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# Copyright 2021 Cory Paik. All Rights Reserved.
#
# 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.
# ==============================================================================
""" The Color Dataset (CoDa)
CoDa is a probing dataset to evaluate the representation of visual properties
in language models. CoDa consists of color distributions for 521 common
objects, which are split into 3 groups: Single, Multi, and Any.
The default configuration of CoDa uses 10 CLIP-style templates (e.g. "A photo
of a ___"), and 10 cloze-style templates (e.g. "Everyone knows most ___ are
___." )
"""
import json
import datasets
_CITATION = """\
@misc{paik2021world,
title={The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color},
author={Cory Paik and Stéphane Aroca-Ouellette and Alessandro Roncone and Katharina Kann},
year={2021},
eprint={2110.08182},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
*The Color Dataset* (CoDa) is a probing dataset to evaluate the representation of visual properties in language models. CoDa consists of color distributions for 521 common objects, which are split into 3 groups: Single, Multi, and Any.
"""
_HOMEPAGE = 'https://github.com/nala-cub/coda'
_LICENSE = 'Apache 2.0'
_URL = 'https://huggingface.co/datasets/corypaik/coda/resolve/main/data'
_URLs = {
'default': {
'train': f'{_URL}/default_train.jsonl',
'validation': f'{_URL}/default_validation.jsonl',
'test': f'{_URL}/default_test.jsonl',
}
}
class Coda(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version('1.0.1')
# TODO(corypaik): add object and annotation configs.
def _info(self):
features = datasets.Features({
'class_id':
datasets.Value('string'),
'display_name':
datasets.Value('string'),
'ngram':
datasets.Value('string'),
'label':
datasets.Sequence(datasets.Value('float')),
'object_group':
datasets.ClassLabel(names=('Single', 'Multi', 'Any')),
'text':
datasets.Value('string'),
'template_group':
datasets.ClassLabel(names=('clip-imagenet', 'text-masked')),
'template_idx':
datasets.Value('int32')
})
return datasets.DatasetInfo(description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION)
def _split_generators(self, dl_manager):
""" Returns SplitGenerators."""
files = dl_manager.download_and_extract(_URLs[self.config.name])
return [
datasets.SplitGenerator(datasets.Split.TRAIN,
gen_kwargs={'path': files['train']}),
datasets.SplitGenerator(datasets.Split.VALIDATION,
gen_kwargs={'path': files['validation']}),
datasets.SplitGenerator(datasets.Split.TEST,
gen_kwargs={'path': files['test']}),
]
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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