Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
open-domain-qa
Languages:
Indonesian
Size:
10K - 100K
License:
File size: 3,756 Bytes
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"""CommonsenseQA-ID dataset."""
import json
import datasets
_HOMEPAGE = "https://www.tau-nlp.org/commonsenseqa"
_DESCRIPTION = """\
CommonsenseQA-ID is Indonesian translation version of CommonsenseQA, a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details.
"""
_CITATION = """\
@inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1421",
doi = "10.18653/v1/N19-1421",
pages = "4149--4158",
archivePrefix = "arXiv",
eprint = "1811.00937",
primaryClass = "cs",
}
"""
_URL = "https://raw.githubusercontent.com/rizquuula/commonsense_qa-ID/main/dataset"
_URLS = {
"train": f"{_URL}/commonsense_qa-id_train.jsonl",
"validation": f"{_URL}/commonsense_qa-id_dev.jsonl",
"test": f"{_URL}/commonsense_qa-id_test_no_answer.jsonl",
}
class CommonsenseQaID(datasets.GeneratorBasedBuilder):
"""CommonsenseQA-ID dataset."""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"question_concept": datasets.Value("string"),
"choices": datasets.features.Sequence(
{
"label": datasets.Value("string"),
"text": datasets.Value("string"),
}
),
"answerKey": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
filepaths = dl_manager.download_and_extract(_URLS)
splits = [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"filepath": filepaths[split],
},
)
for split in splits
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
for uid, row in enumerate(f):
data = json.loads(row)
choices = data["question"]["choices"]
labels = [label["label"] for label in choices]
texts = [text["text"] for text in choices]
yield uid, {
"id": data["id"],
"question": data["question"]["stem"],
"question_concept": data["question"]["question_concept"],
"choices": {"label": labels, "text": texts},
"answerKey": data.get("answerKey", ""),
}
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