Datasets:
Commit
•
3970f16
1
Parent(s):
6d026e8
Upload README.md with huggingface_hub
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README.md
CHANGED
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@@ -1,5 +1,2252 @@
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| 2 |
dataset_info:
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|
|
| 3 |
features:
|
| 4 |
- name: sample
|
| 5 |
dtype: binary
|
|
@@ -9,9 +2256,72 @@ dataset_info:
|
|
| 9 |
num_examples: 1200
|
| 10 |
download_size: 3903019328
|
| 11 |
dataset_size: 5138422400
|
| 12 |
-
configs:
|
| 13 |
-
- config_name: default
|
| 14 |
-
data_files:
|
| 15 |
-
- split: all_samples
|
| 16 |
-
path: data/all_samples-*
|
| 17 |
---
|
|
|
|
|
|
|
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|
|
| 1 |
---
|
| 2 |
+
license: cc-by-sa-4.0
|
| 3 |
+
size_categories:
|
| 4 |
+
- 1K<n<10K
|
| 5 |
+
task_categories:
|
| 6 |
+
- graph-ml
|
| 7 |
+
pretty_name: 3D RANS simulations of the rotor37
|
| 8 |
+
tags:
|
| 9 |
+
- physics learning
|
| 10 |
+
- geometry learning
|
| 11 |
+
configs:
|
| 12 |
+
- config_name: default
|
| 13 |
+
data_files:
|
| 14 |
+
- split: all_samples
|
| 15 |
+
path: data/all_samples-*
|
| 16 |
dataset_info:
|
| 17 |
+
description:
|
| 18 |
+
legal:
|
| 19 |
+
owner: Safran
|
| 20 |
+
license: CC-BY-SA
|
| 21 |
+
data_production:
|
| 22 |
+
type: simulation
|
| 23 |
+
physics: 3D CFD RANS compressor blade
|
| 24 |
+
split:
|
| 25 |
+
test:
|
| 26 |
+
- 1000
|
| 27 |
+
- 1001
|
| 28 |
+
- 1002
|
| 29 |
+
- 1003
|
| 30 |
+
- 1004
|
| 31 |
+
- 1005
|
| 32 |
+
- 1006
|
| 33 |
+
- 1007
|
| 34 |
+
- 1008
|
| 35 |
+
- 1009
|
| 36 |
+
- 1010
|
| 37 |
+
- 1011
|
| 38 |
+
- 1012
|
| 39 |
+
- 1013
|
| 40 |
+
- 1014
|
| 41 |
+
- 1015
|
| 42 |
+
- 1016
|
| 43 |
+
- 1017
|
| 44 |
+
- 1018
|
| 45 |
+
- 1019
|
| 46 |
+
- 1020
|
| 47 |
+
- 1021
|
| 48 |
+
- 1022
|
| 49 |
+
- 1023
|
| 50 |
+
- 1024
|
| 51 |
+
- 1025
|
| 52 |
+
- 1026
|
| 53 |
+
- 1027
|
| 54 |
+
- 1028
|
| 55 |
+
- 1029
|
| 56 |
+
- 1030
|
| 57 |
+
- 1031
|
| 58 |
+
- 1032
|
| 59 |
+
- 1033
|
| 60 |
+
- 1034
|
| 61 |
+
- 1035
|
| 62 |
+
- 1036
|
| 63 |
+
- 1037
|
| 64 |
+
- 1038
|
| 65 |
+
- 1039
|
| 66 |
+
- 1040
|
| 67 |
+
- 1041
|
| 68 |
+
- 1042
|
| 69 |
+
- 1043
|
| 70 |
+
- 1044
|
| 71 |
+
- 1045
|
| 72 |
+
- 1046
|
| 73 |
+
- 1047
|
| 74 |
+
- 1048
|
| 75 |
+
- 1049
|
| 76 |
+
- 1050
|
| 77 |
+
- 1051
|
| 78 |
+
- 1052
|
| 79 |
+
- 1053
|
| 80 |
+
- 1054
|
| 81 |
+
- 1055
|
| 82 |
+
- 1056
|
| 83 |
+
- 1057
|
| 84 |
+
- 1058
|
| 85 |
+
- 1059
|
| 86 |
+
- 1060
|
| 87 |
+
- 1061
|
| 88 |
+
- 1062
|
| 89 |
+
- 1063
|
| 90 |
+
- 1064
|
| 91 |
+
- 1065
|
| 92 |
+
- 1066
|
| 93 |
+
- 1067
|
| 94 |
+
- 1068
|
| 95 |
+
- 1069
|
| 96 |
+
- 1070
|
| 97 |
+
- 1071
|
| 98 |
+
- 1072
|
| 99 |
+
- 1073
|
| 100 |
+
- 1074
|
| 101 |
+
- 1075
|
| 102 |
+
- 1076
|
| 103 |
+
- 1077
|
| 104 |
+
- 1078
|
| 105 |
+
- 1079
|
| 106 |
+
- 1080
|
| 107 |
+
- 1081
|
| 108 |
+
- 1082
|
| 109 |
+
- 1083
|
| 110 |
+
- 1084
|
| 111 |
+
- 1085
|
| 112 |
+
- 1086
|
| 113 |
+
- 1087
|
| 114 |
+
- 1088
|
| 115 |
+
- 1089
|
| 116 |
+
- 1090
|
| 117 |
+
- 1091
|
| 118 |
+
- 1092
|
| 119 |
+
- 1093
|
| 120 |
+
- 1094
|
| 121 |
+
- 1095
|
| 122 |
+
- 1096
|
| 123 |
+
- 1097
|
| 124 |
+
- 1098
|
| 125 |
+
- 1099
|
| 126 |
+
- 1100
|
| 127 |
+
- 1101
|
| 128 |
+
- 1102
|
| 129 |
+
- 1103
|
| 130 |
+
- 1104
|
| 131 |
+
- 1105
|
| 132 |
+
- 1106
|
| 133 |
+
- 1107
|
| 134 |
+
- 1108
|
| 135 |
+
- 1109
|
| 136 |
+
- 1110
|
| 137 |
+
- 1111
|
| 138 |
+
- 1112
|
| 139 |
+
- 1113
|
| 140 |
+
- 1114
|
| 141 |
+
- 1115
|
| 142 |
+
- 1116
|
| 143 |
+
- 1117
|
| 144 |
+
- 1118
|
| 145 |
+
- 1119
|
| 146 |
+
- 1120
|
| 147 |
+
- 1121
|
| 148 |
+
- 1122
|
| 149 |
+
- 1123
|
| 150 |
+
- 1124
|
| 151 |
+
- 1125
|
| 152 |
+
- 1126
|
| 153 |
+
- 1127
|
| 154 |
+
- 1128
|
| 155 |
+
- 1129
|
| 156 |
+
- 1130
|
| 157 |
+
- 1131
|
| 158 |
+
- 1132
|
| 159 |
+
- 1133
|
| 160 |
+
- 1134
|
| 161 |
+
- 1135
|
| 162 |
+
- 1136
|
| 163 |
+
- 1137
|
| 164 |
+
- 1138
|
| 165 |
+
- 1139
|
| 166 |
+
- 1140
|
| 167 |
+
- 1141
|
| 168 |
+
- 1142
|
| 169 |
+
- 1143
|
| 170 |
+
- 1144
|
| 171 |
+
- 1145
|
| 172 |
+
- 1146
|
| 173 |
+
- 1147
|
| 174 |
+
- 1148
|
| 175 |
+
- 1149
|
| 176 |
+
- 1150
|
| 177 |
+
- 1151
|
| 178 |
+
- 1152
|
| 179 |
+
- 1153
|
| 180 |
+
- 1154
|
| 181 |
+
- 1155
|
| 182 |
+
- 1156
|
| 183 |
+
- 1157
|
| 184 |
+
- 1158
|
| 185 |
+
- 1159
|
| 186 |
+
- 1160
|
| 187 |
+
- 1161
|
