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09/04/2020 .nio.Q: Use an existing condition as a filter when using Spacy in Python I am using Spacy for NLP in python. I would like to be able to add a custom condition to an existing model. For example: I would like to restrict the model to only select euclidean distance objects. For now the only way I can do this is by adding a custom tokenizer, but this is not desirable. Is there a way to filter an existing model based on the condition? A: In Spacy, a condition can be applied to the tokenizer, as well as the parser. It is not possible to filter the training pipeline, but this can be done after training is complete. For example, to filter out “foo” token, you can apply the following condition to your tokenizer, before training: inputs = {“tokenizer”: { “pattern”: r”[foo]”, “min_token_length”: 1, “lowercase”: True }} This approach will also convert any occurrences of “foo” to its own token. As a result, it won’t be part of the vocabulary, so it won’t be in the `model.vocab and can be discarded safely before training. To filter out the “num_foo” token, you can run: import spacy nlp = spacy.load(“en_core_web_sm”) nlp.tokenizer.remove(“num_foo”) And this will remove “num_foo” as well as it’s synonyms: >>> print(list(set(nlp.tokenizer.token_to_s()))) [‘foo’, ‘bar’, ‘baz’, ‘boo’, ‘qux’, ‘…’] A 25-year-old man has been charged with first-degree murder following the brutal killing of a man in a west end parking

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