Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
open-domain-qa
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 4,369 Bytes
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"""TODO(commonsense_qa): Add a description here."""
from __future__ import absolute_import, division, print_function
import json
import os
import datasets
# TODO(commonsense_qa): BibTeX citation
_CITATION = """\
@InProceedings{commonsense_QA,
title={COMMONSENSEQA: A Question Answering Challenge Targeting Commonsense Knowledge},
author={Alon, Talmor and Jonathan, Herzig and Nicholas, Lourie and Jonathan ,Berant},
journal={arXiv preprint arXiv:1811.00937v2},
year={2019}
"""
# TODO(commonsense_qa):
_DESCRIPTION = """\
CommonsenseQA is 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.
"""
_URL = "https://s3.amazonaws.com/commensenseqa"
_TRAINING_FILE = "train_rand_split.jsonl"
_DEV_FILE = "dev_rand_split.jsonl"
_TEST_FILE = "test_rand_split_no_answers.jsonl"
class CommonsenseQa(datasets.GeneratorBasedBuilder):
"""TODO(commonsense_qa): Short description of my dataset."""
# TODO(commonsense_qa): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# These are the features of your dataset like images, labels ...
features = datasets.Features(
{
"answerKey": datasets.Value("string"),
"question": datasets.Value("string"),
"choices": datasets.features.Sequence(
{
"label": datasets.Value("string"),
"text": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=features,
# 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://www.tau-datasets.org/commonsenseqa",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
download_urls = {
"train": os.path.join(_URL, _TRAINING_FILE),
"test": os.path.join(_URL, _TEST_FILE),
"dev": os.path.join(_URL, _DEV_FILE),
}
downloaded_files = dl_manager.download_and_extract(download_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"], "split": "train"}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": downloaded_files["dev"],
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "test",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
# TODO(commonsense_qa): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
question = data["question"]
choices = question["choices"]
labels = [label["label"] for label in choices]
texts = [text["text"] for text in choices]
stem = question["stem"]
if split == "test":
answerkey = ""
else:
answerkey = data["answerKey"]
yield id_, {
"answerKey": answerkey,
"question": stem,
"choices": {"label": labels, "text": texts},
}
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