| 188 |
+
- 1162
|
| 189 |
+
- 1163
|
| 190 |
+
- 1164
|
| 191 |
+
- 1165
|
| 192 |
+
- 1166
|
| 193 |
+
- 1167
|
| 194 |
+
- 1168
|
| 195 |
+
- 1169
|
| 196 |
+
- 1170
|
| 197 |
+
- 1171
|
| 198 |
+
- 1172
|
| 199 |
+
- 1173
|
| 200 |
+
- 1174
|
| 201 |
+
- 1175
|
| 202 |
+
- 1176
|
| 203 |
+
- 1177
|
| 204 |
+
- 1178
|
| 205 |
+
- 1179
|
| 206 |
+
- 1180
|
| 207 |
+
- 1181
|
| 208 |
+
- 1182
|
| 209 |
+
- 1183
|
| 210 |
+
- 1184
|
| 211 |
+
- 1185
|
| 212 |
+
- 1186
|
| 213 |
+
- 1187
|
| 214 |
+
- 1188
|
| 215 |
+
- 1189
|
| 216 |
+
- 1190
|
| 217 |
+
- 1191
|
| 218 |
+
- 1192
|
| 219 |
+
- 1193
|
| 220 |
+
- 1194
|
| 221 |
+
- 1195
|
| 222 |
+
- 1196
|
| 223 |
+
- 1197
|
| 224 |
+
- 1198
|
| 225 |
+
- 1199
|
| 226 |
+
train_8:
|
| 227 |
+
- 154
|
| 228 |
+
- 174
|
| 229 |
+
- 383
|
| 230 |
+
- 501
|
| 231 |
+
- 524
|
| 232 |
+
- 593
|
| 233 |
+
- 711
|
| 234 |
+
- 732
|
| 235 |
+
train_16:
|
| 236 |
+
- 76
|
| 237 |
+
- 124
|
| 238 |
+
- 130
|
| 239 |
+
- 154
|
| 240 |
+
- 157
|
| 241 |
+
- 174
|
| 242 |
+
- 383
|
| 243 |
+
- 501
|
| 244 |
+
- 524
|
| 245 |
+
- 593
|
| 246 |
+
- 711
|
| 247 |
+
- 732
|
| 248 |
+
- 798
|
| 249 |
+
- 800
|
| 250 |
+
- 959
|
| 251 |
+
- 987
|
| 252 |
+
train_32:
|
| 253 |
+
- 3
|
| 254 |
+
- 23
|
| 255 |
+
- 76
|
| 256 |
+
- 124
|
| 257 |
+
- 130
|
| 258 |
+
- 154
|
| 259 |
+
- 157
|
| 260 |
+
- 174
|
| 261 |
+
- 190
|
| 262 |
+
- 316
|
| 263 |
+
- 324
|
| 264 |
+
- 339
|
| 265 |
+
- 383
|
| 266 |
+
- 469
|
| 267 |
+
- 501
|
| 268 |
+
- 524
|
| 269 |
+
- 556
|
| 270 |
+
- 593
|
| 271 |
+
- 606
|
| 272 |
+
- 616
|
| 273 |
+
- 662
|
| 274 |
+
- 673
|
| 275 |
+
- 711
|
| 276 |
+
- 732
|
| 277 |
+
- 757
|
| 278 |
+
- 798
|
| 279 |
+
- 800
|
| 280 |
+
- 846
|
| 281 |
+
- 909
|
| 282 |
+
- 927
|
| 283 |
+
- 959
|
| 284 |
+
- 987
|
| 285 |
+
train_64:
|
| 286 |
+
- 3
|
| 287 |
+
- 23
|
| 288 |
+
- 34
|
| 289 |
+
- 56
|
| 290 |
+
- 71
|
| 291 |
+
- 76
|
| 292 |
+
- 124
|
| 293 |
+
- 130
|
| 294 |
+
- 145
|
| 295 |
+
- 154
|
| 296 |
+
- 157
|
| 297 |
+
- 174
|
| 298 |
+
- 187
|
| 299 |
+
- 190
|
| 300 |
+
- 201
|
| 301 |
+
- 210
|
| 302 |
+
- 223
|
| 303 |
+
- 288
|
| 304 |
+
- 316
|
| 305 |
+
- 324
|
| 306 |
+
- 335
|
| 307 |
+
- 339
|
| 308 |
+
- 376
|
| 309 |
+
- 379
|
| 310 |
+
- 383
|
| 311 |
+
- 389
|
| 312 |
+
- 414
|
| 313 |
+
- 444
|
| 314 |
+
- 469
|
| 315 |
+
- 490
|
| 316 |
+
- 501
|
| 317 |
+
- 519
|
| 318 |
+
- 524
|
| 319 |
+
- 556
|
| 320 |
+
- 557
|
| 321 |
+
- 572
|
| 322 |
+
- 575
|
| 323 |
+
- 589
|
| 324 |
+
- 593
|
| 325 |
+
- 606
|
| 326 |
+
- 616
|
| 327 |
+
- 662
|
| 328 |
+
- 673
|
| 329 |
+
- 692
|
| 330 |
+
- 711
|
| 331 |
+
- 732
|
| 332 |
+
- 741
|
| 333 |
+
- 757
|
| 334 |
+
- 760
|
| 335 |
+
- 772
|
| 336 |
+
- 781
|
| 337 |
+
- 782
|
| 338 |
+
- 798
|
| 339 |
+
- 800
|
| 340 |
+
- 806
|
| 341 |
+
- 836
|
| 342 |
+
- 846
|
| 343 |
+
- 852
|
| 344 |
+
- 896
|
| 345 |
+
- 909
|
| 346 |
+
- 927
|
| 347 |
+
- 959
|
| 348 |
+
- 985
|
| 349 |
+
- 987
|
| 350 |
+
train_125:
|
| 351 |
+
- 3
|
| 352 |
+
- 6
|
| 353 |
+
- 16
|
| 354 |
+
- 20
|
| 355 |
+
- 23
|
| 356 |
+
- 34
|
| 357 |
+
- 56
|
| 358 |
+
- 71
|
| 359 |
+
- 76
|
| 360 |
+
- 78
|
| 361 |
+
- 83
|
| 362 |
+
- 102
|
| 363 |
+
- 105
|
| 364 |
+
- 124
|
| 365 |
+
- 130
|
| 366 |
+
- 145
|
| 367 |
+
- 152
|
| 368 |
+
- 154
|
| 369 |
+
- 157
|
| 370 |
+
- 160
|
| 371 |
+
- 173
|
| 372 |
+
- 174
|
| 373 |
+
- 180
|
| 374 |
+
- 187
|
| 375 |
+
- 190
|
| 376 |
+
- 198
|
| 377 |
+
- 201
|
| 378 |
+
- 204
|
| 379 |
+
- 210
|
| 380 |
+
- 223
|
| 381 |
+
- 224
|
| 382 |
+
- 229
|
| 383 |
+
- 233
|
| 384 |
+
- 251
|
| 385 |
+
- 278
|
| 386 |
+
- 288
|
| 387 |
+
- 289
|
| 388 |
+
- 316
|
| 389 |
+
- 317
|
| 390 |
+
- 324
|
| 391 |
+
- 335
|
| 392 |
+
- 339
|
| 393 |
+
- 371
|
| 394 |
+
- 375
|
| 395 |
+
- 376
|
| 396 |
+
- 377
|
| 397 |
+
- 379
|
| 398 |
+
- 383
|
| 399 |
+
- 389
|
| 400 |
+
- 414
|
| 401 |
+
- 426
|
| 402 |
+
- 428
|
| 403 |
+
- 431
|
| 404 |
+
- 443
|
| 405 |
+
- 444
|
| 406 |
+
- 463
|
| 407 |
+
- 469
|
| 408 |
+
- 471
|
| 409 |
+
- 490
|
| 410 |
+
- 501
|
| 411 |
+
- 513
|
| 412 |
+
- 516
|
| 413 |
+
- 519
|
| 414 |
+
- 524
|
| 415 |
+
- 525
|
| 416 |
+
- 527
|
| 417 |
+
- 530
|
| 418 |
+
- 556
|
| 419 |
+
- 557
|
| 420 |
+
- 558
|
| 421 |
+
- 572
|
| 422 |
+
- 573
|
| 423 |
+
- 575
|
| 424 |
+
- 589
|
| 425 |
+
- 593
|
| 426 |
+
- 606
|
| 427 |
+
- 612
|
| 428 |
+
- 616
|
| 429 |
+
- 624
|
| 430 |
+
- 628
|
| 431 |
+
- 638
|
| 432 |
+
- 662
|
| 433 |
+
- 673
|
| 434 |
