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50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from spacy.lang.en import English
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from spacy.tokenizer import Tokenizer
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from spacy import util
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from ..util import make_tempdir
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def test_issue4190():
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test_string = "Test c."
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# Load default language
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nlp_1 = English()
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doc_1a = nlp_1(test_string)
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result_1a = [token.text for token in doc_1a] # noqa: F841
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# Modify tokenizer
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customize_tokenizer(nlp_1)
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doc_1b = nlp_1(test_string)
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result_1b = [token.text for token in doc_1b]
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# Save and Reload
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with make_tempdir() as model_dir:
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nlp_1.to_disk(model_dir)
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nlp_2 = util.load_model(model_dir)
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# This should be the modified tokenizer
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doc_2 = nlp_2(test_string)
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result_2 = [token.text for token in doc_2]
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assert result_1b == result_2
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def customize_tokenizer(nlp):
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prefix_re = util.compile_prefix_regex(nlp.Defaults.prefixes)
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suffix_re = util.compile_suffix_regex(nlp.Defaults.suffixes)
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infix_re = util.compile_infix_regex(nlp.Defaults.infixes)
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# Remove all exceptions where a single letter is followed by a period (e.g. 'h.')
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exceptions = {
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k: v
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for k, v in dict(nlp.Defaults.tokenizer_exceptions).items()
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if not (len(k) == 2 and k[1] == ".")
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}
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new_tokenizer = Tokenizer(
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nlp.vocab,
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exceptions,
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prefix_search=prefix_re.search,
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suffix_search=suffix_re.search,
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infix_finditer=infix_re.finditer,
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token_match=nlp.tokenizer.token_match,
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)
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nlp.tokenizer = new_tokenizer
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