Update README.md
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README.md
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@@ -1,3 +1,1951 @@
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license: apache-2.0
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| 1 |
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
license: apache-2.0
|
| 5 |
+
library_name: transformers
|
| 6 |
+
tags:
|
| 7 |
+
- language
|
| 8 |
+
- granite
|
| 9 |
+
- embeddings
|
| 10 |
+
model-index:
|
| 11 |
+
- name: ibm-granite/granite-embedding-30m-english
|
| 12 |
+
results:
|
| 13 |
+
- dataset:
|
| 14 |
+
type: mteb/arguana
|
| 15 |
+
name: MTEB ArguaAna
|
| 16 |
+
config: default
|
| 17 |
+
split: test
|
| 18 |
+
task:
|
| 19 |
+
type: Retrieval
|
| 20 |
+
metrics:
|
| 21 |
+
- type: map_at_1
|
| 22 |
+
value: 0.31792
|
| 23 |
+
- type: map_at_10
|
| 24 |
+
value: 0.47599
|
| 25 |
+
- type: map_at_100
|
| 26 |
+
value: 0.48425
|
| 27 |
+
- type: map_at_1000
|
| 28 |
+
value: 0.48427
|
| 29 |
+
- type: map_at_3
|
| 30 |
+
value: 0.42757
|
| 31 |
+
- type: map_at_5
|
| 32 |
+
value: 0.45634
|
| 33 |
+
- type: mrr_at_1
|
| 34 |
+
value: 0.32788
|
| 35 |
+
- type: mrr_at_10
|
| 36 |
+
value: 0.47974
|
| 37 |
+
- type: mrr_at_100
|
| 38 |
+
value: 0.48801
|
| 39 |
+
- type: mrr_at_1000
|
| 40 |
+
value: 0.48802
|
| 41 |
+
- type: mrr_at_3
|
| 42 |
+
value: 0.43065
|
| 43 |
+
- type: mrr_at_5
|
| 44 |
+
value: 0.45999
|
| 45 |
+
- type: ndcg_at_1
|
| 46 |
+
value: 0.31792
|
| 47 |
+
- type: ndcg_at_10
|
| 48 |
+
value: 0.56356
|
| 49 |
+
- type: ndcg_at_100
|
| 50 |
+
value: 0.59789
|
| 51 |
+
- type: ndcg_at_1000
|
| 52 |
+
value: 0.59857
|
| 53 |
+
- type: ndcg_at_3
|
| 54 |
+
value: 0.46453
|
| 55 |
+
- type: ndcg_at_5
|
| 56 |
+
value: 0.51623
|
| 57 |
+
- type: precision_at_1
|
| 58 |
+
value: 0.31792
|
| 59 |
+
- type: precision_at_10
|
| 60 |
+
value: 0.08428
|
| 61 |
+
- type: precision_at_100
|
| 62 |
+
value: 0.00991
|
| 63 |
+
- type: precision_at_1000
|
| 64 |
+
value: 0.001
|
| 65 |
+
- type: precision_at_3
|
| 66 |
+
value: 0.19061
|
| 67 |
+
- type: precision_at_5
|
| 68 |
+
value: 0.1394
|
| 69 |
+
- type: recall_at_1
|
| 70 |
+
value: 0.31792
|
| 71 |
+
- type: recall_at_10
|
| 72 |
+
value: 0.84282
|
| 73 |
+
- type: recall_at_100
|
| 74 |
+
value: 0.99075
|
| 75 |
+
- type: recall_at_1000
|
| 76 |
+
value: 0.99644
|
| 77 |
+
- type: recall_at_3
|
| 78 |
+
value: 0.57183
|
| 79 |
+
- type: recall_at_5
|
| 80 |
+
value: 0.69701
|
| 81 |
+
- dataset:
|
| 82 |
+
type: mteb/climate-fever
|
| 83 |
+
name: MTEB ClimateFEVER
|
| 84 |
+
config: default
|
| 85 |
+
split: test
|
| 86 |
+
task:
|
| 87 |
+
type: Retrieval
|
| 88 |
+
metrics:
|
| 89 |
+
- type: map_at_1
|
| 90 |
+
value: 0.13189
|
| 91 |
+
- type: map_at_10
|
| 92 |
+
value: 0.21789
|
| 93 |
+
- type: map_at_100
|
| 94 |
+
value: 0.2358
|
| 95 |
+
- type: map_at_1000
|
| 96 |
+
value: 0.23772
|
| 97 |
+
- type: map_at_3
|
| 98 |
+
value: 0.18513
|
| 99 |
+
- type: map_at_5
|
| 100 |
+
value: 0.20212
|
| 101 |
+
- type: mrr_at_1
|
| 102 |
+
value: 0.29837
|
| 103 |
+
- type: mrr_at_10
|
| 104 |
+
value: 0.41376
|
| 105 |
+
- type: mrr_at_100
|
| 106 |
+
value: 0.42282
|
| 107 |
+
- type: mrr_at_1000
|
| 108 |
+
value: 0.42319
|
| 109 |
+
- type: mrr_at_3
|
| 110 |
+
value: 0.38284
|
| 111 |
+
- type: mrr_at_5
|
| 112 |
+
value: 0.40301
|
| 113 |
+
- type: ndcg_at_1
|
| 114 |
+
value: 0.29837
|
| 115 |
+
- type: ndcg_at_10
|
| 116 |
+
value: 0.30263
|
| 117 |
+
- type: ndcg_at_100
|
| 118 |
+
value: 0.37228
|
| 119 |
+
- type: ndcg_at_1000
|
| 120 |
+
value: 0.40677
|
| 121 |
+
- type: ndcg_at_3
|
| 122 |
+
value: 0.25392
|
| 123 |
+
- type: ndcg_at_5
|
| 124 |
+
value: 0.27153
|
| 125 |
+
- type: precision_at_1
|
| 126 |
+
value: 0.29837
|
| 127 |
+
- type: precision_at_10
|
| 128 |
+
value: 0.09179
|
| 129 |
+
- type: precision_at_100
|
| 130 |
+
value: 0.01659
|
| 131 |
+
- type: precision_at_1000
|
| 132 |
+
value: 0.0023
|
| 133 |
+
- type: precision_at_3
|
| 134 |
+
value: 0.18545
|
| 135 |
+
- type: precision_at_5
|
| 136 |
+
value: 0.14241
|
| 137 |
+
- type: recall_at_1
|
| 138 |
+
value: 0.13189
|
| 139 |
+
- type: recall_at_10
|
| 140 |
+
value: 0.35355
|
| 141 |
+
- type: recall_at_100
|
| 142 |
+
value: 0.59255
|
| 143 |
+
- type: recall_at_1000
|
| 144 |
+
value: 0.78637
|
| 145 |
+
- type: recall_at_3
|
| 146 |
+
value: 0.23255
|
| 147 |
+
- type: recall_at_5
|
| 148 |
+
value: 0.28446
|
| 149 |
+
- dataset:
|
| 150 |
+
type: mteb/cqadupstack-android
|
| 151 |
+
name: MTEB CQADupstackAndroidRetrieval
|
| 152 |
+
config: default
|
| 153 |
+
split: test
|
| 154 |
+
task:
|
| 155 |
+
type: Retrieval
|
| 156 |
+
metrics:
|
| 157 |
+
- type: map_at_1
|
| 158 |
+
value: 0.35797
|
| 159 |
+
- type: map_at_10
|
| 160 |
+
value: 0.47793
|
| 161 |
+
- type: map_at_100
|
| 162 |
+
value: 0.49422
|
| 163 |
+
- type: map_at_1000
|
| 164 |
+
value: 0.49546
|
| 165 |
+
- type: map_at_3
|
| 166 |
+
value: 0.44137
|
| 167 |
+
- type: map_at_5
|
| 168 |
+
value: 0.46063
|
| 169 |
+
- type: mrr_at_1
|
| 170 |
+
value: 0.44206
|
| 171 |
+
- type: mrr_at_10
|
| 172 |
+
value: 0.53808
|
| 173 |
+
- type: mrr_at_100
|
| 174 |
+
value: 0.5454
|
| 175 |
+
- type: mrr_at_1000
|
| 176 |
+
value: 0.54578
|
| 177 |
+
- type: mrr_at_3
|
| 178 |
+
value: 0.51431
|
| 179 |
+
- type: mrr_at_5
|
| 180 |
+
value: 0.5284
|
| 181 |
+
- type: ndcg_at_1
|
| 182 |
+
value: 0.44206
|
| 183 |
+
- type: ndcg_at_10
|
| 184 |
+
value: 0.54106
|
| 185 |
+
- type: ndcg_at_100
|
| 186 |
+
value: 0.59335
|
| 187 |
+
- type: ndcg_at_1000
|
| 188 |
+
value: 0.61015
|
| 189 |
+
- type: ndcg_at_3
|
| 190 |
+
value: 0.49365
|
| 191 |
+
- type: ndcg_at_5
|
| 192 |
+
value: 0.51429
|
| 193 |
+
- type: precision_at_1
|
| 194 |
+
value: 0.44206
|
| 195 |
+
- type: precision_at_10
|
| 196 |
+
value: 0.10443
|
| 197 |
+
- type: precision_at_100
|
| 198 |
+
value: 0.01631
|
| 199 |
+
- type: precision_at_1000
|
| 200 |
+
value: 0.00214
|
| 201 |
+
- type: precision_at_3
|
| 202 |
+
value: 0.23653
|
| 203 |
+
- type: precision_at_5
|
| 204 |
+
value: 0.1691
|
| 205 |
+
- type: recall_at_1
|
| 206 |
+
value: 0.35797
|
| 207 |
+
- type: recall_at_10
|
| 208 |
+
value: 0.65182
|
| 209 |
+
- type: recall_at_100
|
| 210 |
+
value: 0.86654
|
| 211 |
+
- type: recall_at_1000
|
| 212 |
+
value: 0.97131
|
| 213 |
+
- type: recall_at_3
|
| 214 |
+
value: 0.51224
|
| 215 |
+
- type: recall_at_5
|
| 216 |
+
value: 0.57219
|
| 217 |
+
- dataset:
|
| 218 |
+
type: mteb/cqadupstack-english
|
| 219 |
+