+
- 681
|
| 435 |
+
- 692
|
| 436 |
+
- 699
|
| 437 |
+
- 711
|
| 438 |
+
- 713
|
| 439 |
+
- 715
|
| 440 |
+
- 732
|
| 441 |
+
- 741
|
| 442 |
+
- 754
|
| 443 |
+
- 757
|
| 444 |
+
- 760
|
| 445 |
+
- 763
|
| 446 |
+
- 769
|
| 447 |
+
- 772
|
| 448 |
+
- 779
|
| 449 |
+
- 781
|
| 450 |
+
- 782
|
| 451 |
+
- 783
|
| 452 |
+
- 784
|
| 453 |
+
- 798
|
| 454 |
+
- 800
|
| 455 |
+
- 806
|
| 456 |
+
- 812
|
| 457 |
+
- 834
|
| 458 |
+
- 836
|
| 459 |
+
- 843
|
| 460 |
+
- 846
|
| 461 |
+
- 852
|
| 462 |
+
- 854
|
| 463 |
+
- 866
|
| 464 |
+
- 872
|
| 465 |
+
- 877
|
| 466 |
+
- 896
|
| 467 |
+
- 909
|
| 468 |
+
- 927
|
| 469 |
+
- 931
|
| 470 |
+
- 939
|
| 471 |
+
- 956
|
| 472 |
+
- 959
|
| 473 |
+
- 985
|
| 474 |
+
- 987
|
| 475 |
+
- 996
|
| 476 |
+
train_250:
|
| 477 |
+
- 3
|
| 478 |
+
- 6
|
| 479 |
+
- 7
|
| 480 |
+
- 16
|
| 481 |
+
- 20
|
| 482 |
+
- 21
|
| 483 |
+
- 22
|
| 484 |
+
- 23
|
| 485 |
+
- 29
|
| 486 |
+
- 33
|
| 487 |
+
- 34
|
| 488 |
+
- 39
|
| 489 |
+
- 46
|
| 490 |
+
- 56
|
| 491 |
+
- 57
|
| 492 |
+
- 71
|
| 493 |
+
- 76
|
| 494 |
+
- 77
|
| 495 |
+
- 78
|
| 496 |
+
- 81
|
| 497 |
+
- 83
|
| 498 |
+
- 95
|
| 499 |
+
- 99
|
| 500 |
+
- 101
|
| 501 |
+
- 102
|
| 502 |
+
- 105
|
| 503 |
+
- 115
|
| 504 |
+
- 117
|
| 505 |
+
- 124
|
| 506 |
+
- 130
|
| 507 |
+
- 143
|
| 508 |
+
- 145
|
| 509 |
+
- 152
|
| 510 |
+
- 154
|
| 511 |
+
- 157
|
| 512 |
+
- 159
|
| 513 |
+
- 160
|
| 514 |
+
- 167
|
| 515 |
+
- 173
|
| 516 |
+
- 174
|
| 517 |
+
- 180
|
| 518 |
+
- 182
|
| 519 |
+
- 187
|
| 520 |
+
- 190
|
| 521 |
+
- 196
|
| 522 |
+
- 198
|
| 523 |
+
- 201
|
| 524 |
+
- 203
|
| 525 |
+
- 204
|
| 526 |
+
- 210
|
| 527 |
+
- 212
|
| 528 |
+
- 217
|
| 529 |
+
- 220
|
| 530 |
+
- 223
|
| 531 |
+
- 224
|
| 532 |
+
- 229
|
| 533 |
+
- 233
|
| 534 |
+
- 246
|
| 535 |
+
- 247
|
| 536 |
+
- 250
|
| 537 |
+
- 251
|
| 538 |
+
- 252
|
| 539 |
+
- 264
|
| 540 |
+
- 268
|
| 541 |
+
- 270
|
| 542 |
+
- 278
|
| 543 |
+
- 288
|
| 544 |
+
- 289
|
| 545 |
+
- 300
|
| 546 |
+
- 312
|
| 547 |
+
- 314
|
| 548 |
+
- 316
|
| 549 |
+
- 317
|
| 550 |
+
- 319
|
| 551 |
+
- 320
|
| 552 |
+
- 324
|
| 553 |
+
- 334
|
| 554 |
+
- 335
|
| 555 |
+
- 337
|
| 556 |
+
- 339
|
| 557 |
+
- 348
|
| 558 |
+
- 356
|
| 559 |
+
- 357
|
| 560 |
+
- 359
|
| 561 |
+
- 367
|
| 562 |
+
- 369
|
| 563 |
+
- 370
|
| 564 |
+
- 371
|
| 565 |
+
- 375
|
| 566 |
+
- 376
|
| 567 |
+
- 377
|
| 568 |
+
- 379
|
| 569 |
+
- 383
|
| 570 |
+
- 389
|
| 571 |
+
- 395
|
| 572 |
+
- 396
|
| 573 |
+
- 398
|
| 574 |
+
- 400
|
| 575 |
+
- 404
|
| 576 |
+
- 405
|
| 577 |
+
- 408
|
| 578 |
+
- 413
|
| 579 |
+
- 414
|
| 580 |
+
- 415
|
| 581 |
+
- 416
|
| 582 |
+
- 420
|
| 583 |
+
- 426
|
| 584 |
+
- 428
|
| 585 |
+
- 431
|
| 586 |
+
- 435
|
| 587 |
+
- 436
|
| 588 |
+
- 441
|
| 589 |
+
- 443
|
| 590 |
+
- 444
|
| 591 |
+
- 449
|
| 592 |
+
- 452
|
| 593 |
+
- 463
|
| 594 |
+
- 468
|
| 595 |
+
- 469
|
| 596 |
+
- 471
|
| 597 |
+
- 472
|
| 598 |
+
- 479
|
| 599 |
+
- 483
|
| 600 |
+
- 490
|
| 601 |
+
- 501
|
| 602 |
+
- 512
|
| 603 |
+
- 513
|
| 604 |
+
- 516
|
| 605 |
+
- 518
|
| 606 |
+
- 519
|
| 607 |
+
- 523
|
| 608 |
+
- 524
|
| 609 |
+
- 525
|
| 610 |
+
- 526
|
| 611 |
+
- 527
|
| 612 |
+
- 528
|
| 613 |
+
- 530
|
| 614 |
+
- 532
|
| 615 |
+
- 553
|
| 616 |
+
- 556
|
| 617 |
+
- 557
|
| 618 |
+
- 558
|
| 619 |
+
- 561
|
| 620 |
+
- 567
|
| 621 |
+
- 568
|
| 622 |
+
- 570
|
| 623 |
+
- 572
|
| 624 |
+
- 573
|
| 625 |
+
- 575
|
| 626 |
+
- 589
|
| 627 |
+
- 593
|
| 628 |
+
- 595
|
| 629 |
+
- 597
|
| 630 |
+
- 601
|
| 631 |
+
- 606
|
| 632 |
+
- 612
|
| 633 |
+
- 616
|
| 634 |
+
- 621
|
| 635 |
+
- 622
|
| 636 |
+
- 624
|
| 637 |
+
- 628
|
| 638 |
+
- 629
|
| 639 |
+
- 631
|
| 640 |
+
- 638
|
| 641 |
+
- 641
|
| 642 |
+
- 643
|
| 643 |
+
- 647
|
| 644 |
+
- 648
|
| 645 |
+
- 657
|
| 646 |
+
- 662
|
| 647 |
+
- 663
|
| 648 |
+
- 673
|
| 649 |
+
- 677
|
| 650 |
+
- 681
|
| 651 |
+
- 692
|
| 652 |
+
- 699
|
| 653 |
+
- 703
|
| 654 |
+
- 704
|
| 655 |
+
- 705
|
| 656 |
+
- 711
|
| 657 |
+
- 713
|
| 658 |
+
- 715
|
| 659 |
+
- 721
|
| 660 |
+
- 728
|
| 661 |
+
- 731
|
| 662 |
+
- 732
|
| 663 |
+
- 741
|
| 664 |
+
- 742
|
| 665 |
+
- 747
|
| 666 |
+
- 754
|
| 667 |
+
- 757
|
| 668 |
+
- 760
|
| 669 |
+
- 763
|
| 670 |
+
- 766
|
| 671 |
+
- 769
|
| 672 |
+
- 772
|
| 673 |
+
- 779
|
| 674 |
+
- 781
|
| 675 |
+
- 782
|
| 676 |
+
- 783
|
| 677 |
+
- 784
|
| 678 |
+
- 798
|
| 679 |
+
- 800
|
| 680 |
+
- 806
|
| 681 |
+
- 812
|
| 682 |
+
- 813
|
| 683 |
+
