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Fix begin_training if get_gold_tuples is None
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affd3404ab
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@ -94,7 +94,7 @@ def main(model=None, output_dir=None, n_iter=100):
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
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with nlp.disable_pipes(*other_pipes): # only train parser
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with nlp.disable_pipes(*other_pipes): # only train parser
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optimizer = nlp.begin_training(lambda: [])
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(TRAIN_DATA)
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losses = {}
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losses = {}
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@ -87,7 +87,7 @@ def main(model=None, new_model_name='animal', output_dir=None, n_iter=50):
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
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with nlp.disable_pipes(*other_pipes): # only train NER
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with nlp.disable_pipes(*other_pipes): # only train NER
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random.seed(0)
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random.seed(0)
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optimizer = nlp.begin_training(lambda: [])
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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for itn in range(n_iter):
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losses = {}
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losses = {}
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gold_parses = get_gold_parses(nlp.make_doc, TRAIN_DATA)
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gold_parses = get_gold_parses(nlp.make_doc, TRAIN_DATA)
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@ -64,7 +64,7 @@ def main(model=None, output_dir=None, n_iter=1000):
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# get names of other pipes to disable them during training
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# get names of other pipes to disable them during training
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
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with nlp.disable_pipes(*other_pipes): # only train parser
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with nlp.disable_pipes(*other_pipes): # only train parser
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optimizer = nlp.begin_training(lambda: [])
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(TRAIN_DATA)
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losses = {}
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losses = {}
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@ -61,7 +61,7 @@ def main(lang='en', output_dir=None, n_iter=25):
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tagger = nlp.create_pipe('tagger')
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tagger = nlp.create_pipe('tagger')
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nlp.add_pipe(tagger)
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nlp.add_pipe(tagger)
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optimizer = nlp.begin_training(lambda: [])
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optimizer = nlp.begin_training()
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for i in range(n_iter):
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for i in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(TRAIN_DATA)
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losses = {}
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losses = {}
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@ -59,7 +59,7 @@ def main(model=None, output_dir=None, n_iter=20):
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# get names of other pipes to disable them during training
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# get names of other pipes to disable them during training
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat']
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat']
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with nlp.disable_pipes(*other_pipes): # only train textcat
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with nlp.disable_pipes(*other_pipes): # only train textcat
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optimizer = nlp.begin_training(lambda: [])
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optimizer = nlp.begin_training()
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print("Training the model...")
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print("Training the model...")
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print('{:^5}\t{:^5}\t{:^5}\t{:^5}'.format('LOSS', 'P', 'R', 'F'))
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print('{:^5}\t{:^5}\t{:^5}\t{:^5}'.format('LOSS', 'P', 'R', 'F'))
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for i in range(n_iter):
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for i in range(n_iter):
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@ -436,8 +436,10 @@ class Language(object):
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**cfg: Config parameters.
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**cfg: Config parameters.
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RETURNS: An optimizer
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RETURNS: An optimizer
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"""
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"""
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if get_gold_tuples is None:
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get_gold_tuples = lambda: []
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# Populate vocab
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# Populate vocab
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if get_gold_tuples is not None:
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else:
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for _, annots_brackets in get_gold_tuples():
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for _, annots_brackets in get_gold_tuples():
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for annots, _ in annots_brackets:
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for annots, _ in annots_brackets:
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for word in annots[1]:
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for word in annots[1]:
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