name: MTEB CQADupstackEnglishRetrieval
|
| 220 |
+
config: default
|
| 221 |
+
split: test
|
| 222 |
+
task:
|
| 223 |
+
type: Retrieval
|
| 224 |
+
metrics:
|
| 225 |
+
- type: map_at_1
|
| 226 |
+
value: 0.32748
|
| 227 |
+
- type: map_at_10
|
| 228 |
+
value: 0.44138
|
| 229 |
+
- type: map_at_100
|
| 230 |
+
value: 0.45565
|
| 231 |
+
- type: map_at_1000
|
| 232 |
+
value: 0.45698
|
| 233 |
+
- type: map_at_3
|
| 234 |
+
value: 0.40916
|
| 235 |
+
- type: map_at_5
|
| 236 |
+
value: 0.42621
|
| 237 |
+
- type: mrr_at_1
|
| 238 |
+
value: 0.41274
|
| 239 |
+
- type: mrr_at_10
|
| 240 |
+
value: 0.5046
|
| 241 |
+
- type: mrr_at_100
|
| 242 |
+
value: 0.5107
|
| 243 |
+
- type: mrr_at_1000
|
| 244 |
+
value: 0.51109
|
| 245 |
+
- type: mrr_at_3
|
| 246 |
+
value: 0.48238
|
| 247 |
+
- type: mrr_at_5
|
| 248 |
+
value: 0.49563
|
| 249 |
+
- type: ndcg_at_1
|
| 250 |
+
value: 0.41274
|
| 251 |
+
- type: ndcg_at_10
|
| 252 |
+
value: 0.50251
|
| 253 |
+
- type: ndcg_at_100
|
| 254 |
+
value: 0.54725
|
| 255 |
+
- type: ndcg_at_1000
|
| 256 |
+
value: 0.56635
|
| 257 |
+
- type: ndcg_at_3
|
| 258 |
+
value: 0.46023
|
| 259 |
+
- type: ndcg_at_5
|
| 260 |
+
value: 0.47883
|
| 261 |
+
- type: precision_at_1
|
| 262 |
+
value: 0.41274
|
| 263 |
+
- type: precision_at_10
|
| 264 |
+
value: 0.09828
|
| 265 |
+
- type: precision_at_100
|
| 266 |
+
value: 0.01573
|
| 267 |
+
- type: precision_at_1000
|
| 268 |
+
value: 0.00202
|
| 269 |
+
- type: precision_at_3
|
| 270 |
+
value: 0.22718
|
| 271 |
+
- type: precision_at_5
|
| 272 |
+
value: 0.16064
|
| 273 |
+
- type: recall_at_1
|
| 274 |
+
value: 0.32748
|
| 275 |
+
- type: recall_at_10
|
| 276 |
+
value: 0.60322
|
| 277 |
+
- type: recall_at_100
|
| 278 |
+
value: 0.79669
|
| 279 |
+
- type: recall_at_1000
|
| 280 |
+
value: 0.9173
|
| 281 |
+
- type: recall_at_3
|
| 282 |
+
value: 0.47523
|
| 283 |
+
- type: recall_at_5
|
| 284 |
+
value: 0.52957
|
| 285 |
+
- dataset:
|
| 286 |
+
type: mteb/cqadupstack-gaming
|
| 287 |
+
name: MTEB CQADupstackGamingRetrieval
|
| 288 |
+
config: default
|
| 289 |
+
split: test
|
| 290 |
+
task:
|
| 291 |
+
type: Retrieval
|
| 292 |
+
metrics:
|
| 293 |
+
- type: map_at_1
|
| 294 |
+
value: 0.41126
|
| 295 |
+
- type: map_at_10
|
| 296 |
+
value: 0.53661
|
| 297 |
+
- type: map_at_100
|
| 298 |
+
value: 0.54588
|
| 299 |
+
- type: map_at_1000
|
| 300 |
+
value: 0.54638
|
| 301 |
+
- type: map_at_3
|
| 302 |
+
value: 0.50389
|
| 303 |
+
- type: map_at_5
|
| 304 |
+
value: 0.52286
|
| 305 |
+
- type: mrr_at_1
|
| 306 |
+
value: 0.47147
|
| 307 |
+
- type: mrr_at_10
|
| 308 |
+
value: 0.5685
|
| 309 |
+
- type: mrr_at_100
|
| 310 |
+
value: 0.57458
|
| 311 |
+
- type: mrr_at_1000
|
| 312 |
+
value: 0.57487
|
| 313 |
+
- type: mrr_at_3
|
| 314 |
+
value: 0.54431
|
| 315 |
+
- type: mrr_at_5
|
| 316 |
+
value: 0.55957
|
| 317 |
+
- type: ndcg_at_1
|
| 318 |
+
value: 0.47147
|
| 319 |
+
- type: ndcg_at_10
|
| 320 |
+
value: 0.59318
|
| 321 |
+
- type: ndcg_at_100
|
| 322 |
+
value: 0.62972
|
| 323 |
+
- type: ndcg_at_1000
|
| 324 |
+
value: 0.64033
|
| 325 |
+
- type: ndcg_at_3
|
| 326 |
+
value: 0.53969
|
| 327 |
+
- type: ndcg_at_5
|
| 328 |
+
value: 0.56743
|
| 329 |
+
- type: precision_at_1
|
| 330 |
+
value: 0.47147
|
| 331 |
+
- type: precision_at_10
|
| 332 |
+
value: 0.09549
|
| 333 |
+
- type: precision_at_100
|
| 334 |
+
value: 0.01224
|
| 335 |
+
- type: precision_at_1000
|
| 336 |
+
value: 0.00135
|
| 337 |
+
- type: precision_at_3
|
| 338 |
+
value: 0.24159
|
| 339 |
+
- type: precision_at_5
|
| 340 |
+
value: 0.16577
|
| 341 |
+
- type: recall_at_1
|
| 342 |
+
value: 0.41126
|
| 343 |
+
- type: recall_at_10
|
| 344 |
+
value: 0.72691
|
| 345 |
+
- type: recall_at_100
|
| 346 |
+
value: 0.88692
|
| 347 |
+
- type: recall_at_1000
|
| 348 |
+
value: 0.96232
|
| 349 |
+
- type: recall_at_3
|
| 350 |
+
value: 0.58374
|
| 351 |
+
- type: recall_at_5
|
| 352 |
+
value: 0.65226
|
| 353 |
+
- dataset:
|
| 354 |
+
type: mteb/cqadupstack-gis
|
| 355 |
+
name: MTEB CQADupstackGisRetrieval
|
| 356 |
+
config: default
|
| 357 |
+
split: test
|
| 358 |
+
task:
|
| 359 |
+
type: Retrieval
|
| 360 |
+
metrics:
|
| 361 |
+
- type: map_at_1
|
| 362 |
+
value: 0.28464
|
| 363 |
+
- type: map_at_10
|
| 364 |
+
value: 0.3828
|
| 365 |
+
- type: map_at_100
|
| 366 |
+
value: 0.39277
|
| 367 |
+
- type: map_at_1000
|
| 368 |
+
value: 0.39355
|
| 369 |
+
- type: map_at_3
|
| 370 |
+
value: 0.35704
|
| 371 |
+
- type: map_at_5
|
| 372 |
+
value: 0.37116
|
| 373 |
+
- type: mrr_at_1
|
| 374 |
+
value: 0.30734
|
| 375 |
+
- type: mrr_at_10
|
| 376 |
+
value: 0.40422
|
| 377 |
+
- type: mrr_at_100
|
| 378 |
+
value: 0.41297
|
| 379 |
+
- type: mrr_at_1000
|
| 380 |
+
value: 0.41355
|
| 381 |
+
- type: mrr_at_3
|
| 382 |
+
value: 0.38136
|
| 383 |
+
- type: mrr_at_5
|
| 384 |
+
value: 0.39362
|
| 385 |
+
- type: ndcg_at_1
|
| 386 |
+
value: 0.30734
|
| 387 |
+
- type: ndcg_at_10
|
| 388 |
+
value: 0.43564
|
| 389 |
+
- type: ndcg_at_100
|
| 390 |
+
value: 0.48419
|
| 391 |
+
- type: ndcg_at_1000
|
| 392 |
+
value: 0.50404
|
| 393 |
+
- type: ndcg_at_3
|
| 394 |
+
value: 0.38672
|
| 395 |
+
- type: ndcg_at_5
|
| 396 |
+
value: 0.40954
|
| 397 |
+
- type: precision_at_1
|
| 398 |
+
value: 0.30734
|
| 399 |
+
- type: precision_at_10
|
| 400 |
+
value: 0.06633
|
| 401 |
+
- type: precision_at_100
|
| 402 |
+
value: 0.00956
|
| 403 |
+
- type: precision_at_1000
|
| 404 |
+
value: 0.00116
|
| 405 |
+
- type: precision_at_3
|
| 406 |
+
value: 0.16497
|
| 407 |
+
- type: precision_at_5
|
| 408 |
+
value: 0.11254
|
| 409 |
+
- type: recall_at_1
|
| 410 |
+
value: 0.28464
|
| 411 |
+
- type: recall_at_10
|
| 412 |
+
value: 0.57621
|
| 413 |
+
- type: recall_at_100
|
| 414 |
+
value: 0.7966
|
| 415 |
+
- type: recall_at_1000
|
| 416 |
+
value: 0.94633
|
| 417 |
+
- type: recall_at_3
|
| 418 |
+
value: 0.44588
|
| 419 |
+
- type: recall_at_5
|
| 420 |
+
value: 0.50031
|
| 421 |
+
- dataset:
|
| 422 |
+
type: mteb/cqadupstack-mathematica
|
| 423 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
| 424 |
+
config: default
|
| 425 |
+
split: test
|
| 426 |
+
task:
|
| 427 |
+
type: Retrieval
|
| 428 |
+
metrics:
|
| 429 |
+
- type: map_at_1
|
| 430 |
+
value: 0.18119
|
| 431 |
+
- type: map_at_10
|
| 432 |
+
value: 0.27055
|
| 433 |
+
- type: map_at_100
|
| 434 |
+
value: 0.28461
|
| 435 |
+
- type: map_at_1000
|
| 436 |
+
value: 0.28577
|
| 437 |
+
- type: map_at_3
|
| 438 |
+
value: 0.24341
|
| 439 |
+
- type: map_at_5
|
| 440 |
+
value: 0.25861
|
| 441 |
+
- type: mrr_at_1
|
| 442 |
+
