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"""TODO(qasc): Add a description here."""
import json
import datasets
# TODO(qasc): BibTeX citation
_CITATION = """\
@article{allenai:qasc,
author = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
title = {QASC: A Dataset for Question Answering via Sentence Composition},
journal = {arXiv:1910.11473v2},
year = {2020},
}
"""
# TODO(qasc):
_DESCRIPTION = """
QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
"""
_URl = "http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz"
class Qasc(datasets.GeneratorBasedBuilder):
"""TODO(qasc): Short description of my dataset."""
# TODO(qasc): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(qasc): 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"),
"question": datasets.Value("string"),
"choices": datasets.features.Sequence(
{"text": datasets.Value("string"), "label": datasets.Value("string")}
),
"answerKey": datasets.Value("string"),
"fact1": datasets.Value("string"),
"fact2": datasets.Value("string"),
"combinedfact": datasets.Value("string"),
"formatted_question": datasets.Value("string"),
# 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://allenai.org/data/qasc",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(qasc): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
archive = dl_manager.download(_URl)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": "/".join(["QASC_Dataset", "train.jsonl"]),
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": "/".join(["QASC_Dataset", "test.jsonl"]),
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": "/".join(["QASC_Dataset", "dev.jsonl"]),
"files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, filepath, files):
"""Yields examples."""
# TODO(qasc): Yields (key, example) tuples from the dataset
for path, f in files:
if path == filepath:
for row in f:
data = json.loads(row.decode("utf-8"))
answerkey = data.get("answerKey", "")
id_ = data["id"]
question = data["question"]["stem"]
choices = data["question"]["choices"]
text_choices = [choice["text"] for choice in choices]
label_choices = [choice["label"] for choice in choices]
fact1 = data.get("fact1", "")
fact2 = data.get("fact2", "")
combined_fact = data.get("combinedfact", "")
formatted_question = data.get("formatted_question", "")
yield id_, {
"id": id_,
"answerKey": answerkey,
"question": question,
"choices": {"text": text_choices, "label": label_choices},
"fact1": fact1,
"fact2": fact2,
"combinedfact": combined_fact,
"formatted_question": formatted_question,
}
break
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