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* Ensure better separation between score printing and training in train.py
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@ -218,6 +218,11 @@ def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
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for itn in range(n_iter):
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scorer = Scorer()
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for raw_text, segmented_text, annot_tuples in gold_tuples:
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# Eval before train
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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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if gold_preproc:
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sents = [nlp.tokenizer.tokens_from_list(s) for s in segmented_text]
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else:
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@ -229,15 +234,11 @@ def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
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nlp.entity.train(tokens, gold, force_gold=force_gold)
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nlp.tagger.train(tokens, gold.tags)
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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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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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nlp.parser.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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print nlp.vocab.strings['NMOD']
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def evaluate(Language, dev_loc, model_dir, gold_preproc=False, verbose=True):
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@ -274,13 +275,16 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
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out_loc=("Out location", "option", "o", str),
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n_sents=("Number of training sentences", "option", "n", int),
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verbose=("Verbose error reporting", "flag", "v", bool),
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debug=("Debug mode", "flag", "d", bool)
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)
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def main(train_loc, dev_loc, model_dir, n_sents=0, out_loc="", verbose=False):
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train(English, train_loc, model_dir,
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def main(train_loc, dev_loc, model_dir, n_sents=0, out_loc="", verbose=False,
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debug=False):
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train(English, train_loc, model_dir, feat_set='basic' if not debug else 'debug',
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gold_preproc=False, force_gold=False, n_sents=n_sents)
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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, dev_loc, model_dir, gold_preproc=False, verbose=verbose)
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print 'TOK', scorer.mistokened
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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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