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Expose noise_level option in train CLI

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Matthew Honnibal 2018-08-16 00:41:44 +02:00
parent 8365226bf3
commit 595c893791
1 changed files with 3 additions and 2 deletions
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5
spacy/cli/train.py
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@ -34,6 +34,7 @@ from ..compat import json_dumps
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),
parser_multitasks=("Side objectives for parser CNN, e.g. dep dep,tag", "option", "pt", str), parser_multitasks=("Side objectives for parser CNN, e.g. dep dep,tag", "option", "pt", str),
noise_level=("Amount of corruption to add for data augmentation", "option", "nl", float),
entity_multitasks=("Side objectives for ner CNN, e.g. dep dep,tag", "option", "et", str), entity_multitasks=("Side objectives for ner CNN, e.g. dep dep,tag", "option", "et", str),
gold_preproc=("Use gold preprocessing", "flag", "G", bool), gold_preproc=("Use gold preprocessing", "flag", "G", bool),
version=("Model version", "option", "V", str), version=("Model version", "option", "V", str),
@ -42,7 +43,7 @@ from ..compat import json_dumps
verbose=("Display more information for debug", "option", None, bool)) verbose=("Display more information for debug", "option", None, bool))
def train(lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0, def train(lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
parser_multitasks='', entity_multitasks='', parser_multitasks='', entity_multitasks='',
use_gpu=-1, vectors=None, no_tagger=False, use_gpu=-1, vectors=None, no_tagger=False, noise_level=0.0,
no_parser=False, no_entities=False, gold_preproc=False, no_parser=False, no_entities=False, gold_preproc=False,
version="0.0.0", meta_path=None, verbose=False): version="0.0.0", meta_path=None, verbose=False):
""" """
@ -124,7 +125,7 @@ def train(lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
print("Itn. Dep Loss NER Loss UAS NER P. NER R. NER F. Tag % Token % CPU WPS GPU WPS") print("Itn. Dep Loss NER Loss UAS NER P. NER R. NER F. Tag % Token % CPU WPS GPU WPS")
try: try:
for i in range(n_iter): for i in range(n_iter):
train_docs = corpus.train_docs(nlp, noise_level=0.0, train_docs = corpus.train_docs(nlp, noise_level=noise_level,
gold_preproc=gold_preproc, max_length=0) gold_preproc=gold_preproc, max_length=0)
words_seen = 0 words_seen = 0
with tqdm.tqdm(total=n_train_words, leave=False) as pbar: with tqdm.tqdm(total=n_train_words, leave=False) as pbar: