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	* Remove unicode declarations * Remove Python 3.5 and 2.7 from CI * Don't require pathlib * Replace compat helpers * Remove OrderedDict * Use f-strings * Set Cython compiler language level * Fix typo * Re-add OrderedDict for Table * Update setup.cfg * Revert CONTRIBUTING.md * Revert lookups.md * Revert top-level.md * Small adjustments and docs [ci skip]
		
			
				
	
	
		
			47 lines
		
	
	
		
			1.5 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			47 lines
		
	
	
		
			1.5 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| 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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| 
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| from ..util import make_tempdir
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| 
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| 
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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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| 
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| 
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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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