- 816
|
| 684 |
+
- 823
|
| 685 |
+
- 826
|
| 686 |
+
- 832
|
| 687 |
+
- 833
|
| 688 |
+
- 834
|
| 689 |
+
- 836
|
| 690 |
+
- 842
|
| 691 |
+
- 843
|
| 692 |
+
- 846
|
| 693 |
+
- 852
|
| 694 |
+
- 854
|
| 695 |
+
- 857
|
| 696 |
+
- 864
|
| 697 |
+
- 866
|
| 698 |
+
- 871
|
| 699 |
+
- 872
|
| 700 |
+
- 876
|
| 701 |
+
- 877
|
| 702 |
+
- 884
|
| 703 |
+
- 892
|
| 704 |
+
- 896
|
| 705 |
+
- 901
|
| 706 |
+
- 909
|
| 707 |
+
- 922
|
| 708 |
+
- 927
|
| 709 |
+
- 931
|
| 710 |
+
- 936
|
| 711 |
+
- 937
|
| 712 |
+
- 939
|
| 713 |
+
- 946
|
| 714 |
+
- 956
|
| 715 |
+
- 959
|
| 716 |
+
- 965
|
| 717 |
+
- 975
|
| 718 |
+
- 978
|
| 719 |
+
- 982
|
| 720 |
+
- 985
|
| 721 |
+
- 987
|
| 722 |
+
- 993
|
| 723 |
+
- 994
|
| 724 |
+
- 995
|
| 725 |
+
- 996
|
| 726 |
+
- 999
|
| 727 |
+
train_500:
|
| 728 |
+
- 1
|
| 729 |
+
- 2
|
| 730 |
+
- 3
|
| 731 |
+
- 6
|
| 732 |
+
- 7
|
| 733 |
+
- 9
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+
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+
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|
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+
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|
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+
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|
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+
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|
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|
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+
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|
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|
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+
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+
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
- 767
|
| 1108 |
+
- 768
|
| 1109 |
+
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|
| 1110 |
+
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|
| 1111 |
+
- 771
|
| 1112 |
+
- 772
|
| 1113 |
+
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|
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+
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|
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+
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|
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+
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|
| 1117 |
+
- 780
|
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+
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|
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+
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|
| 1120 |
+
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|
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+
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|
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+
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|
| 1123 |
+
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|
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+
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|
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+
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|
| 1126 |
+
- 798
|
| 1127 |
+
- 800
|
| 1128 |
+
- 802
|
| 1129 |
+
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|
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+
- 806
|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
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|
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+
- 826
|
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+
- 827
|
| 1142 |
+
- 829
|
| 1143 |
+
- 832
|
| 1144 |
+
- 833
|
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+
- 834
|
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+
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|
| 1147 |
+
- 842
|
| 1148 |
+
- 843
|
| 1149 |
+
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|
| 1150 |
+
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|
| 1151 |
+
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|
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+
- 852
|
| 1153 |
+
- 853
|
| 1154 |
+
- 854
|
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+
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|
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+
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|
| 1157 |
+
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|
| 1158 |
+
