explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-11-12 04:38:28 +03:00
Code Issues Packages Projects Releases Wiki Activity

* Update conll_train script

Browse Source
This commit is contained in:
Matthew Honnibal 2016-02-24 18:19:38 +01:00
parent fab538672e
commit 77f2b218f9
1 changed files with 8 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
bin/tagger/conll_train.py
View File

@ -87,15 +87,15 @@ def _parse_line(line):
def score_model(nlp, gold_tuples, verbose=False):
scorer = Scorer()
correct = 0.0
total = 0.0
for words, gold_tags in gold_tuples:
tokens = nlp.tokenizer.tokens_from_list(words)
nlp.tagger(tokens)
for token, gold in zip(tokens, gold_tags):
scorer.tags.tp += token.tag_ == gold
scorer.tags.fp += token.tag_ != gold
scorer.tags.fn += token.tag_ != gold
return scorer.tags_acc
correct += token.tag_ == gold
total += 1
return (correct / total) * 100
def train(Language, train_sents, dev_sents, model_dir, n_iter=15, seed=0,
@ -116,8 +116,6 @@ def train(Language, train_sents, dev_sents, model_dir, n_iter=15, seed=0,
random.shuffle(train_sents)
heldout_sents = train_sents[:int(nr_train * 0.1)]
train_sents = train_sents[len(heldout_sents):]
#train_sents = train_sents[:500]
#assert len(heldout_sents) < len(train_sents)
prev_score = 0.0
variance = 0.001
last_good_learn_rate = nlp.tagger.model.eta
@ -130,15 +128,15 @@ def train(Language, train_sents, dev_sents, model_dir, n_iter=15, seed=0,
acc += nlp.tagger.train(tokens, gold_tags)
total += len(tokens)
n += 1
if n and n % 10000 == 0:
if n and n % 20000 == 0:
dev_score = score_model(nlp, heldout_sents)
eval_score = score_model(nlp, dev_sents)
if dev_score > prev_score:
if dev_score >= prev_score:
nlp.tagger.model.keep_update()
prev_score = dev_score
variance = 0.001
last_good_learn_rate = nlp.tagger.model.eta
nlp.tagger.model.eta *= 1.05
nlp.tagger.model.eta *= 1.01
print('%d:\t%.3f\t%.3f\t%.3f\t%.4f' % (n, acc/total, dev_score, eval_score, nlp.tagger.model.eta))
else:
nlp.tagger.model.backtrack()