mirror of https://github.com/explosion/spaCy.git
Update intent parser example
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@ -64,7 +64,7 @@ TRAIN_DATA = [
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model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
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model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
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output_dir=("Optional output directory", "option", "o", Path),
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output_dir=("Optional output directory", "option", "o", Path),
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n_iter=("Number of training iterations", "option", "n", int))
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n_iter=("Number of training iterations", "option", "n", int))
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def main(model=None, output_dir=None, n_iter=100):
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def main(model=None, output_dir=None, n_iter=5):
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"""Load the model, set up the pipeline and train the parser."""
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"""Load the model, set up the pipeline and train the parser."""
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if model is not None:
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if model is not None:
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nlp = spacy.load(model) # load existing spaCy model
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nlp = spacy.load(model) # load existing spaCy model
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@ -73,20 +73,19 @@ def main(model=None, output_dir=None, n_iter=100):
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nlp = spacy.blank('en') # create blank Language class
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nlp = spacy.blank('en') # create blank Language class
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print("Created blank 'en' model")
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print("Created blank 'en' model")
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# add the parser to the pipeline if it doesn't exist
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# We'll use the built-in dependency parser
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# nlp.create_pipe works for built-ins that are registered with spaCy
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# class, but we want to create a fresh instance, and give it a different
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if 'parser' not in nlp.pipe_names:
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# name.
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parser = nlp.create_pipe('parser')
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if 'parser' in nlp.pipe_names:
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nlp.add_pipe(parser, first=True)
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nlp.remove_pipe('parser')
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# otherwise, get it, so we can add labels to it
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parser = nlp.create_pipe('parser')
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else:
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nlp.add_pipe(parser, name='intent-parser', first=True)
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parser = nlp.get_pipe('parser')
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for text, annotations in TRAIN_DATA:
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for text, annotations in TRAIN_DATA:
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for dep in annotations.get('deps', []):
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for dep in annotations.get('deps', []):
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parser.add_label(dep)
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parser.add_label(dep)
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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 != 'intent-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()
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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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