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|
| 1159 |
+
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|
| 1160 |
+
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|
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+
- 863
|
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+
- 864
|
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+
- 866
|
| 1164 |
+
- 870
|
| 1165 |
+
- 871
|
| 1166 |
+
- 872
|
| 1167 |
+
- 873
|
| 1168 |
+
- 876
|
| 1169 |
+
- 877
|
| 1170 |
+
- 880
|
| 1171 |
+
- 882
|
| 1172 |
+
- 884
|
| 1173 |
+
- 887
|
| 1174 |
+
- 888
|
| 1175 |
+
- 891
|
| 1176 |
+
- 892
|
| 1177 |
+
- 893
|
| 1178 |
+
- 894
|
| 1179 |
+
- 896
|
| 1180 |
+
- 898
|
| 1181 |
+
- 900
|
| 1182 |
+
- 901
|
| 1183 |
+
- 902
|
| 1184 |
+
- 906
|
| 1185 |
+
- 909
|
| 1186 |
+
- 914
|
| 1187 |
+
- 920
|
| 1188 |
+
- 921
|
| 1189 |
+
- 922
|
| 1190 |
+
- 925
|
| 1191 |
+
- 927
|
| 1192 |
+
- 931
|
| 1193 |
+
- 936
|
| 1194 |
+
- 937
|
| 1195 |
+
- 938
|
| 1196 |
+
- 939
|
| 1197 |
+
- 942
|
| 1198 |
+
- 946
|
| 1199 |
+
- 950
|
| 1200 |
+
- 951
|
| 1201 |
+
- 952
|
| 1202 |
+
- 955
|
| 1203 |
+
- 956
|
| 1204 |
+
- 957
|
| 1205 |
+
- 959
|
| 1206 |
+
- 964
|
| 1207 |
+
- 965
|
| 1208 |
+
- 966
|
| 1209 |
+
- 969
|
| 1210 |
+
- 972
|
| 1211 |
+
- 975
|
| 1212 |
+
- 978
|
| 1213 |
+
- 980
|
| 1214 |
+
- 981
|
| 1215 |
+
- 982
|
| 1216 |
+
- 983
|
| 1217 |
+
- 985
|
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+
- 986
|
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+
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|
| 1220 |
+
- 988
|
| 1221 |
+
- 989
|
| 1222 |
+
- 990
|
| 1223 |
+
- 993
|
| 1224 |
+
- 994
|
| 1225 |
+
- 995
|
| 1226 |
+
- 996
|
| 1227 |
+
- 999
|
| 1228 |
+
train_1000:
|
| 1229 |
+
- 0
|
| 1230 |
+
- 1
|
| 1231 |
+
- 2
|
| 1232 |
+
- 3
|
| 1233 |
+
- 4
|
| 1234 |
+
- 5
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|
| 1747 |
+
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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- 588
|
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- 589
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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- 619
|
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- 620
|
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+
- 621
|
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|
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- 623
|
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|
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|
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|
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|
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- 628
|
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|
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- 630
|
| 1860 |
+
- 631
|
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+
- 632
|
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|
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- 634
|
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- 635
|
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|
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- 637
|
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|
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|
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- 640
|
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- 641
|
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|
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- 643
|
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|
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|
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|
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|
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|
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|
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- 650
|
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- 651
|
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- 652
|
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+
- 653
|
| 1883 |
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- 654
|
| 1884 |
+
- 655
|
| 1885 |
+
- 656
|
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+
- 657
|
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+
- 658
|
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|
| 1889 |
+
- 660
|
| 1890 |
+
- 661
