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Add gold_preproc flag to cli/train

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This commit is contained in:
Matthew Honnibal 2017-08-20 11:07:00 -05:00
parent 42fa84075f
commit 84bb543e4d
1 changed files with 6 additions and 4 deletions
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10
spacy/cli/train.py
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@ -32,10 +32,12 @@ from ..compat import json_dumps
resume=("Whether to resume training", "flag", "R", bool), resume=("Whether to resume training", "flag", "R", bool),
no_tagger=("Don't train tagger", "flag", "T", bool), no_tagger=("Don't train tagger", "flag", "T", bool),
no_parser=("Don't train parser", "flag", "P", bool), no_parser=("Don't train parser", "flag", "P", bool),
no_entities=("Don't train NER", "flag", "N", bool) no_entities=("Don't train NER", "flag", "N", bool),
gold_preproc=("Use gold preprocessing", "flag", "G", bool),
) )
def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0, def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
use_gpu=-1, resume=False, no_tagger=False, no_parser=False, no_entities=False): use_gpu=-1, resume=False, no_tagger=False, no_parser=False, no_entities=False,
gold_preproc=False):
""" """
Train a model. Expects data in spaCy's JSON format. Train a model. Expects data in spaCy's JSON format.
""" """
@ -86,7 +88,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
i += 20 i += 20
with tqdm.tqdm(total=n_train_words, leave=False) as pbar: with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
train_docs = corpus.train_docs(nlp, projectivize=True, train_docs = corpus.train_docs(nlp, projectivize=True,
gold_preproc=False, max_length=0) gold_preproc=gold_preproc, max_length=0)
losses = {} losses = {}
for batch in minibatch(train_docs, size=batch_sizes): for batch in minibatch(train_docs, size=batch_sizes):
docs, golds = zip(*batch) docs, golds = zip(*batch)
@ -104,7 +106,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
scorer = nlp_loaded.evaluate( scorer = nlp_loaded.evaluate(
corpus.dev_docs( corpus.dev_docs(
nlp_loaded, nlp_loaded,
gold_preproc=False)) gold_preproc=gold_preproc))
acc_loc =(output_path / ('model%d' % i) / 'accuracy.json') acc_loc =(output_path / ('model%d' % i) / 'accuracy.json')
with acc_loc.open('w') as file_: with acc_loc.open('w') as file_:
file_.write(json_dumps(scorer.scores)) file_.write(json_dumps(scorer.scores))