value: 0.22886
|
| 443 |
+
- type: mrr_at_10
|
| 444 |
+
value: 0.32234
|
| 445 |
+
- type: mrr_at_100
|
| 446 |
+
value: 0.3328
|
| 447 |
+
- type: mrr_at_1000
|
| 448 |
+
value: 0.3334
|
| 449 |
+
- type: mrr_at_3
|
| 450 |
+
value: 0.29664
|
| 451 |
+
- type: mrr_at_5
|
| 452 |
+
value: 0.31107
|
| 453 |
+
- type: ndcg_at_1
|
| 454 |
+
value: 0.22886
|
| 455 |
+
- type: ndcg_at_10
|
| 456 |
+
value: 0.32749
|
| 457 |
+
- type: ndcg_at_100
|
| 458 |
+
value: 0.39095
|
| 459 |
+
- type: ndcg_at_1000
|
| 460 |
+
value: 0.41656
|
| 461 |
+
- type: ndcg_at_3
|
| 462 |
+
value: 0.27864
|
| 463 |
+
- type: ndcg_at_5
|
| 464 |
+
value: 0.30177
|
| 465 |
+
- type: precision_at_1
|
| 466 |
+
value: 0.22886
|
| 467 |
+
- type: precision_at_10
|
| 468 |
+
value: 0.06169
|
| 469 |
+
- type: precision_at_100
|
| 470 |
+
value: 0.0107
|
| 471 |
+
- type: precision_at_1000
|
| 472 |
+
value: 0.00143
|
| 473 |
+
- type: precision_at_3
|
| 474 |
+
value: 0.13682
|
| 475 |
+
- type: precision_at_5
|
| 476 |
+
value: 0.0995
|
| 477 |
+
- type: recall_at_1
|
| 478 |
+
value: 0.18119
|
| 479 |
+
- type: recall_at_10
|
| 480 |
+
value: 0.44983
|
| 481 |
+
- type: recall_at_100
|
| 482 |
+
value: 0.72396
|
| 483 |
+
- type: recall_at_1000
|
| 484 |
+
value: 0.90223
|
| 485 |
+
- type: recall_at_3
|
| 486 |
+
value: 0.31633
|
| 487 |
+
- type: recall_at_5
|
| 488 |
+
value: 0.37532
|
| 489 |
+
- dataset:
|
| 490 |
+
type: mteb/cqadupstack-physics
|
| 491 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
| 492 |
+
config: default
|
| 493 |
+
split: test
|
| 494 |
+
task:
|
| 495 |
+
type: Retrieval
|
| 496 |
+
metrics:
|
| 497 |
+
- type: map_at_1
|
| 498 |
+
value: 0.30517
|
| 499 |
+
- type: map_at_10
|
| 500 |
+
value: 0.42031
|
| 501 |
+
- type: map_at_100
|
| 502 |
+
value: 0.43415
|
| 503 |
+
- type: map_at_1000
|
| 504 |
+
value: 0.43525
|
| 505 |
+
- type: map_at_3
|
| 506 |
+
value: 0.38443
|
| 507 |
+
- type: map_at_5
|
| 508 |
+
value: 0.40685
|
| 509 |
+
- type: mrr_at_1
|
| 510 |
+
value: 0.38114
|
| 511 |
+
- type: mrr_at_10
|
| 512 |
+
value: 0.47783
|
| 513 |
+
- type: mrr_at_100
|
| 514 |
+
value: 0.48647
|
| 515 |
+
- type: mrr_at_1000
|
| 516 |
+
value: 0.48688
|
| 517 |
+
- type: mrr_at_3
|
| 518 |
+
value: 0.45172
|
| 519 |
+
- type: mrr_at_5
|
| 520 |
+
value: 0.46817
|
| 521 |
+
- type: ndcg_at_1
|
| 522 |
+
value: 0.38114
|
| 523 |
+
- type: ndcg_at_10
|
| 524 |
+
value: 0.4834
|
| 525 |
+
- type: ndcg_at_100
|
| 526 |
+
value: 0.53861
|
| 527 |
+
- type: ndcg_at_1000
|
| 528 |
+
value: 0.55701
|
| 529 |
+
- type: ndcg_at_3
|
| 530 |
+
value: 0.42986
|
| 531 |
+
- type: ndcg_at_5
|
| 532 |
+
value: 0.45893
|
| 533 |
+
- type: precision_at_1
|
| 534 |
+
value: 0.38114
|
| 535 |
+
- type: precision_at_10
|
| 536 |
+
value: 0.08893
|
| 537 |
+
- type: precision_at_100
|
| 538 |
+
value: 0.01375
|
| 539 |
+
- type: precision_at_1000
|
| 540 |
+
value: 0.00172
|
| 541 |
+
- type: precision_at_3
|
| 542 |
+
value: 0.20821
|
| 543 |
+
- type: precision_at_5
|
| 544 |
+
value: 0.15034
|
| 545 |
+
- type: recall_at_1
|
| 546 |
+
value: 0.30517
|
| 547 |
+
- type: recall_at_10
|
| 548 |
+
value: 0.61332
|
| 549 |
+
- type: recall_at_100
|
| 550 |
+
value: 0.84051
|
| 551 |
+
- type: recall_at_1000
|
| 552 |
+
value: 0.95826
|
| 553 |
+
- type: recall_at_3
|
| 554 |
+
value: 0.46015
|
| 555 |
+
- type: recall_at_5
|
| 556 |
+
value: 0.53801
|
| 557 |
+
- dataset:
|
| 558 |
+
type: mteb/cqadupstack-programmers
|
| 559 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
| 560 |
+
config: default
|
| 561 |
+
split: test
|
| 562 |
+
task:
|
| 563 |
+
type: Retrieval
|
| 564 |
+
metrics:
|
| 565 |
+
- type: map_at_1
|
| 566 |
+
value: 0.27396
|
| 567 |
+
- type: map_at_10
|
| 568 |
+
value: 0.38043
|
| 569 |
+
- type: map_at_100
|
| 570 |
+
value: 0.39341
|
| 571 |
+
- type: map_at_1000
|
| 572 |
+
value: 0.39454
|
| 573 |
+
- type: map_at_3
|
| 574 |
+
value: 0.34783
|
| 575 |
+
- type: map_at_5
|
| 576 |
+
value: 0.3663
|
| 577 |
+
- type: mrr_at_1
|
| 578 |
+
value: 0.34247
|
| 579 |
+
- type: mrr_at_10
|
| 580 |
+
value: 0.43681
|
| 581 |
+
- type: mrr_at_100
|
| 582 |
+
value: 0.4451
|
| 583 |
+
- type: mrr_at_1000
|
| 584 |
+
value: 0.44569
|
| 585 |
+
- type: mrr_at_3
|
| 586 |
+
value: 0.41172
|
| 587 |
+
- type: mrr_at_5
|
| 588 |
+
value: 0.42702
|
| 589 |
+
- type: ndcg_at_1
|
| 590 |
+
value: 0.34247
|
| 591 |
+
- type: ndcg_at_10
|
| 592 |
+
value: 0.44065
|
| 593 |
+
- type: ndcg_at_100
|
| 594 |
+
value: 0.49434
|
| 595 |
+
- type: ndcg_at_1000
|
| 596 |
+
value: 0.51682
|
| 597 |
+
- type: ndcg_at_3
|
| 598 |
+
value: 0.38976
|
| 599 |
+
- type: ndcg_at_5
|
| 600 |
+
value: 0.41332
|
| 601 |
+
- type: precision_at_1
|
| 602 |
+
value: 0.34247
|
| 603 |
+
- type: precision_at_10
|
| 604 |
+
value: 0.08059
|
| 605 |
+
- type: precision_at_100
|
| 606 |
+
value: 0.01258
|
| 607 |
+
- type: precision_at_1000
|
| 608 |
+
value: 0.00162
|
| 609 |
+
- type: precision_at_3
|
| 610 |
+
value: 0.1876
|
| 611 |
+
- type: precision_at_5
|
| 612 |
+
value: 0.13333
|
| 613 |
+
- type: recall_at_1
|
| 614 |
+
value: 0.27396
|
| 615 |
+
- type: recall_at_10
|
| 616 |
+
value: 0.56481
|
| 617 |
+
- type: recall_at_100
|
| 618 |
+
value: 0.79012
|
| 619 |
+
- type: recall_at_1000
|
| 620 |
+
value: 0.94182
|
| 621 |
+
- type: recall_at_3
|
| 622 |
+
value: 0.41785
|
| 623 |
+
- type: recall_at_5
|
| 624 |
+
value: 0.48303
|
| 625 |
+
- dataset:
|
| 626 |
+
type: mteb/cqadupstack-stats
|
| 627 |
+
name: MTEB CQADupstackStatsRetrieval
|
| 628 |
+
config: default
|
| 629 |
+
split: test
|
| 630 |
+
task:
|
| 631 |
+
type: Retrieval
|
| 632 |
+
metrics:
|
| 633 |
+
- type: map_at_1
|
| 634 |
+
value: 0.25728
|
| 635 |
+
- type: map_at_10
|
| 636 |
+
value: 0.33903
|
| 637 |
+
- type: map_at_100
|
| 638 |
+
value: 0.34853
|
| 639 |
+
- type: map_at_1000
|
| 640 |
+
value: 0.34944
|
| 641 |
+
- type: map_at_3
|
| 642 |
+
value: 0.31268
|
| 643 |
+
- type: map_at_5
|
| 644 |
+
value: 0.32596
|
| 645 |
+
- type: mrr_at_1
|
| 646 |
+
value: 0.29141
|
| 647 |
+
- type: mrr_at_10
|
| 648 |
+
value: 0.36739
|
| 649 |
+
- type: mrr_at_100
|
| 650 |
+
value: 0.37545
|
| 651 |
+
- type: mrr_at_1000
|
| 652 |
+
value: 0.37608
|
| 653 |
+
- type: mrr_at_3
|
| 654 |
+
value: 0.34407
|
| 655 |
+
- type: mrr_at_5
|
| 656 |
+
value: 0.3568
|
| 657 |
+
- type: ndcg_at_1
|
| 658 |
+
value: 0.29141
|
| 659 |
+
- type: ndcg_at_10
|
| 660 |
+
value: 0.38596
|
| 661 |
+
- type: ndcg_at_100
|
| 662 |
+
value: 0.43375
|
| 663 |
+
- type: ndcg_at_1000
|
| 664 |
+
