Datasets:
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# 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,
},
}
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