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Tmp. Working on NN NER.

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This commit is contained in:
Matthew Honnibal 2016-09-08 13:00:24 +02:00
parent b3b180010b
commit 8be231d490
1 changed files with 14 additions and 7 deletions
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21
bin/parser/train.py
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@ -91,7 +91,7 @@ def train(Language, gold_tuples, model_dir, dev_loc, n_iter=15, feat_set=u'basic
Config.write(dep_model_dir, 'config', feat_set=feat_set, seed=seed,
labels=ArcEager.get_labels(gold_tuples),
rho=0.0, eta=1.0, mu=0.9, noise=0.0,
rho=1e-5, eta=1.0, mu=0.9, noise=0.0,
beam_width=beam_width,projectivize=pseudoprojective)
#feat_set, slots = get_templates('neural')
#vector_widths = [10, 10, 10]
@ -112,15 +112,19 @@ def train(Language, gold_tuples, model_dir, dev_loc, n_iter=15, feat_set=u'basic
Config.write(ner_model_dir, 'config', feat_set='ner', seed=seed,
labels=BiluoPushDown.get_labels(gold_tuples),
beam_width=0, rho=0.0, eta=1.0, mu=0.9, noise=0.0)
beam_width=beam_width, rho=1e-8, eta=1.0, mu=0.9, noise=0.0)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
micro_eval = gold_tuples[:50]
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = Parser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
if beam_width >= 2:
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = BeamParser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
else:
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = Parser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
print(nlp.parser.model.widths)
for raw_text, sents in gold_tuples:
for annot_tuples, ctnt in sents:
@ -178,7 +182,7 @@ def _train_epoch(nlp, gold_tuples, eg_seen, itn, dev_loc, micro_eval,
nlp.parser.model.nr_active_feat,
nlp.entity.model.nr_active_feat))
loss = 0
nlp.parser.model.learn_rate *= 0.99
#nlp.parser.model.learn_rate *= 0.99
scorer = score_file(nlp, dev_loc)
print('D:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (loss, scorer.uas, scorer.ents_f,
scorer.tags_acc, scorer.token_acc))
@ -271,6 +275,7 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
train_loc=("Location of training file or directory"),
dev_loc=("Location of development file or directory"),
model_dir=("Location of output model directory",),
beam_width=("Parser and NER beam width", "option", "k", int),
eval_only=("Skip training, and only evaluate", "flag", "e", bool),
corruption_level=("Amount of noise to add to training data", "option", "c", float),
gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
@ -282,7 +287,8 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
pseudoprojective=("Use pseudo-projective parsing", "flag", "p", bool),
)
def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False, pseudoprojective=False):
debug=False, corruption_level=0.0, beam_width=1,
gold_preproc=False, eval_only=False, pseudoprojective=False):
lang = spacy.util.get_lang_class(language)
if not eval_only:
@ -291,7 +297,8 @@ def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc=
feat_set='basic', #'neural' if not debug else 'debug',
gold_preproc=gold_preproc, n_sents=n_sents,
corruption_level=corruption_level, n_iter=n_iter,
verbose=verbose,pseudoprojective=pseudoprojective)
verbose=verbose, pseudoprojective=pseudoprojective,
beam_width=beam_width)
if out_loc:
write_parses(lang, dev_loc, model_dir, out_loc)
print(model_dir)