value: 0.45562
|
| 665 |
+
- type: ndcg_at_3
|
| 666 |
+
value: 0.33861
|
| 667 |
+
- type: ndcg_at_5
|
| 668 |
+
value: 0.35887
|
| 669 |
+
- type: precision_at_1
|
| 670 |
+
value: 0.29141
|
| 671 |
+
- type: precision_at_10
|
| 672 |
+
value: 0.06334
|
| 673 |
+
- type: precision_at_100
|
| 674 |
+
value: 0.00952
|
| 675 |
+
- type: precision_at_1000
|
| 676 |
+
value: 0.00121
|
| 677 |
+
- type: precision_at_3
|
| 678 |
+
value: 0.14826
|
| 679 |
+
- type: precision_at_5
|
| 680 |
+
value: 0.10429
|
| 681 |
+
- type: recall_at_1
|
| 682 |
+
value: 0.25728
|
| 683 |
+
- type: recall_at_10
|
| 684 |
+
value: 0.50121
|
| 685 |
+
- type: recall_at_100
|
| 686 |
+
value: 0.72382
|
| 687 |
+
- type: recall_at_1000
|
| 688 |
+
value: 0.88306
|
| 689 |
+
- type: recall_at_3
|
| 690 |
+
value: 0.36638
|
| 691 |
+
- type: recall_at_5
|
| 692 |
+
value: 0.41689
|
| 693 |
+
- dataset:
|
| 694 |
+
type: mteb/cqadupstack-tex
|
| 695 |
+
name: MTEB CQADupstackTexRetrieval
|
| 696 |
+
config: default
|
| 697 |
+
split: test
|
| 698 |
+
task:
|
| 699 |
+
type: Retrieval
|
| 700 |
+
metrics:
|
| 701 |
+
- type: map_at_1
|
| 702 |
+
value: 0.19911
|
| 703 |
+
- type: map_at_10
|
| 704 |
+
value: 0.2856
|
| 705 |
+
- type: map_at_100
|
| 706 |
+
value: 0.29785
|
| 707 |
+
- type: map_at_1000
|
| 708 |
+
value: 0.29911
|
| 709 |
+
- type: map_at_3
|
| 710 |
+
value: 0.25875
|
| 711 |
+
- type: map_at_5
|
| 712 |
+
value: 0.2741
|
| 713 |
+
- type: mrr_at_1
|
| 714 |
+
value: 0.24054
|
| 715 |
+
- type: mrr_at_10
|
| 716 |
+
value: 0.32483
|
| 717 |
+
- type: mrr_at_100
|
| 718 |
+
value: 0.33464
|
| 719 |
+
- type: mrr_at_1000
|
| 720 |
+
value: 0.33534
|
| 721 |
+
- type: mrr_at_3
|
| 722 |
+
value: 0.30162
|
| 723 |
+
- type: mrr_at_5
|
| 724 |
+
value: 0.31506
|
| 725 |
+
- type: ndcg_at_1
|
| 726 |
+
value: 0.24054
|
| 727 |
+
- type: ndcg_at_10
|
| 728 |
+
value: 0.33723
|
| 729 |
+
- type: ndcg_at_100
|
| 730 |
+
value: 0.39362
|
| 731 |
+
- type: ndcg_at_1000
|
| 732 |
+
value: 0.42065
|
| 733 |
+
- type: ndcg_at_3
|
| 734 |
+
value: 0.29116
|
| 735 |
+
- type: ndcg_at_5
|
| 736 |
+
value: 0.31299
|
| 737 |
+
- type: precision_at_1
|
| 738 |
+
value: 0.24054
|
| 739 |
+
- type: precision_at_10
|
| 740 |
+
value: 0.06194
|
| 741 |
+
- type: precision_at_100
|
| 742 |
+
value: 0.01058
|
| 743 |
+
- type: precision_at_1000
|
| 744 |
+
value: 0.00148
|
| 745 |
+
- type: precision_at_3
|
| 746 |
+
value: 0.13914
|
| 747 |
+
- type: precision_at_5
|
| 748 |
+
value: 0.10076
|
| 749 |
+
- type: recall_at_1
|
| 750 |
+
value: 0.19911
|
| 751 |
+
- type: recall_at_10
|
| 752 |
+
value: 0.45183
|
| 753 |
+
- type: recall_at_100
|
| 754 |
+
value: 0.7025
|
| 755 |
+
- type: recall_at_1000
|
| 756 |
+
value: 0.89222
|
| 757 |
+
- type: recall_at_3
|
| 758 |
+
value: 0.32195
|
| 759 |
+
- type: recall_at_5
|
| 760 |
+
value: 0.37852
|
| 761 |
+
- dataset:
|
| 762 |
+
type: mteb/cqadupstack-unix
|
| 763 |
+
name: MTEB CQADupstackUnixRetrieval
|
| 764 |
+
config: default
|
| 765 |
+
split: test
|
| 766 |
+
task:
|
| 767 |
+
type: Retrieval
|
| 768 |
+
metrics:
|
| 769 |
+
- type: map_at_1
|
| 770 |
+
value: 0.29819
|
| 771 |
+
- type: map_at_10
|
| 772 |
+
value: 0.40073
|
| 773 |
+
- type: map_at_100
|
| 774 |
+
value: 0.41289
|
| 775 |
+
- type: map_at_1000
|
| 776 |
+
value: 0.41375
|
| 777 |
+
- type: map_at_3
|
| 778 |
+
value: 0.36572
|
| 779 |
+
- type: map_at_5
|
| 780 |
+
value: 0.38386
|
| 781 |
+
- type: mrr_at_1
|
| 782 |
+
value: 0.35168
|
| 783 |
+
- type: mrr_at_10
|
| 784 |
+
value: 0.44381
|
| 785 |
+
- type: mrr_at_100
|
| 786 |
+
value: 0.45191
|
| 787 |
+
- type: mrr_at_1000
|
| 788 |
+
value: 0.45234
|
| 789 |
+
- type: mrr_at_3
|
| 790 |
+
value: 0.41402
|
| 791 |
+
- type: mrr_at_5
|
| 792 |
+
value: 0.43039
|
| 793 |
+
- type: ndcg_at_1
|
| 794 |
+
value: 0.35168
|
| 795 |
+
- type: ndcg_at_10
|
| 796 |
+
value: 0.46071
|
| 797 |
+
- type: ndcg_at_100
|
| 798 |
+
value: 0.51351
|
| 799 |
+
- type: ndcg_at_1000
|
| 800 |
+
value: 0.5317
|
| 801 |
+
- type: ndcg_at_3
|
| 802 |
+
value: 0.39972
|
| 803 |
+
- type: ndcg_at_5
|
| 804 |
+
value: 0.42586
|
| 805 |
+
- type: precision_at_1
|
| 806 |
+
value: 0.35168
|
| 807 |
+
- type: precision_at_10
|
| 808 |
+
value: 0.07985
|
| 809 |
+
- type: precision_at_100
|
| 810 |
+
value: 0.01185
|
| 811 |
+
- type: precision_at_1000
|
| 812 |
+
value: 0.00144
|
| 813 |
+
- type: precision_at_3
|
| 814 |
+
value: 0.18221
|
| 815 |
+
- type: precision_at_5
|
| 816 |
+
value: 0.12892
|
| 817 |
+
- type: recall_at_1
|
| 818 |
+
value: 0.29819
|
| 819 |
+
- type: recall_at_10
|
| 820 |
+
value: 0.60075
|
| 821 |
+
- type: recall_at_100
|
| 822 |
+
value: 0.82771
|
| 823 |
+
- type: recall_at_1000
|
| 824 |
+
value: 0.95219
|
| 825 |
+
- type: recall_at_3
|
| 826 |
+
value: 0.43245
|
| 827 |
+
- type: recall_at_5
|
| 828 |
+
value: 0.49931
|
| 829 |
+
- dataset:
|
| 830 |
+
type: mteb/cqadupstack-webmasters
|
| 831 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
| 832 |
+
config: default
|
| 833 |
+
split: test
|
| 834 |
+
task:
|
| 835 |
+
type: Retrieval
|
| 836 |
+
metrics:
|
| 837 |
+
- type: map_at_1
|
| 838 |
+
value: 0.28409
|
| 839 |
+
- type: map_at_10
|
| 840 |
+
value: 0.37621
|
| 841 |
+
- type: map_at_100
|
| 842 |
+
value: 0.39233
|
| 843 |
+
- type: map_at_1000
|
| 844 |
+
value: 0.39471
|
| 845 |
+
- type: map_at_3
|
| 846 |
+
value: 0.34337
|
| 847 |
+
- type: map_at_5
|
| 848 |
+
value: 0.35985
|
| 849 |
+
- type: mrr_at_1
|
| 850 |
+
value: 0.33794
|
| 851 |
+
- type: mrr_at_10
|
| 852 |
+
value: 0.42349
|
| 853 |
+
- type: mrr_at_100
|
| 854 |
+
value: 0.43196
|
| 855 |
+
- type: mrr_at_1000
|
| 856 |
+
value: 0.43237
|
| 857 |
+
- type: mrr_at_3
|
| 858 |
+
value: 0.39526
|
| 859 |
+
- type: mrr_at_5
|
| 860 |
+
value: 0.41087
|
| 861 |
+
- type: ndcg_at_1
|
| 862 |
+
value: 0.33794
|
| 863 |
+
- type: ndcg_at_10
|
| 864 |
+
value: 0.43832
|
| 865 |
+
- type: ndcg_at_100
|
| 866 |
+
value: 0.49514
|
| 867 |
+
- type: ndcg_at_1000
|
| 868 |
+
value: 0.51742
|
| 869 |
+
- type: ndcg_at_3
|
| 870 |
+
value: 0.38442
|
| 871 |
+
- type: ndcg_at_5
|
| 872 |
+
value: 0.40737
|
| 873 |
+
- type: precision_at_1
|
| 874 |
+
value: 0.33794
|
| 875 |
+
- type: precision_at_10
|
| 876 |
+
value: 0.08597
|
| 877 |
+
- type: precision_at_100
|
| 878 |
+
value: 0.01652
|
| 879 |
+
- type: precision_at_1000
|
| 880 |
+
value: 0.00251
|
| 881 |
+
- type: precision_at_3
|
| 882 |
+
value: 0.17787
|
| 883 |
+
- type: precision_at_5
|
| 884 |
+
value: 0.13241
|
| 885 |
+
- type: recall_at_1
