| import json | |
| import datasets | |
| from datasets import Sequence, ClassLabel, Value | |
| _TRAIN = "MACCROBAT2020-V2.json" | |
| class MACCROBAT_biomedical_ner(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description="", | |
| features=datasets.Features( | |
| { | |
| "full_text": Value(dtype="string"), | |
| "ner_info": [ | |
| { | |
| "text": Value(dtype="string"), | |
| "label": Value(dtype="string"), | |
| "start": Value(dtype="int64"), | |
| "end": Value(dtype="int64"), | |
| } | |
| ], | |
| "tokens": Sequence(Value(dtype="string")), | |
| "ner_labels": Sequence( | |
| ClassLabel( | |
| names=[ | |
| "O", | |
| "B-ACTIVITY", | |
| "I-ACTIVITY", | |
| "I-ADMINISTRATION", | |
| "B-ADMINISTRATION", | |
| "B-AGE", | |
| "I-AGE", | |
| "I-AREA", | |
| "B-AREA", | |
| "B-BIOLOGICAL_ATTRIBUTE", | |
| "I-BIOLOGICAL_ATTRIBUTE", | |
| "I-BIOLOGICAL_STRUCTURE", | |
| "B-BIOLOGICAL_STRUCTURE", | |
| "B-CLINICAL_EVENT", | |
| "I-CLINICAL_EVENT", | |
| "B-COLOR", | |
| "I-COLOR", | |
| "I-COREFERENCE", | |
| "B-COREFERENCE", | |
| "B-DATE", | |
| "I-DATE", | |
| "I-DETAILED_DESCRIPTION", | |
| "B-DETAILED_DESCRIPTION", | |
| "I-DIAGNOSTIC_PROCEDURE", | |
| "B-DIAGNOSTIC_PROCEDURE", | |
| "I-DISEASE_DISORDER", | |
| "B-DISEASE_DISORDER", | |
| "B-DISTANCE", | |
| "I-DISTANCE", | |
| "B-DOSAGE", | |
| "I-DOSAGE", | |
| "I-DURATION", | |
| "B-DURATION", | |
| "I-FAMILY_HISTORY", | |
| "B-FAMILY_HISTORY", | |
| "B-FREQUENCY", | |
| "I-FREQUENCY", | |
| "I-HEIGHT", | |
| "B-HEIGHT", | |
| "B-HISTORY", | |
| "I-HISTORY", | |
| "I-LAB_VALUE", | |
| "B-LAB_VALUE", | |
| "I-MASS", | |
| "B-MASS", | |
| "I-MEDICATION", | |
| "B-MEDICATION", | |
| "I-NONBIOLOGICAL_LOCATION", | |
| "B-NONBIOLOGICAL_LOCATION", | |
| "I-OCCUPATION", | |
| "B-OCCUPATION", | |
| "B-OTHER_ENTITY", | |
| "I-OTHER_ENTITY", | |
| "B-OTHER_EVENT", | |
| "I-OTHER_EVENT", | |
| "I-OUTCOME", | |
| "B-OUTCOME", | |
| "I-PERSONAL_BACKGROUND", | |
| "B-PERSONAL_BACKGROUND", | |
| "B-QUALITATIVE_CONCEPT", | |
| "I-QUALITATIVE_CONCEPT", | |
| "I-QUANTITATIVE_CONCEPT", | |
| "B-QUANTITATIVE_CONCEPT", | |
| "B-SEVERITY", | |
| "I-SEVERITY", | |
| "B-SEX", | |
| "I-SEX", | |
| "B-SHAPE", | |
| "I-SHAPE", | |
| "B-SIGN_SYMPTOM", | |
| "I-SIGN_SYMPTOM", | |
| "B-SUBJECT", | |
| "I-SUBJECT", | |
| "B-TEXTURE", | |
| "I-TEXTURE", | |
| "B-THERAPEUTIC_PROCEDURE", | |
| "I-THERAPEUTIC_PROCEDURE", | |
| "I-TIME", | |
| "B-TIME", | |
| "B-VOLUME", | |
| "I-VOLUME", | |
| "I-WEIGHT", | |
| "B-WEIGHT", | |
| ] | |
| ) | |
| ), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="", | |
| citation="", | |
| ) | |
| def _split_generators(self, a): | |
| """Returns SplitGenerators.""" | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": _TRAIN}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| with open(filepath, encoding="utf-8") as f: | |
| datas = json.load(f) | |
| datas = datas["data"] | |
| guid = 0 | |
| for data in datas: | |
| yield guid, data | |
| guid += 1 | |