mirror of https://github.com/explosion/spaCy.git
* Use print function in train.py, for py 2/3 compatibility
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@ -1,6 +1,7 @@
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#!/usr/bin/env python
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from __future__ import division
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from __future__ import unicode_literals
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from __future__ import print_function
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import os
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from os import path
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@ -107,7 +108,7 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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nlp = Language(data_dir=model_dir)
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print "Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %"
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print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
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for itn in range(n_iter):
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scorer = Scorer()
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loss = 0
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@ -138,9 +139,9 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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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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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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scorer.token_acc)
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scorer.token_acc))
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nlp.end_training()
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def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
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@ -219,14 +220,14 @@ def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbos
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# write_parses(English, dev_loc, model_dir, out_loc, beam_width=beam_width)
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scorer = evaluate(English, list(read_json_file(dev_loc)),
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model_dir, gold_preproc=gold_preproc, verbose=verbose)
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print 'TOK', scorer.token_acc
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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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print('TOK', scorer.token_acc)
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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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print 'NER P', scorer.ents_p
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print 'NER R', scorer.ents_r
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print 'NER F', scorer.ents_f
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print('NER P', scorer.ents_p)
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print('NER R', scorer.ents_r)
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print('NER F', scorer.ents_f)
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if __name__ == '__main__':
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