|
| 886 |
+
value: 0.28409
|
| 887 |
+
- type: recall_at_10
|
| 888 |
+
value: 0.55388
|
| 889 |
+
- type: recall_at_100
|
| 890 |
+
value: 0.81517
|
| 891 |
+
- type: recall_at_1000
|
| 892 |
+
value: 0.95038
|
| 893 |
+
- type: recall_at_3
|
| 894 |
+
value: 0.40133
|
| 895 |
+
- type: recall_at_5
|
| 896 |
+
value: 0.45913
|
| 897 |
+
- dataset:
|
| 898 |
+
type: mteb/cqadupstack-wordpress
|
| 899 |
+
name: MTEB CQADupstackWordpressRetrieval
|
| 900 |
+
config: default
|
| 901 |
+
split: test
|
| 902 |
+
task:
|
| 903 |
+
type: Retrieval
|
| 904 |
+
metrics:
|
| 905 |
+
- type: map_at_1
|
| 906 |
+
value: 0.24067
|
| 907 |
+
- type: map_at_10
|
| 908 |
+
value: 0.32184
|
| 909 |
+
- type: map_at_100
|
| 910 |
+
value: 0.33357
|
| 911 |
+
- type: map_at_1000
|
| 912 |
+
value: 0.33458
|
| 913 |
+
- type: map_at_3
|
| 914 |
+
value: 0.29492
|
| 915 |
+
- type: map_at_5
|
| 916 |
+
value: 0.3111
|
| 917 |
+
- type: mrr_at_1
|
| 918 |
+
value: 0.26248
|
| 919 |
+
- type: mrr_at_10
|
| 920 |
+
value: 0.34149
|
| 921 |
+
- type: mrr_at_100
|
| 922 |
+
value: 0.35189
|
| 923 |
+
- type: mrr_at_1000
|
| 924 |
+
value: 0.35251
|
| 925 |
+
- type: mrr_at_3
|
| 926 |
+
value: 0.31639
|
| 927 |
+
- type: mrr_at_5
|
| 928 |
+
value: 0.33182
|
| 929 |
+
- type: ndcg_at_1
|
| 930 |
+
value: 0.26248
|
| 931 |
+
- type: ndcg_at_10
|
| 932 |
+
value: 0.36889
|
| 933 |
+
- type: ndcg_at_100
|
| 934 |
+
value: 0.42426
|
| 935 |
+
- type: ndcg_at_1000
|
| 936 |
+
value: 0.44745
|
| 937 |
+
- type: ndcg_at_3
|
| 938 |
+
value: 0.31799
|
| 939 |
+
- type: ndcg_at_5
|
| 940 |
+
value: 0.34563
|
| 941 |
+
- type: precision_at_1
|
| 942 |
+
value: 0.26248
|
| 943 |
+
- type: precision_at_10
|
| 944 |
+
value: 0.05712
|
| 945 |
+
- type: precision_at_100
|
| 946 |
+
value: 0.00915
|
| 947 |
+
- type: precision_at_1000
|
| 948 |
+
value: 0.00123
|
| 949 |
+
- type: precision_at_3
|
| 950 |
+
value: 0.13309
|
| 951 |
+
- type: precision_at_5
|
| 952 |
+
value: 0.09649
|
| 953 |
+
- type: recall_at_1
|
| 954 |
+
value: 0.24067
|
| 955 |
+
- type: recall_at_10
|
| 956 |
+
value: 0.49344
|
| 957 |
+
- type: recall_at_100
|
| 958 |
+
value: 0.7412
|
| 959 |
+
- type: recall_at_1000
|
| 960 |
+
value: 0.91276
|
| 961 |
+
- type: recall_at_3
|
| 962 |
+
value: 0.36272
|
| 963 |
+
- type: recall_at_5
|
| 964 |
+
value: 0.4277
|
| 965 |
+
- dataset:
|
| 966 |
+
type: mteb/dbpedia
|
| 967 |
+
name: MTEB DBPedia
|
| 968 |
+
config: default
|
| 969 |
+
split: test
|
| 970 |
+
task:
|
| 971 |
+
type: Retrieval
|
| 972 |
+
metrics:
|
| 973 |
+
- type: map_at_1
|
| 974 |
+
value: 0.08651
|
| 975 |
+
- type: map_at_10
|
| 976 |
+
value: 0.17628
|
| 977 |
+
- type: map_at_100
|
| 978 |
+
value: 0.23354
|
| 979 |
+
- type: map_at_1000
|
| 980 |
+
value: 0.24827
|
| 981 |
+
- type: map_at_3
|
| 982 |
+
value: 0.1351
|
| 983 |
+
- type: map_at_5
|
| 984 |
+
value: 0.15468
|
| 985 |
+
- type: mrr_at_1
|
| 986 |
+
value: 0.645
|
| 987 |
+
- type: mrr_at_10
|
| 988 |
+
value: 0.71989
|
| 989 |
+
- type: mrr_at_100
|
| 990 |
+
value: 0.72332
|
| 991 |
+
- type: mrr_at_1000
|
| 992 |
+
value: 0.72346
|
| 993 |
+
- type: mrr_at_3
|
| 994 |
+
value: 0.7025
|
| 995 |
+
- type: mrr_at_5
|
| 996 |
+
value: 0.71275
|
| 997 |
+
- type: ndcg_at_1
|
| 998 |
+
value: 0.51375
|
| 999 |
+
- type: ndcg_at_10
|
| 1000 |
+
value: 0.3596
|
| 1001 |
+
- type: ndcg_at_100
|
| 1002 |
+
value: 0.39878
|
| 1003 |
+
- type: ndcg_at_1000
|
| 1004 |
+
value: 0.47931
|
| 1005 |
+
- type: ndcg_at_3
|
| 1006 |
+
value: 0.41275
|
| 1007 |
+
- type: ndcg_at_5
|
| 1008 |
+
value: 0.38297
|
| 1009 |
+
- type: precision_at_1
|
| 1010 |
+
value: 0.645
|
| 1011 |
+
- type: precision_at_10
|
| 1012 |
+
value: 0.2745
|
| 1013 |
+
- type: precision_at_100
|
| 1014 |
+
value: 0.08405
|
| 1015 |
+
- type: precision_at_1000
|
| 1016 |
+
value: 0.01923
|
| 1017 |
+
- type: precision_at_3
|
| 1018 |
+
value: 0.44417
|
| 1019 |
+
- type: precision_at_5
|
| 1020 |
+
value: 0.366
|
| 1021 |
+
- type: recall_at_1
|
| 1022 |
+
value: 0.08651
|
| 1023 |
+
- type: recall_at_10
|
| 1024 |
+
value: 0.22416
|
| 1025 |
+
- type: recall_at_100
|
| 1026 |
+
value: 0.46381
|
| 1027 |
+
- type: recall_at_1000
|
| 1028 |
+
value: 0.71557
|
| 1029 |
+
- type: recall_at_3
|
| 1030 |
+
value: 0.14847
|
| 1031 |
+
- type: recall_at_5
|
| 1032 |
+
value: 0.1804
|
| 1033 |
+
- dataset:
|
| 1034 |
+
type: mteb/fever
|
| 1035 |
+
name: MTEB FEVER
|
| 1036 |
+
config: default
|
| 1037 |
+
split: test
|
| 1038 |
+
task:
|
| 1039 |
+
type: Retrieval
|
| 1040 |
+
metrics:
|
| 1041 |
+
- type: map_at_1
|
| 1042 |
+
value: 0.73211
|
| 1043 |
+
- type: map_at_10
|
| 1044 |
+
value: 0.81463
|
| 1045 |
+
- type: map_at_100
|
| 1046 |
+
value: 0.81622
|
| 1047 |
+
- type: map_at_1000
|
| 1048 |
+
value: 0.81634
|
| 1049 |
+
- type: map_at_3
|
| 1050 |
+
value: 0.805
|
| 1051 |
+
- type: map_at_5
|
| 1052 |
+
value: 0.81134
|
| 1053 |
+
- type: mrr_at_1
|
| 1054 |
+
value: 0.79088
|
| 1055 |
+
- type: mrr_at_10
|
| 1056 |
+
value: 0.86943
|
| 1057 |
+
- type: mrr_at_100
|
| 1058 |
+
value: 0.87017
|
| 1059 |
+
- type: mrr_at_1000
|
| 1060 |
+
value: 0.87018
|
| 1061 |
+
- type: mrr_at_3
|
| 1062 |
+
value: 0.86154
|
| 1063 |
+
- type: mrr_at_5
|
| 1064 |
+
value: 0.867
|
| 1065 |
+
- type: ndcg_at_1
|
| 1066 |
+
value: 0.79088
|
| 1067 |
+
- type: ndcg_at_10
|
| 1068 |
+
value: 0.85528
|
| 1069 |
+
- type: ndcg_at_100
|
| 1070 |
+
value: 0.86134
|
| 1071 |
+
- type: ndcg_at_1000
|
| 1072 |
+
value: 0.86367
|
| 1073 |
+
- type: ndcg_at_3
|
| 1074 |
+
value: 0.83943
|
| 1075 |
+
- type: ndcg_at_5
|
| 1076 |
+
value: 0.84878
|
| 1077 |
+
- type: precision_at_1
|
| 1078 |
+
value: 0.79088
|
| 1079 |
+
- type: precision_at_10
|
| 1080 |
+
value: 0.10132
|
| 1081 |
+
- type: precision_at_100
|
| 1082 |
+
value: 0.01055
|
| 1083 |
+
- type: precision_at_1000
|
| 1084 |
+
value: 0.00109
|
| 1085 |
+
- type: precision_at_3
|
| 1086 |
+
value: 0.31963
|
| 1087 |
+
- type: precision_at_5
|
| 1088 |
+
value: 0.19769
|
| 1089 |
+
- type: recall_at_1
|
| 1090 |
+
value: 0.73211
|
| 1091 |
+
- type: recall_at_10
|
| 1092 |
+
value: 0.92797
|
| 1093 |
+
- type: recall_at_100
|
| 1094 |
+
value: 0.95263
|
| 1095 |
+
- type: recall_at_1000
|
| 1096 |
+
value: 0.96738
|
| 1097 |
+
- type: recall_at_3
|
| 1098 |
+
value: 0.88328
|
| 1099 |
+
- type: recall_at_5
|
| 1100 |
+
value: 0.90821
|
| 1101 |
+
- dataset:
|
| 1102 |
+
type: mteb/fiqa
|
| 1103 |
+
name: MTEB FiQA2018
|
| 1104 |
+
config: default
|
| 1105 |
+
split: test
|
| 1106 |
+
task:
|
| 1107 |
+
