# Taken from https://huggingface.co/datasets/Shayanvsf/pquad_public/raw/main/pquad_public.py # Edited for the complete dataset (22MB 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, }, }