|
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+
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|
| 1892 |
+
- 663
|
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+
- 664
|
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+
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|
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+
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|
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|
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|
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|
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|
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|
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|
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- 675
|
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+
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|
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|
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|
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- 679
|
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- 680
|
| 1910 |
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- 681
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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+
- 700
|
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|
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+
- 702
|
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|
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|
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|
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|
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+
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|
| 1937 |
+
- 708
|
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+
- 709
|
| 1939 |
+
- 710
|
| 1940 |
+
- 711
|
| 1941 |
+
- 712
|
| 1942 |
+
- 713
|
| 1943 |
+
- 714
|
| 1944 |
+
- 715
|
| 1945 |
+
- 716
|
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+
- 717
|
| 1947 |
+
- 718
|
| 1948 |
+
- 719
|
| 1949 |
+
- 720
|
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+
- 721
|
| 1951 |
+
- 722
|
| 1952 |
+
- 723
|
| 1953 |
+
- 724
|
| 1954 |
+
- 725
|
| 1955 |
+
- 726
|
| 1956 |
+
- 727
|
| 1957 |
+
- 728
|
| 1958 |
+
- 729
|
| 1959 |
+
- 730
|
| 1960 |
+
- 731
|
| 1961 |
+
- 732
|
| 1962 |
+
- 733
|
| 1963 |
+
- 734
|
| 1964 |
+
- 735
|
| 1965 |
+
- 736
|
| 1966 |
+
- 737
|
| 1967 |
+
- 738
|
| 1968 |
+
- 739
|
| 1969 |
+
- 740
|
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+
- 741
|
| 1971 |
+
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|
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+
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|
| 1973 |
+
- 744
|
| 1974 |
+
- 745
|
| 1975 |
+
- 746
|
| 1976 |
+
- 747
|
| 1977 |
+
- 748
|
| 1978 |
+
- 749
|
| 1979 |
+
- 750
|
| 1980 |
+
- 751
|
| 1981 |
+
- 752
|
| 1982 |
+
- 753
|
| 1983 |
+
- 754
|
| 1984 |
+
- 755
|
| 1985 |
+
- 756
|
| 1986 |
+
- 757
|
| 1987 |
+
- 758
|
| 1988 |
+
- 759
|
| 1989 |
+
- 760
|
| 1990 |
+
- 761
|
| 1991 |
+
- 762
|
| 1992 |
+
- 763
|
| 1993 |
+
- 764
|
| 1994 |
+
- 765
|
| 1995 |
+
- 766
|
| 1996 |
+
- 767
|
| 1997 |
+
- 768
|
| 1998 |
+
- 769
|
| 1999 |
+
- 770
|
| 2000 |
+
- 771
|
| 2001 |
+
- 772
|
| 2002 |
+
- 773
|
| 2003 |
+
- 774
|
| 2004 |
+
- 775
|
| 2005 |
+
- 776
|
| 2006 |
+
- 777
|
| 2007 |
+
- 778
|
| 2008 |
+
- 779
|
| 2009 |
+
- 780
|
| 2010 |
+
- 781
|
| 2011 |
+
- 782
|
| 2012 |
+
- 783
|
| 2013 |
+
- 784
|
| 2014 |
+
- 785
|
| 2015 |
+
- 786
|
| 2016 |
+
- 787
|
| 2017 |
+
- 788
|
| 2018 |
+
- 789
|
| 2019 |
+
- 790
|
| 2020 |
+
- 791
|
| 2021 |
+
- 792
|
| 2022 |
+
- 793
|
| 2023 |
+
- 794
|
| 2024 |
+
- 795
|
| 2025 |
+
- 796
|
| 2026 |
+
- 797
|
| 2027 |
+
- 798
|
| 2028 |
+
- 799
|
| 2029 |
+
- 800
|
| 2030 |
+
- 801
|
| 2031 |
+
- 802
|
| 2032 |
+
- 803
|
| 2033 |
+
- 804
|
| 2034 |
+
- 805
|
| 2035 |
+
- 806
|
| 2036 |
+
- 807
|
| 2037 |
+
- 808
|
| 2038 |
+
- 809
|
| 2039 |
+
- 810
|
| 2040 |
+
- 811
|
| 2041 |
+
- 812
|
| 2042 |
+
- 813
|
| 2043 |
+
- 814
|
| 2044 |
+
- 815
|
| 2045 |
+
- 816
|
| 2046 |
+
- 817
|
| 2047 |
+
- 818
|
| 2048 |
+
- 819
|
| 2049 |
+
- 820
|
| 2050 |
+
- 821
|
| 2051 |
+
- 822
|
| 2052 |
+
- 823
|
| 2053 |
+
- 824
|
| 2054 |
+
- 825
|
| 2055 |
+
- 826
|
| 2056 |
+
- 827
|
| 2057 |
+
- 828
|
| 2058 |
+
- 829
|
| 2059 |
+
- 830
|
| 2060 |
+
- 831
|