type: Retrieval
|
| 1108 |
+
metrics:
|
| 1109 |
+
- type: map_at_1
|
| 1110 |
+
value: 0.18311
|
| 1111 |
+
- type: map_at_10
|
| 1112 |
+
value: 0.29201
|
| 1113 |
+
- type: map_at_100
|
| 1114 |
+
value: 0.3093
|
| 1115 |
+
- type: map_at_1000
|
| 1116 |
+
value: 0.31116
|
| 1117 |
+
- type: map_at_3
|
| 1118 |
+
value: 0.24778
|
| 1119 |
+
- type: map_at_5
|
| 1120 |
+
value: 0.27453
|
| 1121 |
+
- type: mrr_at_1
|
| 1122 |
+
value: 0.35494
|
| 1123 |
+
- type: mrr_at_10
|
| 1124 |
+
value: 0.44489
|
| 1125 |
+
- type: mrr_at_100
|
| 1126 |
+
value: 0.4532
|
| 1127 |
+
- type: mrr_at_1000
|
| 1128 |
+
value: 0.45369
|
| 1129 |
+
- type: mrr_at_3
|
| 1130 |
+
value: 0.41667
|
| 1131 |
+
- type: mrr_at_5
|
| 1132 |
+
value: 0.43418
|
| 1133 |
+
- type: ndcg_at_1
|
| 1134 |
+
value: 0.35494
|
| 1135 |
+
- type: ndcg_at_10
|
| 1136 |
+
value: 0.36868
|
| 1137 |
+
- type: ndcg_at_100
|
| 1138 |
+
value: 0.43463
|
| 1139 |
+
- type: ndcg_at_1000
|
| 1140 |
+
value: 0.46766
|
| 1141 |
+
- type: ndcg_at_3
|
| 1142 |
+
value: 0.32305
|
| 1143 |
+
- type: ndcg_at_5
|
| 1144 |
+
value: 0.34332
|
| 1145 |
+
- type: precision_at_1
|
| 1146 |
+
value: 0.35494
|
| 1147 |
+
- type: precision_at_10
|
| 1148 |
+
value: 0.10324
|
| 1149 |
+
- type: precision_at_100
|
| 1150 |
+
value: 0.01707
|
| 1151 |
+
- type: precision_at_1000
|
| 1152 |
+
value: 0.00229
|
| 1153 |
+
- type: precision_at_3
|
| 1154 |
+
value: 0.21142
|
| 1155 |
+
- type: precision_at_5
|
| 1156 |
+
value: 0.16327
|
| 1157 |
+
- type: recall_at_1
|
| 1158 |
+
value: 0.18311
|
| 1159 |
+
- type: recall_at_10
|
| 1160 |
+
value: 0.43881
|
| 1161 |
+
- type: recall_at_100
|
| 1162 |
+
value: 0.68593
|
| 1163 |
+
- type: recall_at_1000
|
| 1164 |
+
value: 0.8855
|
| 1165 |
+
- type: recall_at_3
|
| 1166 |
+
value: 0.28824
|
| 1167 |
+
- type: recall_at_5
|
| 1168 |
+
value: 0.36178
|
| 1169 |
+
- dataset:
|
| 1170 |
+
type: mteb/hotpotqa
|
| 1171 |
+
name: MTEB HotpotQA
|
| 1172 |
+
config: default
|
| 1173 |
+
split: test
|
| 1174 |
+
task:
|
| 1175 |
+
type: Retrieval
|
| 1176 |
+
metrics:
|
| 1177 |
+
- type: map_at_1
|
| 1178 |
+
value: 0.36766
|
| 1179 |
+
- type: map_at_10
|
| 1180 |
+
value: 0.53639
|
| 1181 |
+
- type: map_at_100
|
| 1182 |
+
value: 0.54532
|
| 1183 |
+
- type: map_at_1000
|
| 1184 |
+
value: 0.54608
|
| 1185 |
+
- type: map_at_3
|
| 1186 |
+
value: 0.50427
|
| 1187 |
+
- type: map_at_5
|
| 1188 |
+
value: 0.5245
|
| 1189 |
+
- type: mrr_at_1
|
| 1190 |
+
value: 0.73531
|
| 1191 |
+
- type: mrr_at_10
|
| 1192 |
+
value: 0.80104
|
| 1193 |
+
- type: mrr_at_100
|
| 1194 |
+
value: 0.80341
|
| 1195 |
+
- type: mrr_at_1000
|
| 1196 |
+
value: 0.80351
|
| 1197 |
+
- type: mrr_at_3
|
| 1198 |
+
value: 0.78949
|
| 1199 |
+
- type: mrr_at_5
|
| 1200 |
+
value: 0.79729
|
| 1201 |
+
- type: ndcg_at_1
|
| 1202 |
+
value: 0.73531
|
| 1203 |
+
- type: ndcg_at_10
|
| 1204 |
+
value: 0.62918
|
| 1205 |
+
- type: ndcg_at_100
|
| 1206 |
+
value: 0.66056
|
| 1207 |
+
- type: ndcg_at_1000
|
| 1208 |
+
value: 0.67554
|
| 1209 |
+
- type: ndcg_at_3
|
| 1210 |
+
value: 0.58247
|
| 1211 |
+
- type: ndcg_at_5
|
| 1212 |
+
value: 0.60905
|
| 1213 |
+
- type: precision_at_1
|
| 1214 |
+
value: 0.73531
|
| 1215 |
+
- type: precision_at_10
|
| 1216 |
+
value: 0.1302
|
| 1217 |
+
- type: precision_at_100
|
| 1218 |
+
value: 0.01546
|
| 1219 |
+
- type: precision_at_1000
|
| 1220 |
+
value: 0.00175
|
| 1221 |
+
- type: precision_at_3
|
| 1222 |
+
value: 0.36556
|
| 1223 |
+
- type: precision_at_5
|
| 1224 |
+
value: 0.24032
|
| 1225 |
+
- type: recall_at_1
|
| 1226 |
+
value: 0.36766
|
| 1227 |
+
- type: recall_at_10
|
| 1228 |
+
value: 0.65098
|
| 1229 |
+
- type: recall_at_100
|
| 1230 |
+
value: 0.77306
|
| 1231 |
+
- type: recall_at_1000
|
| 1232 |
+
value: 0.87252
|
| 1233 |
+
- type: recall_at_3
|
| 1234 |
+
value: 0.54835
|
| 1235 |
+
- type: recall_at_5
|
| 1236 |
+
value: 0.60081
|
| 1237 |
+
- dataset:
|
| 1238 |
+
type: mteb/msmarco
|
| 1239 |
+
name: MTEB MSMARCO
|
| 1240 |
+
config: default
|
| 1241 |
+
split: dev
|
| 1242 |
+
task:
|
| 1243 |
+
type: Retrieval
|
| 1244 |
+
metrics:
|
| 1245 |
+
- type: map_at_1
|
| 1246 |
+
value: 0.14654
|
| 1247 |
+
- type: map_at_10
|
| 1248 |
+
value: 0.2472
|
| 1249 |
+
- type: map_at_100
|
| 1250 |
+
value: 0.25994
|
| 1251 |
+
- type: map_at_1000
|
| 1252 |
+
value: 0.26067
|
| 1253 |
+
- type: map_at_3
|
| 1254 |
+
value: 0.21234
|
| 1255 |
+
- type: map_at_5
|
| 1256 |
+
value: 0.2319
|
| 1257 |
+
- type: mrr_at_1
|
| 1258 |
+
value: 0.15086
|
| 1259 |
+
- type: mrr_at_10
|
| 1260 |
+
value: 0.25184
|
| 1261 |
+
- type: mrr_at_100
|
| 1262 |
+
value: 0.26422
|
| 1263 |
+
- type: mrr_at_1000
|
| 1264 |
+
value: 0.26489
|
| 1265 |
+
- type: mrr_at_3
|
| 1266 |
+
value: 0.21731
|
| 1267 |
+
- type: mrr_at_5
|
| 1268 |
+
value: 0.23674
|
| 1269 |
+
- type: ndcg_at_1
|
| 1270 |
+
value: 0.15086
|
| 1271 |
+
- type: ndcg_at_10
|
| 1272 |
+
value: 0.30711
|
| 1273 |
+
- type: ndcg_at_100
|
| 1274 |
+
value: 0.37221
|
| 1275 |
+
- type: ndcg_at_1000
|
| 1276 |
+
value: 0.39133
|
| 1277 |
+
- type: ndcg_at_3
|
| 1278 |
+
value: 0.23567
|
| 1279 |
+
- type: ndcg_at_5
|
| 1280 |
+
value: 0.27066
|
| 1281 |
+
- type: precision_at_1
|
| 1282 |
+
value: 0.15086
|
| 1283 |
+
- type: precision_at_10
|
| 1284 |
+
value: 0.05132
|
| 1285 |
+
- type: precision_at_100
|
| 1286 |
+
value: 0.00845
|
| 1287 |
+
- type: precision_at_1000
|
| 1288 |
+
value: 0.00101
|
| 1289 |
+
- type: precision_at_3
|
| 1290 |
+
value: 0.10277
|
| 1291 |
+
- type: precision_at_5
|
| 1292 |
+
value: 0.07923
|
| 1293 |
+
- type: recall_at_1
|
| 1294 |
+
value: 0.14654
|
| 1295 |
+
- type: recall_at_10
|
| 1296 |
+
value: 0.49341
|
| 1297 |
+
- type: recall_at_100
|
| 1298 |
+
value: 0.80224
|
| 1299 |
+
- type: recall_at_1000
|
| 1300 |
+
value: 0.95037
|
| 1301 |
+
- type: recall_at_3
|
| 1302 |
+
value: 0.29862
|
| 1303 |
+
- type: recall_at_5
|
| 1304 |
+
value: 0.38274
|
| 1305 |
+
- dataset:
|
| 1306 |
+
type: mteb/nfcorpus
|
| 1307 |
+
name: MTEB NFCorpus
|
| 1308 |
+
config: default
|
| 1309 |
+
split: test
|
| 1310 |
+
task:
|
| 1311 |
+
type: Retrieval
|
| 1312 |
+
metrics:
|
| 1313 |
+
- type: map_at_1
|
| 1314 |
+
value: 0.05452
|
| 1315 |
+
- type: map_at_10
|
| 1316 |
+
value: 0.12758
|
| 1317 |
+
- type: map_at_100
|
| 1318 |
+
value: 0.1593
|
| 1319 |
+
- type: map_at_1000
|
| 1320 |
+
value: 0.17422
|
| 1321 |
+
- type: map_at_3
|
| 1322 |
+
value: 0.0945
|
| 1323 |
+
- type: map_at_5
|
| 1324 |
+
value: 0.1092
|
| 1325 |
+
- type: mrr_at_1
|
