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"""SQuAD Bengali Dataset""" |
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import os |
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import json |
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import datasets |
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from datasets.tasks import QuestionAnsweringExtractive |
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_CITATION = """\ |
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@misc{bhattacharjee2021banglabert, |
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title={BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding}, |
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author={Abhik Bhattacharjee and Tahmid Hasan and Kazi Samin and Md Saiful Islam and M. Sohel Rahman and Anindya Iqbal and Rifat Shahriyar}, |
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year={2021}, |
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eprint={2101.00204}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """\ |
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SQuAD-bn is derived from the SQuAD-2.0 and TyDI-QA datasets. |
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""" |
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_HOMEPAGE = "https://github.com/csebuetnlp/banglabert" |
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_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)" |
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_URL = "https://huggingface.co/datasets/csebuetnlp/squad_bn/resolve/main/data/squad_bn.tar.bz2" |
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_VERSION = datasets.Version("0.0.1") |
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class SquadBn(datasets.GeneratorBasedBuilder): |
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"""SQuAD Bengali Dataset""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="squad_bn", |
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version=_VERSION, |
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description=_DESCRIPTION, |
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) |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"context": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answers": datasets.features.Sequence( |
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{ |
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"text": datasets.Value("string"), |
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"answer_start": datasets.Value("int32"), |
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} |
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), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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task_templates=[ |
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QuestionAnsweringExtractive( |
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question_column="question", context_column="context", answers_column="answers" |
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) |
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], |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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data_dir = os.path.join(dl_manager.download_and_extract(_URL), "squad_bn") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "train.json"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "test.json"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "validation.json"), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Yields examples as (key, example) tuples.""" |
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with open(filepath, encoding="utf-8") as f: |
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data = json.load(f) |
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for example in data["data"]: |
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title = example.get("title", "") |
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for paragraph in example["paragraphs"]: |
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context = paragraph["context"].strip() |
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for qa in paragraph["qas"]: |
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question = qa["question"].strip() |
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id_ = qa["id"] |
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answer_starts = [answer["answer_start"] for answer in qa["answers"]] |
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answers = [answer["text"].strip() for answer in qa["answers"]] |
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yield id_, { |
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"title": title, |
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"context": context, |
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"question": question, |
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"id": id_, |
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"answers": { |
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"answer_start": answer_starts, |
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"text": answers, |
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}, |
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} |
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