Datasets:
File size: 11,136 Bytes
02af0df 241ad50 02af0df 241ad50 02af0df de9e147 02af0df de9e147 02af0df de9e147 02af0df de9e147 02af0df de9e147 02af0df 241ad50 02af0df 241ad50 02af0df 241ad50 02af0df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Only Connect Wall (OCW) dataset"""
import json
import os
import datasets
_CITATION = """\
@article{Naeini2023LargeLM,
title = {Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset},
author = {Saeid Alavi Naeini and Raeid Saqur and Mozhgan Saeidi and John Giorgi and Babak Taati},
year = 2023,
journal = {ArXiv},
volume = {abs/2306.11167},
url = {https://api.semanticscholar.org/CorpusID:259203717}
}
"""
_DESCRIPTION = """\
The Only Connect Wall (OCW) dataset contains 618 "Connecting Walls" from the Round 3: Connecting Wall segment of the Only Connect quiz show, collected from 15 seasons' worth of episodes. Each wall contains the ground-truth groups and connections as well as recorded human performance.
"""
_HOMEPAGE_URL = "https://github.com/TaatiTeam/OCW/"
_LICENSE = "MIT"
_BASE_URL = "https://www.cs.toronto.edu/~taati/OCW/"
_URLS = {
"ocw": _BASE_URL + "OCW.tar.gz",
"ocw_randomized": _BASE_URL + "OCW_randomized.tar.gz",
"ocw_wordnet": _BASE_URL + "OCW_wordnet.tar.gz"
}
class OCW(datasets.GeneratorBasedBuilder):
"""OCW dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="ocw", version=VERSION,
description="main OCW dataset"),
datasets.BuilderConfig(name="ocw_randomized", version=VERSION,
description="Easy OCW dataset with randomized groups in each wall"),
datasets.BuilderConfig(name="ocw_wordnet", version=VERSION,
description="Easy OCW dataset with wordnet synonyms replaced with original clues")
]
DEFAULT_CONFIG_NAME = "ocw"
def _info(self):
features = datasets.Features(
{
# "total_walls_in_season": datasets.Value("int32"),
# "season_start_date": datasets.Value("string"),
# "season_end_date": datasets.Value("string"),
"wall_id": datasets.Value("string"),
"season": datasets.Value("int32"),
"episode": datasets.Value("int32"),
"words": datasets.features.Sequence(feature=datasets.Value("string")),
"gt_connections": datasets.features.Sequence(feature=datasets.Value("string")),
"group_1":
{
"group_id": datasets.Value("string"),
"gt_words":datasets.features.Sequence(feature=datasets.Value("string")),
"gt_connection": datasets.Value("string"),
"human_performance":
{
"grouping": datasets.Value("int32"),
"connection": datasets.Value("int32")
}
},
"group_2":
{
"group_id": datasets.Value("string"),
"gt_words": datasets.features.Sequence(feature=datasets.Value("string")),
"gt_connection": datasets.Value("string"),
"human_performance":
{
"grouping": datasets.Value("int32"),
"connection": datasets.Value("int32")
}
},
"group_3":
{
"group_id": datasets.Value("string"),
"gt_words": datasets.features.Sequence(feature=datasets.Value("string")),
"gt_connection": datasets.Value("string"),
"human_performance":
{
"grouping": datasets.Value("int32"),
"connection": datasets.Value("int32")
}
},
"group_4":
{
"group_id": datasets.Value("string"),
"gt_words": datasets.features.Sequence(feature=datasets.Value("string")),
"gt_connection": datasets.Value("string"),
"human_performance":
{
"grouping": datasets.Value("int32"),
"connection": datasets.Value("int32")
}
},
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features= features,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE_URL,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
# No default supervised_keys
supervised_keys=None
)
def _split_generators(self, dl_manager):
url = _URLS[self.config.name]
if self.config.name == "ocw_randomized":
url = [url, _URLS[self.DEFAULT_CONFIG_NAME]]
path = dl_manager.download_and_extract(url)
if self.config.name == self.DEFAULT_CONFIG_NAME:
dir = 'dataset'
train_filepath = os.path.join(path, dir, 'train.json')
val_filepath = os.path.join(path, dir, 'validation.json')
test_filepath = os.path.join(path, dir, 'test.json')
elif self.config.name == "ocw_randomized":
# OCW-randomized only contains a test set, we load main OCW train/validation files
dir = 'OCW_randomized'
dir2 = 'dataset'
train_filepath = os.path.join(path[1], dir2, 'train.json')
val_filepath = os.path.join(path[1], dir2, 'validation.json')
test_filepath = os.path.join(path[0], dir, 'easy_test.json')
else:
dir = 'OCW_wordnet'
train_filepath = os.path.join(path, dir, 'easy_train_wordnet.json')
val_filepath = os.path.join(path, dir, 'easy_validation_wordnet.json')
test_filepath = os.path.join(path, dir, 'easy_test_wordnet.json')
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_filepath}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_filepath}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_filepath}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
key = 0
with open(filepath, encoding="utf-8") as f:
ocw = json.load(f)
for data in ocw["dataset"]:
wall_id = data.get("wall_id")
season = data.get("season")
# season_to_walls_map = ocw['season_to_walls_map'][str(season)]
# total_walls_in_season = season_to_walls_map["num_walls"]
# season_start_date = season_to_walls_map["start_date"]
# season_end_date = season_to_walls_map["end_date"]
episode = data.get("episode")
words = data.get("words")
gt_connections = data.get("gt_connections")
group_1 = data['groups']['group_1']
group_1_human_performance = group_1['human_performance']
group_2 = data['groups']['group_2']
group_2_human_performance = group_2['human_performance']
group_3 = data['groups']['group_3']
group_3_human_performance = group_3['human_performance']
group_4 = data['groups']['group_4']
group_4_human_performance = group_4['human_performance']
yield key, {
# "total_walls_in_season": total_walls_in_season,
# "season_start_date": season_start_date,
# "season_end_date": season_end_date,
"wall_id": wall_id,
"season": season,
"episode": episode,
"words": words,
"gt_connections": gt_connections,
"group_1": {
"group_id": group_1.get("group_id"),
"gt_words": group_1.get("gt_words"),
"gt_connection": group_1.get("gt_connection"),
"human_performance": {
"grouping": group_1_human_performance.get("grouping"),
"connection": group_1_human_performance.get("connection")
}
},
"group_2": {
"group_id": group_2.get("group_id"),
"gt_words": group_2.get("gt_words"),
"gt_connection": group_2.get("gt_connection"),
"human_performance": {
"grouping": group_2_human_performance.get("grouping"),
"connection": group_2_human_performance.get("connection")
}
},
"group_3": {
"group_id": group_3.get("group_id"),
"gt_words": group_3.get("gt_words"),
"gt_connection": group_3.get("gt_connection"),
"human_performance": {
"grouping": group_3_human_performance.get("grouping"),
"connection": group_3_human_performance.get("connection")
}
},
"group_4": {
"group_id": group_4.get("group_id"),
"gt_words": group_4.get("gt_words"),
"gt_connection": group_4.get("gt_connection"),
"human_performance": {
"grouping": group_4_human_performance.get("grouping"),
"connection": group_4_human_performance.get("connection")
}
},
}
key += 1 |