Datasets:

Gholamreza
/

pquad

	# Taken from https://huggingface.co/datasets/Shayanvsf/pquad_public/raw/main/pquad_public.py
	# Edited for the complete dataset (25MB Train.csv)
	# By Gholamreza Dar
	# Feb 2023

	import json
	import datasets
	_CITATION = """\
	@article{darvishi2022pquad,
	title={PQuAD: A Persian Question Answering Dataset},
	author={Darvishi, Kasra and Shahbodagh, Newsha and Abbasiantaeb, Zahra and Momtazi, Saeedeh},
	journal={arXiv preprint arXiv:2202.06219},
	year={2022}
	}
	"""
	_DESCRIPTION = """\\\\
	PQuAD: PQuAD is a crowd-sourced reading comprehension dataset on Persian Language.
	"""
	_URL = "https://raw.githubusercontent.com/AUT-NLP/PQuAD/main/Dataset/"
	_URLS = {
	"train": _URL + "Train.json",
	"validation":_URL + "Validation.json",
	"test": _URL + "Test.json",
	}
	class pquad_public_Config(datasets.BuilderConfig):
	"""BuilderConfig for PQuAD."""
	def __init__(self, **kwargs):
	"""BuilderConfig for PQuAD.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(pquad_public_Config, self).__init__(**kwargs)
	class pquad_public(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	pquad_public_Config(name="pquad", version=datasets.Version("1.0.0"), description="PQuAD"),
	]
	def _info(self):
	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("float64"),
	"title": datasets.Value("string"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	}
	),
	}
	),
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="https://github.com/AUT-NLP/PQuAD",
	citation=_CITATION,
	)
	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(persian_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
	downloaded_files = dl_manager.download_and_extract(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["validation"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
	]
	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(persian_qa): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	print(filepath)
	squad = json.load(f)
	for example in squad["data"]:
	title = example.get("title", "").strip()
	for paragraph in example["paragraphs"]:
	context = paragraph["context"].strip()
	for qa in paragraph["qas"]:
	question = qa["question"].strip()
	id_ = qa["id"]
	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"].strip() for answer in qa["answers"]]
	# Features currently used are "context", "question", and "answers".
	# Others are extracted here for the ease of future expansions.
	yield id_, {
	"title": title,
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}