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Fix begin_training if get_gold_tuples is None

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ines 2017-11-01 13:14:31 +01:00
parent affd3404ab
commit bfe17b7df1
6 changed files with 8 additions and 6 deletions
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2
examples/training/train_intent_parser.py
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@ -94,7 +94,7 @@ def main(model=None, output_dir=None, n_iter=100):
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser'] other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
with nlp.disable_pipes(*other_pipes): # only train parser with nlp.disable_pipes(*other_pipes): # only train parser
optimizer = nlp.begin_training(lambda: []) optimizer = nlp.begin_training()
for itn in range(n_iter): for itn in range(n_iter):
random.shuffle(TRAIN_DATA) random.shuffle(TRAIN_DATA)
losses = {} losses = {}

2
examples/training/train_new_entity_type.py
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@ -87,7 +87,7 @@ def main(model=None, new_model_name='animal', output_dir=None, n_iter=50):
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner'] other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
with nlp.disable_pipes(*other_pipes): # only train NER with nlp.disable_pipes(*other_pipes): # only train NER
random.seed(0) random.seed(0)
optimizer = nlp.begin_training(lambda: []) optimizer = nlp.begin_training()
for itn in range(n_iter): for itn in range(n_iter):
losses = {} losses = {}
gold_parses = get_gold_parses(nlp.make_doc, TRAIN_DATA) gold_parses = get_gold_parses(nlp.make_doc, TRAIN_DATA)

2
examples/training/train_parser.py
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@ -64,7 +64,7 @@ def main(model=None, output_dir=None, n_iter=1000):
# get names of other pipes to disable them during training # get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser'] other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
with nlp.disable_pipes(*other_pipes): # only train parser with nlp.disable_pipes(*other_pipes): # only train parser
optimizer = nlp.begin_training(lambda: []) optimizer = nlp.begin_training()
for itn in range(n_iter): for itn in range(n_iter):
random.shuffle(TRAIN_DATA) random.shuffle(TRAIN_DATA)
losses = {} losses = {}

2
examples/training/train_tagger.py
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@ -61,7 +61,7 @@ def main(lang='en', output_dir=None, n_iter=25):
tagger = nlp.create_pipe('tagger') tagger = nlp.create_pipe('tagger')
nlp.add_pipe(tagger) nlp.add_pipe(tagger)
optimizer = nlp.begin_training(lambda: []) optimizer = nlp.begin_training()
for i in range(n_iter): for i in range(n_iter):
random.shuffle(TRAIN_DATA) random.shuffle(TRAIN_DATA)
losses = {} losses = {}

2
examples/training/train_textcat.py
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@ -59,7 +59,7 @@ def main(model=None, output_dir=None, n_iter=20):
# get names of other pipes to disable them during training # get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat'] other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat']
with nlp.disable_pipes(*other_pipes): # only train textcat with nlp.disable_pipes(*other_pipes): # only train textcat
optimizer = nlp.begin_training(lambda: []) optimizer = nlp.begin_training()
print("Training the model...") print("Training the model...")
print('{:^5}\t{:^5}\t{:^5}\t{:^5}'.format('LOSS', 'P', 'R', 'F')) print('{:^5}\t{:^5}\t{:^5}\t{:^5}'.format('LOSS', 'P', 'R', 'F'))
for i in range(n_iter): for i in range(n_iter):

4
spacy/language.py
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@ -436,8 +436,10 @@ class Language(object):
**cfg: Config parameters. **cfg: Config parameters.
RETURNS: An optimizer RETURNS: An optimizer
""" """
if get_gold_tuples is None:
get_gold_tuples = lambda: []
# Populate vocab # Populate vocab
if get_gold_tuples is not None: else:
for _, annots_brackets in get_gold_tuples(): for _, annots_brackets in get_gold_tuples():
for annots, _ in annots_brackets: for annots, _ in annots_brackets:
for word in annots[1]: for word in annots[1]: