Datasets:
Tasks:
Question Answering
Languages:
Polish
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 5,353 Bytes
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"""FROM SQUAD_V2"""
import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
# TODO(squad_v2): BibTeX citation
_CITATION = """\
Tuora, R., Zawadzka-Paluektau, N., Klamra, C., Zwierzchowska, A., Kobyliński, Ł. (2022).
Towards a Polish Question Answering Dataset (PoQuAD).
In: Tseng, YH., Katsurai, M., Nguyen, H.N. (eds) From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries. ICADL 2022.
Lecture Notes in Computer Science, vol 13636. Springer, Cham.
https://doi.org/10.1007/978-3-031-21756-2_16
"""
_DESCRIPTION = """\
PoQuaD description
"""
_URLS = {
"train": "poquad-train.json",
"dev": "poquad-dev.json",
}
class SquadV2Config(datasets.BuilderConfig):
"""BuilderConfig for SQUAD."""
def __init__(self, **kwargs):
"""BuilderConfig for SQUADV2.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SquadV2Config, self).__init__(**kwargs)
class SquadV2(datasets.GeneratorBasedBuilder):
"""TODO(squad_v2): Short description of my dataset."""
# TODO(squad_v2): Set up version.
BUILDER_CONFIGS = [
SquadV2Config(name="poquad", version=datasets.Version("1.0.0"), description="PoQuaD plaint text"),
]
def _info(self):
# TODO(squad_v2): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
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"),
}
),
# These are the features of your dataset like images, labels ...
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://rajpurkar.github.io/SQuAD-explorer/",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(squad_v2): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
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"]}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(squad_v2): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
squad = json.load(f)
id_ = 0
for example in squad["data"]:
title = example.get("title", "")
# paragraph_id = example["id"]
for paragraph in example["paragraphs"]:
context = paragraph["context"] # do not strip leading blank spaces GH-2585
for qa in paragraph["qas"]:
question = qa["question"]
if "answers" not in qa:
continue
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
#answer_ends = [answer["answer_end"] for answer in qa["answers"]]
answers = [answer["text"] for answer in qa["answers"]]
# is_impossible = qa["is_impossible"]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
id_ += 1
yield str(id_), {
"id": str(id_),
"title": title,
"context": context,
"question": question,
#"is_impossible" : is_impossible,
# "paragraph_id": paragraph_id,
"answers": {
"answer_start": answer_starts,
#"answer_end": answer_ends,
"text": answers,
},
} |