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import json
import pandas as pd
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
Korean Book Summarization Data
"""
_URL = "https://huggingface.co/datasets/LeverageX/book-summarization/resolve/main/"
_URLS = {
"train_data": _URL + "train_data.json",
"validation_data": _URL + "validation_data.json",
}
class KoreanNewspaper(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="Aihub Book Summarization",
version=datasets.Version("1.0.0", ""),
description="Korean Summarization Data",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"name": datasets.Value("string"),
"publisher": datasets.Value("string"),
"passage": datasets.Value("string"),
"summary": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://aihub.or.kr/aidata/30713",
)
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)
for info in data :
doc_id = info['id']
doc_name = info['name']
publisher = info['publisher']
passage = info['passage']
summary = info['summary']
yield key, {
"id" : doc_id,
"name" : doc_name,
"publisher" : publisher,
"passage" : passage,
"summary" : summary,
}
key += 1 |