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"""UnibQuAD: A Indonesian-Language Question Answering Dataset Base On University Of Bengkulu."""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """ """
_DESCRIPTION = """ """
_URL = "https://drive.google.com/uc?export=download&id=14hqePCXk2SFnmgrFomVqI6Qi3cmKSb0H"
class UnibQuADConfig(datasets.BuilderConfig):
"""BuilderConfig for UnibQuAD."""
def __init__(self, **kwargs):
"""BuilderConfig for UnibQuAD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(UnibQuADConfig, self).__init__(**kwargs)
class UnibDPR(datasets.GeneratorBasedBuilder):
"""UnibQuAD: A Indonesian-Language Question Answering Dataset Base On University Of Bengkulu."""
BUILDER_CONFIGS = [
UnibQuADConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
)
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage=" ",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files+"/quad3/train_quad3.json"}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files+"/quad3/test_quad3.json"}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
UnibQuAD = json.load(f)
for article in UnibQuAD["data"]:
for paragraph in article["paragraphs"]:
context = paragraph["context"]
document_id = paragraph["document_id"]
for qa in paragraph["qas"]:
question = qa["question"]
id_ = qa["id"]
answers = [{"answer_start": answer["answer_start"], "text": answer["text"]} for answer in qa["answers"]]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
yield id_, {
"context": context,
"question": question,
"id": id_,
"answers": answers,
} |