Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
closed-domain-qa
Languages:
English
Size:
1K - 10K
ArXiv:
License:
File size: 5,950 Bytes
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# coding=utf-8
# Copyright 2022 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Qasper: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers."""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{Dasigi2021ADO,
title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
year={2021}
}
"""
_LICENSE = "CC BY 4.0"
_DESCRIPTION = """\
A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners.
"""
_HOMEPAGE = "https://allenai.org/data/qasper"
_URL_TRAIN_DEV = "https://qasper-dataset.s3.us-west-2.amazonaws.com/qasper-train-dev-v0.3.tgz"
_URL_TEST = "https://qasper-dataset.s3.us-west-2.amazonaws.com/qasper-test-and-evaluator-v0.3.tgz"
_DATA_FILES = {"train": "qasper-train-v0.3.json",
"dev": "qasper-dev-v0.3.json",
"test": "qasper-test-v0.3.json"}
_VERSION = "0.3.0"
class Qasper(datasets.GeneratorBasedBuilder):
"""Qasper: A Dataset of Information-Seeking Q&A Anchored in Research Papers."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="qasper",
version=datasets.Version(_VERSION),
description=_DESCRIPTION,
)
]
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"abstract": datasets.Value("string"),
"full_text": datasets.features.Sequence(
{
"section_name": datasets.Value("string"),
"paragraphs": [datasets.Value("string")],
}
),
"qas": datasets.features.Sequence(
{
"question": datasets.Value("string"),
"question_id": datasets.Value("string"),
"nlp_background": datasets.Value("string"),
"topic_background": datasets.Value("string"),
"paper_read": datasets.Value("string"),
"search_query": datasets.Value("string"),
"question_writer": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"answer": {
"unanswerable": datasets.Value("bool"),
"extractive_spans": datasets.features.Sequence(datasets.Value("string")),
"yes_no": datasets.Value("bool"),
"free_form_answer": datasets.Value("string"),
"evidence": datasets.features.Sequence(datasets.Value("string")),
"highlighted_evidence": datasets.features.Sequence(datasets.Value("string")),
},
"annotation_id": datasets.Value("string"),
"worker_id": datasets.Value("string"),
}
),
}
),
"figures_and_tables": datasets.features.Sequence(
{
"caption": datasets.Value("string"),
"file": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
archive_train_dev, archive_test = dl_manager.download((
_URL_TRAIN_DEV, _URL_TEST)
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": _DATA_FILES["train"],
"files": dl_manager.iter_archive(archive_train_dev)},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": _DATA_FILES["dev"],
"files": dl_manager.iter_archive(archive_train_dev)},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": _DATA_FILES["test"],
"files": dl_manager.iter_archive(archive_test)},
),
]
def _generate_examples(self, filepath, files):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
for path, f in files:
if path == filepath:
qasper = json.loads(f.read().decode("utf-8"))
for id_ in qasper:
qasper[id_]["id"] = id_
yield id_, qasper[id_]
break
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