import json import pandas as pd import datasets logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """\ Klue Machine Reading Comprehension Data """ _URL = "https://huggingface.co/datasets/LeverageX/klue-mrc/resolve/main/" _URLS = { "train_data": _URL + "klue-mrc-v1.1_train.json", "validation_data": _URL + "klue-mrc-v1.1_dev.json", } class KoreanNewspaper(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig( name="KLUE Machine Reading Comprehension", version=datasets.Version("1.0.0", ""), description="For LeverageX Project", ), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "context": datasets.Value("string"), "question": datasets.Value("string"), "answers": { "answer_start" : datasets.Sequence(datasets.Value("int32")), "text":datasets.Sequence(datasets.Value("string")) }, "guid":datasets.Value("string"), } ), # No default supervised_keys (as we have to pass both question # and context as input). supervised_keys=None, homepage="https://klue-benchmark.com/tasks/70/overview/description", ) def _split_generators(self, dl_manager): downloaded_files = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train_data"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation_data"]}), ] def _generate_examples(self, filepath): """This function returns the examples in the raw (text) form.""" logger.info("generating examples from = %s", filepath) key = 0 with open(filepath, encoding="utf-8") as f : data = json.load(f) data = data['data'] for info in data : title = info['title'] news_category = info['news_category'] source = info['source'] paragraphs = info['paragraphs'] if len(paragraphs) == 0 : continue context = paragraphs[0]['context'] qas = paragraphs[0]['qas'] for q in qas : question = q['question'] answer_key = 'answers' if len(q['answers']) > 0 else 'plausible_answers' answer = q[answer_key] answer_text_list = [] answer_start_list = [] for ans in answer : answer_text_list.append(ans['text']) answer_start_list.append(ans['answer_start']) answer_data = {'text' : answer_text_list, 'answer_start' : answer_start_list} guid = q['guid'] yield key, { "guid" : guid, "context" : context, "question" : question, "answers" : answer_data } key += 1