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import io |
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import json |
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import os |
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import datasets |
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from datasets.tasks import QuestionAnsweringExtractive |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{keren2021parashoot, |
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title={ParaShoot: A Hebrew Question Answering Dataset}, |
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author={Keren, Omri and Levy, Omer}, |
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booktitle={Proceedings of the 3rd Workshop on Machine Reading for Question Answering}, |
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pages={106--112}, |
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year={2021} |
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} |
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""" |
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_DESCRIPTION = """ |
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A Hebrew question and answering dataset in the style of SQuAD, based on articles scraped from Wikipedia. The dataset contains a few thousand crowdsource-annotated pairs of questions and answers, in a setting suitable for few-shot learning. |
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""" |
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_URLS = { |
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"train": "data/train.tar.gz", |
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"validation": "data/dev.tar.gz", |
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"test": "data/test.tar.gz", |
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} |
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class ParashootConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Parashoot.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Parashoot. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(ParashootConfig, self).__init__(**kwargs) |
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class Parashoot(datasets.GeneratorBasedBuilder): |
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"""Parashoot: The Hebrew Question Answering Dataset. Version 1.1.""" |
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BUILDER_CONFIGS = [ |
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ParashootConfig( |
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version=datasets.Version("1.1.0", ""), |
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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="https://github.com/omrikeren/ParaShoot", |
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citation=_CITATION, |
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task_templates=[ |
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QuestionAnsweringExtractive( |
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question_column="question", |
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context_column="context", |
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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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downloaded_files = dl_manager.download(_URLS) |
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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": dl_manager.iter_archive(downloaded_files["train"]), |
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"basename": "train.jsonl", |
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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": dl_manager.iter_archive(downloaded_files["validation"]), |
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"basename": "dev.jsonl", |
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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": dl_manager.iter_archive(downloaded_files["test"]), |
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"basename": "test.jsonl", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, basename): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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key = 0 |
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for file_path, file_obj in filepath: |
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with io.BytesIO(file_obj.read()) as f: |
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for line in f: |
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article = json.loads(line) |
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title = article.get("title", "") |
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context = article["context"] |
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answer_starts = article["answers"]["answer_start"] |
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answers = article["answers"]["text"] |
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yield key, { |
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"title": title, |
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"context": context, |
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"question": article["question"], |
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"id": article["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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key += 1 |
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