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			97 lines
		
	
	
		
			3.9 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			97 lines
		
	
	
		
			3.9 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
# coding: utf8
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from __future__ import unicode_literals
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import srsly
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from spacy.gold import GoldCorpus
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from spacy.lang.en import English
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from ..util import make_tempdir
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def test_issue4402():
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    nlp = English()
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    with make_tempdir() as tmpdir:
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        print("temp", tmpdir)
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        json_path = tmpdir / "test4402.json"
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        srsly.write_json(json_path, json_data)
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        corpus = GoldCorpus(str(json_path), str(json_path))
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        train_docs = list(corpus.train_docs(nlp, gold_preproc=True, max_length=0))
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        # assert that the data got split into 4 sentences
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        assert len(train_docs) == 4
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json_data = [
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    {
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        "id": 0,
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        "paragraphs": [
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            {
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                "raw": "How should I cook bacon in an oven?\nI've heard of people cooking bacon in an oven.",
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                "sentences": [
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                    {
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                        "tokens": [
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                            {"id": 0, "orth": "How", "ner": "O"},
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                            {"id": 1, "orth": "should", "ner": "O"},
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                            {"id": 2, "orth": "I", "ner": "O"},
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                            {"id": 3, "orth": "cook", "ner": "O"},
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                            {"id": 4, "orth": "bacon", "ner": "O"},
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                            {"id": 5, "orth": "in", "ner": "O"},
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                            {"id": 6, "orth": "an", "ner": "O"},
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                            {"id": 7, "orth": "oven", "ner": "O"},
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                            {"id": 8, "orth": "?", "ner": "O"},
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                        ],
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                        "brackets": [],
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                    },
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                    {
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                        "tokens": [
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                            {"id": 9, "orth": "\n", "ner": "O"},
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                            {"id": 10, "orth": "I", "ner": "O"},
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                            {"id": 11, "orth": "'ve", "ner": "O"},
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                            {"id": 12, "orth": "heard", "ner": "O"},
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                            {"id": 13, "orth": "of", "ner": "O"},
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                            {"id": 14, "orth": "people", "ner": "O"},
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                            {"id": 15, "orth": "cooking", "ner": "O"},
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                            {"id": 16, "orth": "bacon", "ner": "O"},
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                            {"id": 17, "orth": "in", "ner": "O"},
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                            {"id": 18, "orth": "an", "ner": "O"},
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                            {"id": 19, "orth": "oven", "ner": "O"},
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                            {"id": 20, "orth": ".", "ner": "O"},
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                        ],
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                        "brackets": [],
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                    },
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                ],
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                "cats": [
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                    {"label": "baking", "value": 1.0},
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                    {"label": "not_baking", "value": 0.0},
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                ],
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            },
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            {
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                "raw": "What is the difference between white and brown eggs?\n",
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                "sentences": [
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                    {
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                        "tokens": [
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                            {"id": 0, "orth": "What", "ner": "O"},
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                            {"id": 1, "orth": "is", "ner": "O"},
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                            {"id": 2, "orth": "the", "ner": "O"},
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                            {"id": 3, "orth": "difference", "ner": "O"},
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                            {"id": 4, "orth": "between", "ner": "O"},
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                            {"id": 5, "orth": "white", "ner": "O"},
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                            {"id": 6, "orth": "and", "ner": "O"},
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                            {"id": 7, "orth": "brown", "ner": "O"},
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                            {"id": 8, "orth": "eggs", "ner": "O"},
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                            {"id": 9, "orth": "?", "ner": "O"},
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                        ],
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                        "brackets": [],
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                    },
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                    {"tokens": [{"id": 10, "orth": "\n", "ner": "O"}], "brackets": []},
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                ],
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                "cats": [
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                    {"label": "baking", "value": 0.0},
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                    {"label": "not_baking", "value": 1.0},
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                ],
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            },
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        ],
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    }
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]
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