Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
Russian
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 4,222 Bytes
358bdfe 92d74b2 358bdfe 92d74b2 358bdfe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
# coding=utf-8
"""SberQUAD: Sber Question Answering Dataset."""
import os
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": os.path.join("data", "train_v1.0.json.gz"), "dev": os.path.join("data", "dev_v1.0.json.gz"), "test": os.path.join("data", "origin_test.json.gz")}
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
|