coref-data/winogrande_raw
Wingrande v1.1 Dataset Summary WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires commonsense reasoning. Data Fields The data fields are the same among all… See the full description on the dataset page: https://huggingface.co/datasets/coref-data/winogrande_raw.
41.8k
1{2 "winogrande_xs": {3 "description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n",4 "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n",5 "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started",6 "license": "",7 "features": {8 "sentence": {9 "dtype": "string",10 "_type": "Value"11 },12 "option1": {13 "dtype": "string",14 "_type": "Value"15 },16 "option2": {17 "dtype": "string",18 "_type": "Value"19 },20 "answer": {21 "dtype": "string",22 "_type": "Value"23 }24 },25 "builder_name": "winogrande_raw",26 "dataset_name": "winogrande_raw",27 "config_name": "winogrande_xs",28 "version": {29 "version_str": "1.1.0",30 "description": "",31 "major": 1,32 "minor": 1,33 "patch": 034 },35 "splits": {36 "train": {37 "name": "train",38 "num_bytes": 20688,39 "num_examples": 160,40 "dataset_name": null41 },42 "test": {43 "name": "test",44 "num_bytes": 227633,45 "num_examples": 1767,46 "dataset_name": null47 },48 "validation": {49 "name": "validation",50 "num_bytes": 164183,51 "num_examples": 1267,52 "dataset_name": null53 }54 },55 "download_size": 215301,56 "dataset_size": 412504,57 "size_in_bytes": 62780558 },59 "winogrande_s": {60 "description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n",61 "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n",62 "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started",63 "license": "",64 "features": {65 "sentence": {66 "dtype": "string",67 "_type": "Value"68 },69 "option1": {70 "dtype": "string",71 "_type": "Value"72 },73 "option2": {74 "dtype": "string",75 "_type": "Value"76 },77 "answer": {78 "dtype": "string",79 "_type": "Value"80 }81 },82 "builder_name": "winogrande_raw",83 "dataset_name": "winogrande_raw",84 "config_name": "winogrande_s",85 "version": {86 "version_str": "1.1.0",87 "description": "",88 "major": 1,89 "minor": 1,90 "patch": 091 },92 "splits": {93 "train": {94 "name": "train",95 "num_bytes": 82292,96 "num_examples": 640,97 "dataset_name": null98 },99 "test": {100 "name": "test",101 "num_bytes": 227633,102 "num_examples": 1767,103 "dataset_name": null104 },105 "validation": {106 "name": "validation",107 "num_bytes": 164183,108 "num_examples": 1267,109 "dataset_name": null110 }111 },112 "download_size": 238397,113 "dataset_size": 474108,114 "size_in_bytes": 712505115 },116 "winogrande_m": {117 "description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n",118 "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n",119 "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started",120 "license": "",121 "features": {122 "sentence": {123 "dtype": "string",124 "_type": "Value"125 },126 "option1": {127 "dtype": "string",128 "_type": "Value"129 },130 "option2": {131 "dtype": "string",132 "_type": "Value"133 },134 "answer": {135 "dtype": "string",136 "_type": "Value"137 }138 },139 "builder_name": "winogrande_raw",140 "dataset_name": "winogrande_raw",141 "config_name": "winogrande_m",142 "version": {143 "version_str": "1.1.0",144 "description": "",145 "major": 1,146 "minor": 1,147 "patch": 0148 },149 "splits": {150 "train": {151 "name": "train",152 "num_bytes": 328985,153 "num_examples": 2558,154 "dataset_name": null155 },156 "test": {157 "name": "test",158 "num_bytes": 227633,159 "num_examples": 1767,160 "dataset_name": null161 },162 "validation": {163 "name": "validation",164 "num_bytes": 164183,165 "num_examples": 1267,166 "dataset_name": null167 }168 },169 "download_size": 337379,170 "dataset_size": 720801,171 "size_in_bytes": 1058180172 },173 "winogrande_l": {174 "description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n",175 "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n",176 "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started",177 "license": "",178 "features": {179 "sentence": {180 "dtype": "string",181 "_type": "Value"182 },183 "option1": {184 "dtype": "string",185 "_type": "Value"186 },187 "option2": {188 "dtype": "string",189 "_type": "Value"190 },191 "answer": {192 "dtype": "string",193 "_type": "Value"194 }195 },196 "builder_name": "winogrande_raw",197 "dataset_name": "winogrande_raw",198 "config_name": "winogrande_l",199 "version": {200 "version_str": "1.1.0",201 "description": "",202 "major": 1,203 "minor": 1,204 "patch": 0205 },206 "splits": {207 "train": {208 "name": "train",209 "num_bytes": 1319544,210 "num_examples": 10234,211 "dataset_name": null212 },213 "test": {214 "name": "test",215 "num_bytes": 227633,216 "num_examples": 1767,217 "dataset_name": null218 },219 "validation": {220 "name": "validation",221 "num_bytes": 164183,222 "num_examples": 1267,223 "dataset_name": null224 }225 },226 "download_size": 733064,227 "dataset_size": 1711360,228 "size_in_bytes": 2444424229 },230 "winogrande_xl": {231 "description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n",232 "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n",233 "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started",234 "license": "",235 "features": {236 "sentence": {237 "dtype": "string",238 "_type": "Value"239 },240 "option1": {241 "dtype": "string",242 "_type": "Value"243 },244 "option2": {245 "dtype": "string",246 "_type": "Value"247 },248 "answer": {249 "dtype": "string",250 "_type": "Value"251 }252 },253 "builder_name": "winogrande_raw",254 "dataset_name": "winogrande_raw",255 "config_name": "winogrande_xl",256 "version": {257 "version_str": "1.1.0",258 "description": "",259 "major": 1,260 "minor": 1,261 "patch": 0262 },263 "splits": {264 "train": {265 "name": "train",266 "num_bytes": 5185752,267 "num_examples": 40398,268 "dataset_name": null269 },270 "test": {271 "name": "test",272 "num_bytes": 227633,273 "num_examples": 1767,274 "dataset_name": null275 },276 "validation": {277 "name": "validation",278 "num_bytes": 164183,279 "num_examples": 1267,280 "dataset_name": null281 }282 },283 "download_size": 2262090,284 "dataset_size": 5577568,285 "size_in_bytes": 7839658286 },287 "winogrande_debiased": {288 "description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.\n",289 "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n",290 "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started",291 "license": "",292 "features": {293 "sentence": {294 "dtype": "string",295 "_type": "Value"296 },297 "option1": {298 "dtype": "string",299 "_type": "Value"300 },301 "option2": {302 "dtype": "string",303 "_type": "Value"304 },305 "answer": {306 "dtype": "string",307 "_type": "Value"308 }309 },310 "builder_name": "winogrande_raw",311 "dataset_name": "winogrande_raw",312 "config_name": "winogrande_debiased",313 "version": {314 "version_str": "1.1.0",315 "description": "",316 "major": 1,317 "minor": 1,318 "patch": 0319 },320 "splits": {321 "train": {322 "name": "train",323 "num_bytes": 1203404,324 "num_examples": 9248,325 "dataset_name": null326 },327 "test": {328 "name": "test",329 "num_bytes": 227633,330 "num_examples": 1767,331 "dataset_name": null332 },333 "validation": {334 "name": "validation",335 "num_bytes": 164183,336 "num_examples": 1267,337 "dataset_name": null338 }339 },340 "download_size": 820340,341 "dataset_size": 1595220,342 "size_in_bytes": 2415560343 }344}