prost.py

# 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)