from datasets import load_dataset_builder, DatasetInfo, DownloadConfig, GeneratorBasedBuilder, datasets class CustomSQuADFormatDataset(GeneratorBasedBuilder): """A custom dataset similar to SQuAD but tailored for 'ArabicaQA' hosted on Hugging Face.""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="ArabicaQA", version=VERSION, description="Custom dataset similar to SQuAD format.") ] def _info(self): return DatasetInfo( description="This dataset is formatted similarly to the SQuAD dataset.", features=datasets.Features( { "id": datasets.Value("string"), "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="https://huggingface.co/datasets/abdoelsayed/ArabicaQA", citation="", ) def _split_generators(self, dl_manager: DownloadConfig): urls_to_download = { "train": "https://huggingface.co/datasets/abdoelsayed/ArabicaQA/raw/main/MRC/train.json", "dev": "https://huggingface.co/datasets/abdoelsayed/ArabicaQA/raw/main/MRC/val.json", "test": "https://huggingface.co/datasets/abdoelsayed/ArabicaQA/raw/main/MRC/test.json" } 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"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["test"]}), ] def _generate_examples(self, filepath): with open(filepath, encoding="utf-8") as f: squad_data = json.load(f)["data"] for article in squad_data: title = article.get("title", "") for paragraph in article["paragraphs"]: context = paragraph["context"] for qa in paragraph["qas"]: id_ = qa["id"] question = qa["question"] answers = [{"text": answer["text"], "answer_start": answer["answer_start"]} for answer in qa.get("answers", [])] yield id_, { "title": title, "context": context, "question": question, "answers": answers, }