Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
File size: 5,174 Bytes
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"""TODO(cosmos_qa): Add a description here."""
from __future__ import absolute_import, division, print_function
import csv
import json
import os
import datasets
# TODO(cosmos_qa): BibTeX citation
_CITATION = """\
@inproceedings{cosmos,
title={COSMOS QA: Machine Reading Comprehension
with Contextual Commonsense Reasoning},
author={Lifu Huang and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
booktitle ={arXiv:1909.00277v2},
year={2019}
}
"""
# TODO(cosmos_qa):
_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
"""
_URL = "https://github.com/wilburOne/cosmosqa/raw/master/data/"
_TEST_FILE = "test.jsonl"
_TRAIN_FILE = "train.csv"
_DEV_FILE = "valid.csv"
class CosmosQa(datasets.GeneratorBasedBuilder):
"""TODO(cosmos_qa): Short description of my dataset."""
# TODO(cosmos_qa): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(cosmos_qa): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
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")
# These are the features of your dataset like images, labels ...
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://wilburone.github.io/cosmos/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(cosmos_qa): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = {
"train": os.path.join(_URL, _TRAIN_FILE),
"test": os.path.join(_URL, _TEST_FILE),
"dev": os.path.join(_URL, _DEV_FILE),
}
dl_dir = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
# TODO(cosmos_qa): Yields (key, example) tuples from the dataset
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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