import json import datasets from datasets import Sequence, ClassLabel, Value _TRAIN_FILE = "MACCROBAT2020-V2.json" _NAME = "MACCROBAT_biomedical_ner" _TRAIN_URL = f"https://huggingface.co/datasets/singh-aditya/{_NAME}/raw/main/{_TRAIN_FILE}" 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_URL}), ] 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