| 1326 |
+
value: 0.43963
|
| 1327 |
+
- type: mrr_at_10
|
| 1328 |
+
value: 0.53237
|
| 1329 |
+
- type: mrr_at_100
|
| 1330 |
+
value: 0.53777
|
| 1331 |
+
- type: mrr_at_1000
|
| 1332 |
+
value: 0.53822
|
| 1333 |
+
- type: mrr_at_3
|
| 1334 |
+
value: 0.51445
|
| 1335 |
+
- type: mrr_at_5
|
| 1336 |
+
value: 0.52466
|
| 1337 |
+
- type: ndcg_at_1
|
| 1338 |
+
value: 0.41486
|
| 1339 |
+
- type: ndcg_at_10
|
| 1340 |
+
value: 0.33737
|
| 1341 |
+
- type: ndcg_at_100
|
| 1342 |
+
value: 0.30886
|
| 1343 |
+
- type: ndcg_at_1000
|
| 1344 |
+
value: 0.40018
|
| 1345 |
+
- type: ndcg_at_3
|
| 1346 |
+
value: 0.39324
|
| 1347 |
+
- type: ndcg_at_5
|
| 1348 |
+
value: 0.36949
|
| 1349 |
+
- type: precision_at_1
|
| 1350 |
+
value: 0.43344
|
| 1351 |
+
- type: precision_at_10
|
| 1352 |
+
value: 0.24799
|
| 1353 |
+
- type: precision_at_100
|
| 1354 |
+
value: 0.07895
|
| 1355 |
+
- type: precision_at_1000
|
| 1356 |
+
value: 0.02091
|
| 1357 |
+
- type: precision_at_3
|
| 1358 |
+
value: 0.37152
|
| 1359 |
+
- type: precision_at_5
|
| 1360 |
+
value: 0.31703
|
| 1361 |
+
- type: recall_at_1
|
| 1362 |
+
value: 0.05452
|
| 1363 |
+
- type: recall_at_10
|
| 1364 |
+
value: 0.1712
|
| 1365 |
+
- type: recall_at_100
|
| 1366 |
+
value: 0.30719
|
| 1367 |
+
- type: recall_at_1000
|
| 1368 |
+
value: 0.62766
|
| 1369 |
+
- type: recall_at_3
|
| 1370 |
+
value: 0.10733
|
| 1371 |
+
- type: recall_at_5
|
| 1372 |
+
value: 0.13553
|
| 1373 |
+
- dataset:
|
| 1374 |
+
type: mteb/nq
|
| 1375 |
+
name: MTEB NQ
|
| 1376 |
+
config: default
|
| 1377 |
+
split: test
|
| 1378 |
+
task:
|
| 1379 |
+
type: Retrieval
|
| 1380 |
+
metrics:
|
| 1381 |
+
- type: map_at_1
|
| 1382 |
+
value: 0.29022
|
| 1383 |
+
- type: map_at_10
|
| 1384 |
+
value: 0.4373
|
| 1385 |
+
- type: map_at_100
|
| 1386 |
+
value: 0.44849
|
| 1387 |
+
- type: map_at_1000
|
| 1388 |
+
value: 0.44877
|
| 1389 |
+
- type: map_at_3
|
| 1390 |
+
value: 0.39045
|
| 1391 |
+
- type: map_at_5
|
| 1392 |
+
value: 0.4186
|
| 1393 |
+
- type: mrr_at_1
|
| 1394 |
+
value: 0.32793
|
| 1395 |
+
- type: mrr_at_10
|
| 1396 |
+
value: 0.46243
|
| 1397 |
+
- type: mrr_at_100
|
| 1398 |
+
value: 0.47083
|
| 1399 |
+
- type: mrr_at_1000
|
| 1400 |
+
value: 0.47101
|
| 1401 |
+
- type: mrr_at_3
|
| 1402 |
+
value: 0.42261
|
| 1403 |
+
- type: mrr_at_5
|
| 1404 |
+
value: 0.44775
|
| 1405 |
+
- type: ndcg_at_1
|
| 1406 |
+
value: 0.32793
|
| 1407 |
+
- type: ndcg_at_10
|
| 1408 |
+
value: 0.51631
|
| 1409 |
+
- type: ndcg_at_100
|
| 1410 |
+
value: 0.56287
|
| 1411 |
+
- type: ndcg_at_1000
|
| 1412 |
+
value: 0.56949
|
| 1413 |
+
- type: ndcg_at_3
|
| 1414 |
+
value: 0.42782
|
| 1415 |
+
- type: ndcg_at_5
|
| 1416 |
+
value: 0.47554
|
| 1417 |
+
- type: precision_at_1
|
| 1418 |
+
value: 0.32793
|
| 1419 |
+
- type: precision_at_10
|
| 1420 |
+
value: 0.08737
|
| 1421 |
+
- type: precision_at_100
|
| 1422 |
+
value: 0.01134
|
| 1423 |
+
- type: precision_at_1000
|
| 1424 |
+
value: 0.0012
|
| 1425 |
+
- type: precision_at_3
|
| 1426 |
+
value: 0.19583
|
| 1427 |
+
- type: precision_at_5
|
| 1428 |
+
value: 0.14484
|
| 1429 |
+
- type: recall_at_1
|
| 1430 |
+
value: 0.29022
|
| 1431 |
+
- type: recall_at_10
|
| 1432 |
+
value: 0.73325
|
| 1433 |
+
- type: recall_at_100
|
| 1434 |
+
value: 0.93455
|
| 1435 |
+
- type: recall_at_1000
|
| 1436 |
+
value: 0.98414
|
| 1437 |
+
- type: recall_at_3
|
| 1438 |
+
value: 0.50406
|
| 1439 |
+
- type: recall_at_5
|
| 1440 |
+
value: 0.6145
|
| 1441 |
+
- dataset:
|
| 1442 |
+
type: mteb/quora
|
| 1443 |
+
name: MTEB QuoraRetrieval
|
| 1444 |
+
config: default
|
| 1445 |
+
split: test
|
| 1446 |
+
task:
|
| 1447 |
+
type: Retrieval
|
| 1448 |
+
metrics:
|
| 1449 |
+
- type: map_at_1
|
| 1450 |
+
value: 0.68941
|
| 1451 |
+
- type: map_at_10
|
| 1452 |
+
value: 0.82641
|
| 1453 |
+
- type: map_at_100
|
| 1454 |
+
value: 0.83317
|
| 1455 |
+
- type: map_at_1000
|
| 1456 |
+
value: 0.83337
|
| 1457 |
+
- type: map_at_3
|
| 1458 |
+
value: 0.79604
|
| 1459 |
+
- type: map_at_5
|
| 1460 |
+
value: 0.81525
|
| 1461 |
+
- type: mrr_at_1
|
| 1462 |
+
value: 0.7935
|
| 1463 |
+
- type: mrr_at_10
|
| 1464 |
+
value: 0.85969
|
| 1465 |
+
- type: mrr_at_100
|
| 1466 |
+
value: 0.86094
|
| 1467 |
+
- type: mrr_at_1000
|
| 1468 |
+
value: 0.86095
|
| 1469 |
+
- type: mrr_at_3
|
| 1470 |
+
value: 0.84852
|
| 1471 |
+
- type: mrr_at_5
|
| 1472 |
+
value: 0.85627
|
| 1473 |
+
- type: ndcg_at_1
|
| 1474 |
+
value: 0.7936
|
| 1475 |
+
- type: ndcg_at_10
|
| 1476 |
+
value: 0.86687
|
| 1477 |
+
- type: ndcg_at_100
|
| 1478 |
+
value: 0.88094
|
| 1479 |
+
- type: ndcg_at_1000
|
| 1480 |
+
value: 0.88243
|
| 1481 |
+
- type: ndcg_at_3
|
| 1482 |
+
value: 0.83538
|
| 1483 |
+
- type: ndcg_at_5
|
| 1484 |
+
value: 0.85308
|
| 1485 |
+
- type: precision_at_1
|
| 1486 |
+
value: 0.7936
|
| 1487 |
+
- type: precision_at_10
|
| 1488 |
+
value: 0.13145
|
| 1489 |
+
- type: precision_at_100
|
| 1490 |
+
value: 0.01517
|
| 1491 |
+
- type: precision_at_1000
|
| 1492 |
+
value: 0.00156
|
| 1493 |
+
- type: precision_at_3
|
| 1494 |
+
value: 0.36353
|
| 1495 |
+
- type: precision_at_5
|
| 1496 |
+
value: 0.24044
|
| 1497 |
+
- type: recall_at_1
|
| 1498 |
+
value: 0.68941
|
| 1499 |
+
- type: recall_at_10
|
| 1500 |
+
value: 0.94407
|
| 1501 |
+
- type: recall_at_100
|
| 1502 |
+
value: 0.99226
|
| 1503 |
+
- type: recall_at_1000
|
| 1504 |
+
value: 0.99958
|
| 1505 |
+
- type: recall_at_3
|
| 1506 |
+
value: 0.85502
|
| 1507 |
+
- type: recall_at_5
|
| 1508 |
+
value: 0.90372
|
| 1509 |
+
- dataset:
|
| 1510 |
+
type: mteb/scidocs
|
| 1511 |
+
name: MTEB SCIDOCS
|
| 1512 |
+
config: default
|
| 1513 |
+
split: test
|
| 1514 |
+
task:
|
| 1515 |
+
type: Retrieval
|
| 1516 |
+
metrics:
|
| 1517 |
+
- type: map_at_1
|
| 1518 |
+
value: 0.04988
|
| 1519 |
+
- type: map_at_10
|
| 1520 |
+
value: 0.13553
|
| 1521 |
+
- type: map_at_100
|
| 1522 |
+
value: 0.16136
|
| 1523 |
+
- type: map_at_1000
|
| 1524 |
+
value: 0.16512
|
| 1525 |
+
- type: map_at_3
|
| 1526 |
+
value: 0.09439
|
| 1527 |
+
- type: map_at_5
|
| 1528 |
+
value: 0.1146
|
| 1529 |
+
- type: mrr_at_1
|
| 1530 |
+
value: 0.246
|
| 1531 |
+
- type: mrr_at_10
|
| 1532 |
+
value: 0.36792
|
| 1533 |
+
- type: mrr_at_100
|
| 1534 |
+
value: 0.37973
|
| 1535 |
+
- type: mrr_at_1000
|
| 1536 |
+
value: 0.38011
|
| 1537 |
+
- type: mrr_at_3
|
| 1538 |
+
value: 0.33117
|
| 1539 |
+
- type: mrr_at_5
|
| 1540 |
+
value: 0.35172
|
| 1541 |
+
- type: ndcg_at_1
|
| 1542 |
+
value: 0.246
|
| 1543 |
+
- type: ndcg_at_10
|
| 1544 |
+
value: 0.22542
