Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 4,318 Bytes
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"""Cosmos QA dataset."""
import csv
import json
import datasets
_HOMEPAGE = "https://wilburone.github.io/cosmos/"
_DESCRIPTION = """\
Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context
"""
_CITATION = """\
@inproceedings{huang-etal-2019-cosmos,
title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
author = "Huang, Lifu and
Le Bras, Ronan and
Bhagavatula, Chandra and
Choi, Yejin",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1243",
doi = "10.18653/v1/D19-1243",
pages = "2391--2401",
}
"""
_LICENSE = "CC BY 4.0"
_URL = "https://github.com/wilburOne/cosmosqa/raw/master/data/"
_URLS = {
"train": _URL + "train.csv",
"test": _URL + "test.jsonl",
"dev": _URL + "valid.csv",
}
class CosmosQa(datasets.GeneratorBasedBuilder):
"""Cosmos QA dataset."""
VERSION = datasets.Version("0.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answer0": datasets.Value("string"),
"answer1": datasets.Value("string"),
"answer2": datasets.Value("string"),
"answer3": datasets.Value("string"),
"label": datasets.Value("int32"),
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = _URLS
dl_dir = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
if split == "test":
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, {
"id": data["id"],
"context": data["context"],
"question": data["question"],
"answer0": data["answer0"],
"answer1": data["answer1"],
"answer2": data["answer2"],
"answer3": data["answer3"],
"label": int(data.get("label", -1)),
}
else:
data = csv.DictReader(f)
for id_, row in enumerate(data):
yield id_, {
"id": row["id"],
"context": row["context"],
"question": row["question"],
"answer0": row["answer0"],
"answer1": row["answer1"],
"answer2": row["answer2"],
"answer3": row["answer3"],
"label": int(row.get("label", -1)),
}
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