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import json |
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import pandas as pd |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """\ |
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Klue Machine Reading Comprehension Data |
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""" |
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_URL = "https://huggingface.co/datasets/LeverageX/klue-mrc/resolve/main/" |
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_URLS = { |
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"train_data": _URL + "klue-mrc-v1.1_train.json", |
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"validation_data": _URL + "klue-mrc-v1.1_dev.json", |
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} |
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class KoreanNewspaper(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="KLUE Machine Reading Comprehension", |
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version=datasets.Version("1.0.0", ""), |
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description="For LeverageX Project", |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"context": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answers": |
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{ |
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"answer_start" : datasets.Sequence(datasets.Value("int32")), |
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"text":datasets.Sequence(datasets.Value("string")) |
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}, |
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"guid":datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://klue-benchmark.com/tasks/70/overview/description", |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train_data"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation_data"]}), |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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key = 0 |
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with open(filepath, encoding="utf-8") as f : |
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data = json.load(f) |
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data = data['data'] |
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for info in data : |
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title = info['title'] |
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news_category = info['news_category'] |
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source = info['source'] |
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paragraphs = info['paragraphs'] |
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if len(paragraphs) == 0 : |
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continue |
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context = paragraphs[0]['context'] |
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qas = paragraphs[0]['qas'] |
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for q in qas : |
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question = q['question'] |
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answer_key = 'answers' if len(q['answers']) > 0 else 'plausible_answers' |
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answer = q[answer_key] |
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answer_text_list = [] |
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answer_start_list = [] |
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for ans in answer : |
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answer_text_list.append(ans['text']) |
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answer_start_list.append(ans['answer_start']) |
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answer_data = {'text' : answer_text_list, 'answer_start' : answer_start_list} |
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guid = q['guid'] |
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yield key, { |
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"guid" : guid, |
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"context" : context, |
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"question" : question, |
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"answers" : answer_data |
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} |
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key += 1 |
