Datasets:

allenai
/

cosmos_qa

	"""TODO(cosmos_qa): Add a description here."""

	from __future__ import absolute_import, division, print_function

	import csv
	import json

	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/"
	_URLS = {
	"train": _URL + "train.csv",
	"test": _URL + "test.jsonl",
	"dev": _URL + "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 = _URLS
	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)),
	}