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+
- 832
|
| 2062 |
+
- 833
|
| 2063 |
+
- 834
|
| 2064 |
+
- 835
|
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+
- 836
|
| 2066 |
+
- 837
|
| 2067 |
+
- 838
|
| 2068 |
+
- 839
|
| 2069 |
+
- 840
|
| 2070 |
+
- 841
|
| 2071 |
+
- 842
|
| 2072 |
+
- 843
|
| 2073 |
+
- 844
|
| 2074 |
+
- 845
|
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+
- 846
|
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+
- 847
|
| 2077 |
+
- 848
|
| 2078 |
+
- 849
|
| 2079 |
+
- 850
|
| 2080 |
+
- 851
|
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+
- 852
|
| 2082 |
+
- 853
|
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+
- 854
|
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+
- 855
|
| 2085 |
+
- 856
|
| 2086 |
+
- 857
|
| 2087 |
+
- 858
|
| 2088 |
+
- 859
|
| 2089 |
+
- 860
|
| 2090 |
+
- 861
|
| 2091 |
+
- 862
|
| 2092 |
+
- 863
|
| 2093 |
+
- 864
|
| 2094 |
+
- 865
|
| 2095 |
+
- 866
|
| 2096 |
+
- 867
|
| 2097 |
+
- 868
|
| 2098 |
+
- 869
|
| 2099 |
+
- 870
|
| 2100 |
+
- 871
|
| 2101 |
+
- 872
|
| 2102 |
+
- 873
|
| 2103 |
+
- 874
|
| 2104 |
+
- 875
|
| 2105 |
+
- 876
|
| 2106 |
+
- 877
|
| 2107 |
+
- 878
|
| 2108 |
+
- 879
|
| 2109 |
+
- 880
|
| 2110 |
+
- 881
|
| 2111 |
+
- 882
|
| 2112 |
+
- 883
|
| 2113 |
+
- 884
|
| 2114 |
+
- 885
|
| 2115 |
+
- 886
|
| 2116 |
+
- 887
|
| 2117 |
+
- 888
|
| 2118 |
+
- 889
|
| 2119 |
+
- 890
|
| 2120 |
+
- 891
|
| 2121 |
+
- 892
|
| 2122 |
+
- 893
|
| 2123 |
+
- 894
|
| 2124 |
+
- 895
|
| 2125 |
+
- 896
|
| 2126 |
+
- 897
|
| 2127 |
+
- 898
|
| 2128 |
+
- 899
|
| 2129 |
+
- 900
|
| 2130 |
+
- 901
|
| 2131 |
+
- 902
|
| 2132 |
+
- 903
|
| 2133 |
+
- 904
|
| 2134 |
+
- 905
|
| 2135 |
+
- 906
|
| 2136 |
+
- 907
|
| 2137 |
+
- 908
|
| 2138 |
+
- 909
|
| 2139 |
+
- 910
|
| 2140 |
+
- 911
|
| 2141 |
+
- 912
|
| 2142 |
+
- 913
|
| 2143 |
+
- 914
|
| 2144 |
+
- 915
|
| 2145 |
+
- 916
|
| 2146 |
+
- 917
|
| 2147 |
+
- 918
|
| 2148 |
+
- 919
|
| 2149 |
+
- 920
|
| 2150 |
+
- 921
|
| 2151 |
+
- 922
|
| 2152 |
+
- 923
|
| 2153 |
+
- 924
|
| 2154 |
+
- 925
|
| 2155 |
+
- 926
|
| 2156 |
+
- 927
|
| 2157 |
+
- 928
|
| 2158 |
+
- 929
|
| 2159 |
+
- 930
|
| 2160 |
+
- 931
|
| 2161 |
+
- 932
|
| 2162 |
+
- 933
|
| 2163 |
+
- 934
|
| 2164 |
+
- 935
|
| 2165 |
+
- 936
|
| 2166 |
+
- 937
|
| 2167 |
+
- 938
|
| 2168 |
+
- 939
|
| 2169 |
+
- 940
|
| 2170 |
+
- 941
|
| 2171 |
+
- 942
|
| 2172 |
+
- 943
|
| 2173 |
+
- 944
|
| 2174 |
+
- 945
|
| 2175 |
+
- 946
|
| 2176 |
+
- 947
|
| 2177 |
+
- 948
|
| 2178 |
+
- 949
|
| 2179 |
+
- 950
|
| 2180 |
+
- 951
|
| 2181 |
+
- 952
|
| 2182 |
+
- 953
|
| 2183 |
+
- 954
|
| 2184 |
+
- 955
|
| 2185 |
+
- 956
|
| 2186 |
+
- 957
|
| 2187 |
+
- 958
|
| 2188 |
+
- 959
|
| 2189 |
+
- 960
|
| 2190 |
+
- 961
|
| 2191 |
+
- 962
|
| 2192 |
+
- 963
|
| 2193 |
+
- 964
|
| 2194 |
+
- 965
|
| 2195 |
+
- 966
|
| 2196 |
+
- 967
|
| 2197 |
+
- 968
|
| 2198 |
+
- 969
|
| 2199 |
+
- 970
|
| 2200 |
+
- 971
|
| 2201 |
+
- 972
|
| 2202 |
+
- 973
|
| 2203 |
+
- 974
|
| 2204 |
+
- 975
|
| 2205 |
+
- 976
|
| 2206 |
+
- 977
|
| 2207 |
+
- 978
|
| 2208 |
+
- 979
|
| 2209 |
+
- 980
|
| 2210 |
+
- 981
|
| 2211 |
+
- 982
|
| 2212 |
+
- 983
|
| 2213 |
+
- 984
|
| 2214 |
+
- 985
|
| 2215 |
+
- 986
|
| 2216 |
+
- 987
|
| 2217 |
+
- 988
|
| 2218 |
+
- 989
|
| 2219 |
+
- 990
|
| 2220 |
+
- 991
|
| 2221 |
+
- 992
|
| 2222 |
+
- 993
|
| 2223 |
+
- 994
|
| 2224 |
+
- 995
|
| 2225 |
+
- 996
|
| 2226 |
+
- 997
|
| 2227 |
+
- 998
|
| 2228 |
+
- 999
|
| 2229 |
+
task: regression
|
| 2230 |
+
in_scalars_names:
|
| 2231 |
+
- Omega
|
| 2232 |
+
- P
|
| 2233 |
+
out_scalars_names:
|
| 2234 |
+
- Massflow
|
| 2235 |
+
- Compression_ratio
|
| 2236 |
+
- Efficiency
|
| 2237 |
+
in_timeseries_names: []
|
| 2238 |
+
out_timeseries_names: []
|
| 2239 |
+
in_fields_names:
|
| 2240 |
+
- NormalsX
|
| 2241 |
+
- NormalsY
|
| 2242 |
+
- NormalsZ
|
| 2243 |
+
out_fields_names:
|
| 2244 |
+
- Density
|
| 2245 |
+
- Pressure
|
| 2246 |
+
- Temperature
|
| 2247 |
+
in_meshes_names:
|
| 2248 |
+
- /Base_2_3/Zone
|
| 2249 |
+
out_meshes_names: []
|
| 2250 |
features:
|
| 2251 |
- name: sample
|
| 2252 |
dtype: binary
|
|
|
|
| 2256 |
num_examples: 1200
|
| 2257 |
download_size: 3903019328
|
| 2258 |
dataset_size: 5138422400
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2259 |
---
|
| 2260 |
+
|
| 2261 |
+
# Dataset Card
|
| 2262 |
+
|
| 2263 |
+
|
| 2264 |
+
This dataset contains a single huggingface split, named 'all_samples'.
