Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Russian
Size:
10K - 100K
ArXiv:
License:
| # coding=utf-8 | |
| """SberQUAD: Sber Question Answering Dataset.""" | |
| import json | |
| import datasets | |
| from datasets.tasks import QuestionAnsweringExtractive | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """\ | |
| @article{Efimov_2020, | |
| title={SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis}, | |
| ISBN={9783030582197}, | |
| ISSN={1611-3349}, | |
| url={http://dx.doi.org/10.1007/978-3-030-58219-7_1}, | |
| DOI={10.1007/978-3-030-58219-7_1}, | |
| journal={Experimental IR Meets Multilinguality, Multimodality, and Interaction}, | |
| publisher={Springer International Publishing}, | |
| author={Efimov, Pavel and Chertok, Andrey and Boytsov, Leonid and Braslavski, Pavel}, | |
| year={2020}, | |
| pages={3–15} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| Sber Question Answering Dataset (SberQuAD) is a reading comprehension \ | |
| dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \ | |
| articles, where the answer to every question is a segment of text, or span, \ | |
| from the corresponding reading passage, or the question might be unanswerable. \ | |
| Russian original analogue presented in Sberbank Data Science Journey 2017. | |
| """ | |
| _URLS = {"train": "https://sc.link/PNWl", "dev": "https://sc.link/W6oX", "test": "https://sc.link/VOn9"} | |
| class Sberquad(datasets.GeneratorBasedBuilder): | |
| """SberQUAD: Sber Question Answering Dataset. Version 1.0.""" | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [datasets.BuilderConfig(name="sberquad", version=VERSION, description=_DESCRIPTION)] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("int32"), | |
| "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="", | |
| citation=_CITATION, | |
| task_templates=[ | |
| QuestionAnsweringExtractive( | |
| question_column="question", context_column="context", answers_column="answers" | |
| ) | |
| ], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| downloaded_files = dl_manager.download_and_extract(_URLS) | |
| 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.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| logger.info("generating examples from = %s", filepath) | |
| key = 0 | |
| with open(filepath, encoding="utf-8") as f: | |
| squad = json.load(f) | |
| for article in squad["data"]: | |
| title = article.get("title", "") | |
| for paragraph in article["paragraphs"]: | |
| context = paragraph["context"] | |
| for qa in paragraph["qas"]: | |
| answer_starts = [answer["answer_start"] for answer in qa["answers"]] | |
| answers = [answer["text"] for answer in qa["answers"]] | |
| yield key, { | |
| "title": title, | |
| "context": context, | |
| "question": qa["question"], | |
| "id": qa["id"], | |
| "answers": { | |
| "answer_start": answer_starts, | |
| "text": answers, | |
| }, | |
| } | |
| key += 1 | |