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Dropout can now be specified in the `Parser.update()` method via the `drop` keyword argument, e.g. nlp.entity.update(doc, gold, drop=0.4) This will randomly drop 40% of features, and multiply the value of the others by 1. / 0.4. This may be useful for generalising from small data sets. This commit also patches the examples/training/train_new_entity_type.py example, to use dropout and fix the output (previously it did not output the learned entity). |
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load_ner.py | ||
train_ner_standalone.py | ||
train_ner.py | ||
train_new_entity_type.py | ||
train_parser.py | ||
train_tagger.py |