|
| 2265 |
+
|
| 2266 |
+
The samples contains a single huggingface feature, named called "sample".
|
| 2267 |
+
|
| 2268 |
+
Samples are instances of [plaid.containers.sample.Sample](https://plaid-lib.readthedocs.io/en/latest/autoapi/plaid/containers/sample/index.html#plaid.containers.sample.Sample).
|
| 2269 |
+
Mesh objects included in samples follow the [CGNS](https://cgns.github.io/) standard, and can be converted in
|
| 2270 |
+
[Muscat.Containers.Mesh.Mesh](https://muscat.readthedocs.io/en/latest/_source/Muscat.Containers.Mesh.html#Muscat.Containers.Mesh.Mesh).
|
| 2271 |
+
|
| 2272 |
+
|
| 2273 |
+
Example of commands:
|
| 2274 |
+
```python
|
| 2275 |
+
import pickle
|
| 2276 |
+
from datasets import load_dataset
|
| 2277 |
+
from plaid.containers.sample import Sample
|
| 2278 |
+
|
| 2279 |
+
# Load the dataset
|
| 2280 |
+
dataset = load_dataset("chanel/dataset", split="all_samples")
|
| 2281 |
+
|
| 2282 |
+
# Get the first sample of the first split
|
| 2283 |
+
split_names = list(dataset.description["split"].keys())
|
| 2284 |
+
ids_split_0 = dataset.description["split"][split_names[0]]
|
| 2285 |
+
sample_0_split_0 = dataset[ids_split_0[0]]["sample"]
|
| 2286 |
+
plaid_sample = Sample.model_validate(pickle.loads(sample_0_split_0))
|
| 2287 |
+
print("type(plaid_sample) =", type(plaid_sample))
|
| 2288 |
+
|
| 2289 |
+
print("plaid_sample =", plaid_sample)
|
| 2290 |
+
|
| 2291 |
+
# Get a field from the sample
|
| 2292 |
+
field_names = plaid_sample.get_field_names()
|
| 2293 |
+
field = plaid_sample.get_field(field_names[0])
|
| 2294 |
+
print("field_names[0] =", field_names[0])
|
| 2295 |
+
|
| 2296 |
+
print("field.shape =", field.shape)
|
| 2297 |
+
|
| 2298 |
+
# Get the mesh and convert it to Muscat
|
| 2299 |
+
from Muscat.Bridges import CGNSBridge
|
| 2300 |
+
CGNS_tree = plaid_sample.get_mesh()
|
| 2301 |
+
mesh = CGNSBridge.CGNSToMesh(CGNS_tree)
|
| 2302 |
+
print(mesh)
|
| 2303 |
+
```
|
| 2304 |
+
|
| 2305 |
+
## Dataset Details
|
| 2306 |
+
|
| 2307 |
+
### Dataset Description
|
| 2308 |
+
|
| 2309 |
+
|
| 2310 |
+
This dataset contains 3D CFD RANS solutions, under geometrical variations of a compressor blade.
|
| 2311 |
+
|
| 2312 |
+
A description is provided in the [MMGP paper ](https://arxiv.org/pdf/2305.12871) Sections 4.1 and Appendix A.1.
|
| 2313 |
+
|
| 2314 |
+
The variablity in the samples are 2 input scalars and the geometry (mesh). Outputs of interest are 3 scalars and 3 fields.
|
| 2315 |
+
|
| 2316 |
+
Eight nested training sets of sizes 8 to 1000 are provided, with complete input-output data. A testing set of size 200 is provided, for which outputs are not provided.
|
| 2317 |
+
|
| 2318 |
+
Dataset created using the [PLAID](https://plaid-lib.readthedocs.io/) library and datamodel, version: 0.0.10.dev0+g197feb3.d20240624.
|
| 2319 |
+
|
| 2320 |
+
- **Language:** [PLAID](https://plaid-lib.readthedocs.io/)
|
| 2321 |
+
- **License:** cc-by-sa-4.0
|
| 2322 |
+
- **Owner:** Safran
|
| 2323 |
+
|
| 2324 |
+
### Dataset Sources
|
| 2325 |
+
|
| 2326 |
+
- **Repository:** [Zenodo](https://zenodo.org/records/10149830)
|
| 2327 |
+
- **Paper:** [arxiv](https://arxiv.org/pdf/2305.12871)
|