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Use fewer iterations by default to avoid overfitting on blank model (resolves #1632)

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ines 2017-11-23 15:27:12 +01:00
parent f29c3925ee
commit 726fb2d0b5
1 changed files with 1 additions and 1 deletions
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examples/training/train_new_entity_type.py
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@ -73,7 +73,7 @@ TRAIN_DATA = [
new_model_name=("New model name for model meta.", "option", "nm", str), new_model_name=("New model name for model meta.", "option", "nm", str),
output_dir=("Optional output directory", "option", "o", Path), output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int)) n_iter=("Number of training iterations", "option", "n", int))
def main(model=None, new_model_name='animal', output_dir=None, n_iter=50): def main(model=None, new_model_name='animal', output_dir=None, n_iter=20):
"""Set up the pipeline and entity recognizer, and train the new entity.""" """Set up the pipeline and entity recognizer, and train the new entity."""
if model is not None: if model is not None:
nlp = spacy.load(model) # load existing spaCy model nlp = spacy.load(model) # load existing spaCy model