\tNER F.\tTag %"
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for itn in range(n_iter):
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@ -243,8 +244,7 @@ def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
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nlp.tagger.model.end_training()
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def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
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global loss
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def evaluate(Language, dev_loc, model_dir, gold_preproc=False, verbose=False):
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assert not gold_preproc
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nlp = Language()
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gold_tuples = read_docparse_file(dev_loc)
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@ -252,11 +252,10 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
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for raw_text, segmented_text, annot_tuples in gold_tuples:
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tokens = nlp(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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scorer.score(tokens, gold, verbose=verbose)
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return scorer
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@plac.annotations(
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train_loc=("Training file location",),
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dev_loc=("Dev. file location",),
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@ -266,7 +265,7 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
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def main(train_loc, dev_loc, model_dir, n_sents=0):
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train(English, train_loc, model_dir,
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gold_preproc=False, force_gold=False, n_sents=n_sents)
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scorer = evaluate(English, dev_loc, model_dir, gold_preproc=False)
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scorer = evaluate(English, dev_loc, model_dir, gold_preproc=False, verbose=False)
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print 'POS', scorer.tags_acc
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print 'UAS', scorer.uas
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print 'LAS', scorer.las
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