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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import io
import json
import os
import datasets
from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{keren2021parashoot,
title={ParaShoot: A Hebrew Question Answering Dataset},
author={Keren, Omri and Levy, Omer},
booktitle={Proceedings of the 3rd Workshop on Machine Reading for Question Answering},
pages={106--112},
year={2021}
}
"""
_DESCRIPTION = """
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.
"""
_URLS = {
"train": "data/train.tar.gz",
"validation": "data/dev.tar.gz",
"test": "data/test.tar.gz",
}
class ParashootConfig(datasets.BuilderConfig):
"""BuilderConfig for Parashoot."""
def __init__(self, **kwargs):
"""BuilderConfig for Parashoot.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(ParashootConfig, self).__init__(**kwargs)
class Parashoot(datasets.GeneratorBasedBuilder):
"""Parashoot: The Hebrew Question Answering Dataset. Version 1.1."""
BUILDER_CONFIGS = [
ParashootConfig(
version=datasets.Version("1.1.0", ""),
description=_DESCRIPTION,
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
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"),
}
),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://github.com/omrikeren/ParaShoot",
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(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": dl_manager.iter_archive(downloaded_files["train"]),
"basename": "train.jsonl",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": dl_manager.iter_archive(downloaded_files["validation"]),
"basename": "dev.jsonl",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": dl_manager.iter_archive(downloaded_files["test"]),
"basename": "test.jsonl",
},
),
]
def _generate_examples(self, filepath, basename):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
key = 0
for file_path, file_obj in filepath:
with io.BytesIO(file_obj.read()) as f:
for line in f:
article = json.loads(line)
title = article.get("title", "")
context = article["context"]
answer_starts = article["answers"]["answer_start"]
answers = article["answers"]["text"]
yield key, {
"title": title,
"context": context,
"question": article["question"],
"id": article["id"],
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}
key += 1
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