boolq / boolq.py
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Update boolq.py
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# Based on https://huggingface.co/datasets/boolq/blob/main/boolq.py
import json
import datasets
# TODO(boolq): BibTeX citation
_CITATION = """\
@inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
}
"""
# TODO(boolq):
_DESCRIPTION = """\
BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair classification setup is similar to existing natural language inference tasks.
"""
_URL = "https://storage.googleapis.com/boolq/"
_URLS = {
"train": _URL + "train.jsonl",
"dev": _URL + "dev.jsonl",
}
class Boolq(datasets.GeneratorBasedBuilder):
"""TODO(boolq): Short description of my dataset."""
# TODO(boolq): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(boolq): 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(
{
"question": datasets.Value("string"),
"answer": datasets.Value("bool"),
"passage": datasets.Value("string"),
"title": datasets.Value("string"),
}
),
# 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://github.com/google-research-datasets/boolean-questions",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(boolq): 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
downloaded_files = dl_manager.download(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": downloaded_files["dev"]},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(boolq): 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"]
answer = data["answer"]
passage = data["passage"]
title = data["title"]
yield id_, {"question": question, "answer": answer, "passage": passage, "title": title}