Datasets:
Tasks:
Question Answering
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
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License:
File size: 3,343 Bytes
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# Copyright 2020 The HuggingFace Datasets Authors and Cory Paik
#
# 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.
# ==============================================================================
""" Physical Reasoning about Objects Through Space and Time (PROST)
PROST is a probing dataset to evaluate the ability of pretrained LMs to
understand and reason about the physical world.
"""
import json
import datasets
_CITATION = """\
@inproceedings{aroca-ouellette-etal-2021-prost,
title = "{PROST}: {P}hysical Reasoning about Objects through Space and Time",
author = "Aroca-Ouellette, St{\'e}phane and
Paik, Cory and
Roncone, Alessandro and
Kann, Katharina",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.404",
pages = "4597--4608",
}
"""
_DESCRIPTION = """\
*Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts: direction, mass, height, circumference, stackable, rollable, graspable, breakable, slideable, and bounceable.
"""
_HOMEPAGE = 'https://github.com/nala-cub/prost'
_LICENSE = 'Apache 2.0'
_URL = 'https://huggingface.co/datasets/corypaik/prost/resolve/main/data'
_URLs = {'default': f'{_URL}/default.jsonl'}
MC_LABELS = list('ABCD')
class Prost(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version('1.0.1')
def _info(self):
features = datasets.Features({
'A': datasets.Value('string'),
'B': datasets.Value('string'),
'C': datasets.Value('string'),
'D': datasets.Value('string'),
'context': datasets.Value('string'),
'question': datasets.Value('string'),
'ex_question': datasets.Value('string'),
'group': datasets.Value('string'),
'label': datasets.ClassLabel(names=MC_LABELS),
'name': 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):
""" Returns SplitGenerators."""
path = dl_manager.download_and_extract(_URLs[self.config.name])
kwargs = {'path': path}
return [datasets.SplitGenerator(datasets.Split.TEST, gen_kwargs=kwargs)]
def _generate_examples(self, path):
with open(path, 'r') as f:
for _id, line in enumerate(f.readlines()):
yield _id, json.loads(line)
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