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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":dict,
"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][0]
answer_text = answer['text']
answer_start = answer['answer_start']
answer_data = {'answer_start' : [answer_start], 'text': [answer_text]}
guid = q['guid']
yield key, {
"guid" : guid,
"context" : context,
"question" : question,
"answers" : answer_data
}
key += 1 |