|
| 1545 |
+
- type: ndcg_at_100
|
| 1546 |
+
value: 0.32326
|
| 1547 |
+
- type: ndcg_at_1000
|
| 1548 |
+
value: 0.3828
|
| 1549 |
+
- type: ndcg_at_3
|
| 1550 |
+
value: 0.20896
|
| 1551 |
+
- type: ndcg_at_5
|
| 1552 |
+
value: 0.18497
|
| 1553 |
+
- type: precision_at_1
|
| 1554 |
+
value: 0.246
|
| 1555 |
+
- type: precision_at_10
|
| 1556 |
+
value: 0.1194
|
| 1557 |
+
- type: precision_at_100
|
| 1558 |
+
value: 0.02616
|
| 1559 |
+
- type: precision_at_1000
|
| 1560 |
+
value: 0.00404
|
| 1561 |
+
- type: precision_at_3
|
| 1562 |
+
value: 0.198
|
| 1563 |
+
- type: precision_at_5
|
| 1564 |
+
value: 0.1654
|
| 1565 |
+
- type: recall_at_1
|
| 1566 |
+
value: 0.04988
|
| 1567 |
+
- type: recall_at_10
|
| 1568 |
+
value: 0.24212
|
| 1569 |
+
- type: recall_at_100
|
| 1570 |
+
value: 0.53105
|
| 1571 |
+
- type: recall_at_1000
|
| 1572 |
+
value: 0.82022
|
| 1573 |
+
- type: recall_at_3
|
| 1574 |
+
value: 0.12047
|
| 1575 |
+
- type: recall_at_5
|
| 1576 |
+
value: 0.16777
|
| 1577 |
+
- dataset:
|
| 1578 |
+
type: mteb/scifact
|
| 1579 |
+
name: MTEB SciFact
|
| 1580 |
+
config: default
|
| 1581 |
+
split: test
|
| 1582 |
+
task:
|
| 1583 |
+
type: Retrieval
|
| 1584 |
+
metrics:
|
| 1585 |
+
- type: map_at_1
|
| 1586 |
+
value: 0.56578
|
| 1587 |
+
- type: map_at_10
|
| 1588 |
+
value: 0.66725
|
| 1589 |
+
- type: map_at_100
|
| 1590 |
+
value: 0.67379
|
| 1591 |
+
- type: map_at_1000
|
| 1592 |
+
value: 0.674
|
| 1593 |
+
- type: map_at_3
|
| 1594 |
+
value: 0.63416
|
| 1595 |
+
- type: map_at_5
|
| 1596 |
+
value: 0.6577
|
| 1597 |
+
- type: mrr_at_1
|
| 1598 |
+
value: 0.59333
|
| 1599 |
+
- type: mrr_at_10
|
| 1600 |
+
value: 0.67533
|
| 1601 |
+
- type: mrr_at_100
|
| 1602 |
+
value: 0.68062
|
| 1603 |
+
- type: mrr_at_1000
|
| 1604 |
+
value: 0.68082
|
| 1605 |
+
- type: mrr_at_3
|
| 1606 |
+
value: 0.64944
|
| 1607 |
+
- type: mrr_at_5
|
| 1608 |
+
value: 0.66928
|
| 1609 |
+
- type: ndcg_at_1
|
| 1610 |
+
value: 0.59333
|
| 1611 |
+
- type: ndcg_at_10
|
| 1612 |
+
value: 0.7127
|
| 1613 |
+
- type: ndcg_at_100
|
| 1614 |
+
value: 0.73889
|
| 1615 |
+
- type: ndcg_at_1000
|
| 1616 |
+
value: 0.7441
|
| 1617 |
+
- type: ndcg_at_3
|
| 1618 |
+
value: 0.65793
|
| 1619 |
+
- type: ndcg_at_5
|
| 1620 |
+
value: 0.69429
|
| 1621 |
+
- type: precision_at_1
|
| 1622 |
+
value: 0.59333
|
| 1623 |
+
- type: precision_at_10
|
| 1624 |
+
value: 0.096
|
| 1625 |
+
- type: precision_at_100
|
| 1626 |
+
value: 0.01087
|
| 1627 |
+
- type: precision_at_1000
|
| 1628 |
+
value: 0.00113
|
| 1629 |
+
- type: precision_at_3
|
| 1630 |
+
value: 0.25556
|
| 1631 |
+
- type: precision_at_5
|
| 1632 |
+
value: 0.17667
|
| 1633 |
+
- type: recall_at_1
|
| 1634 |
+
value: 0.56578
|
| 1635 |
+
- type: recall_at_10
|
| 1636 |
+
value: 0.842
|
| 1637 |
+
- type: recall_at_100
|
| 1638 |
+
value: 0.95667
|
| 1639 |
+
- type: recall_at_1000
|
| 1640 |
+
value: 0.99667
|
| 1641 |
+
- type: recall_at_3
|
| 1642 |
+
value: 0.70072
|
| 1643 |
+
- type: recall_at_5
|
| 1644 |
+
value: 0.79011
|
| 1645 |
+
- dataset:
|
| 1646 |
+
type: mteb/touche2020
|
| 1647 |
+
name: MTEB Touche2020
|
| 1648 |
+
config: default
|
| 1649 |
+
split: test
|
| 1650 |
+
task:
|
| 1651 |
+
type: Retrieval
|
| 1652 |
+
metrics:
|
| 1653 |
+
- type: map_at_1
|
| 1654 |
+
value: 0.01976
|
| 1655 |
+
- type: map_at_10
|
| 1656 |
+
value: 0.09688
|
| 1657 |
+
- type: map_at_100
|
| 1658 |
+
value: 0.15117
|
| 1659 |
+
- type: map_at_1000
|
| 1660 |
+
value: 0.16769
|
| 1661 |
+
- type: map_at_3
|
| 1662 |
+
value: 0.04589
|
| 1663 |
+
- type: map_at_5
|
| 1664 |
+
value: 0.06556
|
| 1665 |
+
- type: mrr_at_1
|
| 1666 |
+
value: 0.26531
|
| 1667 |
+
- type: mrr_at_10
|
| 1668 |
+
value: 0.43863
|
| 1669 |
+
- type: mrr_at_100
|
| 1670 |
+
value: 0.44767
|
| 1671 |
+
- type: mrr_at_1000
|
| 1672 |
+
value: 0.44767
|
| 1673 |
+
- type: mrr_at_3
|
| 1674 |
+
value: 0.39116
|
| 1675 |
+
- type: mrr_at_5
|
| 1676 |
+
value: 0.41156
|
| 1677 |
+
- type: ndcg_at_1
|
| 1678 |
+
value: 0.23469
|
| 1679 |
+
- type: ndcg_at_10
|
| 1680 |
+
value: 0.24029
|
| 1681 |
+
- type: ndcg_at_100
|
| 1682 |
+
value: 0.34425
|
| 1683 |
+
- type: ndcg_at_1000
|
| 1684 |
+
value: 0.46907
|
| 1685 |
+
- type: ndcg_at_3
|
| 1686 |
+
value: 0.25522
|
| 1687 |
+
- type: ndcg_at_5
|
| 1688 |
+
value: 0.24333
|
| 1689 |
+
- type: precision_at_1
|
| 1690 |
+
value: 0.26531
|
| 1691 |
+
- type: precision_at_10
|
| 1692 |
+
value: 0.22449
|
| 1693 |
+
- type: precision_at_100
|
| 1694 |
+
value: 0.07122
|
| 1695 |
+
- type: precision_at_1000
|
| 1696 |
+
value: 0.01527
|
| 1697 |
+
- type: precision_at_3
|
| 1698 |
+
value: 0.27891
|
| 1699 |
+
- type: precision_at_5
|
| 1700 |
+
value: 0.25714
|
| 1701 |
+
- type: recall_at_1
|
| 1702 |
+
value: 0.01976
|
| 1703 |
+
- type: recall_at_10
|
| 1704 |
+
value: 0.16633
|
| 1705 |
+
- type: recall_at_100
|
| 1706 |
+
value: 0.4561
|
| 1707 |
+
- type: recall_at_1000
|
| 1708 |
+
value: 0.82481
|
| 1709 |
+
- type: recall_at_3
|
| 1710 |
+
value: 0.06101
|
| 1711 |
+
- type: recall_at_5
|
| 1712 |
+
value: 0.0968
|
| 1713 |
+
- dataset:
|
| 1714 |
+
type: mteb/trec-covid
|
| 1715 |
+
name: MTEB TRECCOVID
|
| 1716 |
+
config: default
|
| 1717 |
+
split: test
|
| 1718 |
+
task:
|
| 1719 |
+
type: Retrieval
|
| 1720 |
+
metrics:
|
| 1721 |
+
- type: map_at_1
|
| 1722 |
+
value: 0.00211
|
| 1723 |
+
- type: map_at_10
|
| 1724 |
+
value: 0.01526
|
| 1725 |
+
- type: map_at_100
|
| 1726 |
+
value: 0.08863
|
| 1727 |
+
- type: map_at_1000
|
| 1728 |
+
value: 0.23162
|
| 1729 |
+
- type: map_at_3
|
| 1730 |
+
value: 0.00555
|
| 1731 |
+
- type: map_at_5
|
| 1732 |
+
value: 0.00873
|
| 1733 |
+
- type: mrr_at_1
|
| 1734 |
+
value: 0.76
|
| 1735 |
+
- type: mrr_at_10
|
| 1736 |
+
value: 0.8485
|
| 1737 |
+
- type: mrr_at_100
|
| 1738 |
+
value: 0.8485
|
| 1739 |
+
- type: mrr_at_1000
|
| 1740 |
+
value: 0.8485
|
| 1741 |
+
- type: mrr_at_3
|
| 1742 |
+
value: 0.84
|
| 1743 |
+
- type: mrr_at_5
|
| 1744 |
+
value: 0.844
|
| 1745 |
+
- type: ndcg_at_1
|
| 1746 |
+
value: 0.7
|
| 1747 |
+
- type: ndcg_at_10
|
| 1748 |
+
value: 0.63098
|
| 1749 |
+
- type: ndcg_at_100
|
| 1750 |
+
value: 0.49847
|
| 1751 |
+
- type: ndcg_at_1000
|
| 1752 |
+
value: 0.48395
|
| 1753 |
+
- type: ndcg_at_3
|
| 1754 |
+
value: 0.68704
|
| 1755 |
+
- type: ndcg_at_5
|
| 1756 |
+
value: 0.67533
|
| 1757 |
+
- type: precision_at_1
|
| 1758 |
+
value: 0.76
|
| 1759 |
+
- type: precision_at_10
|
| 1760 |
+
value: 0.66
|
| 1761 |
+
- type: precision_at_100
|
| 1762 |
+
value: 0.5134
|
| 1763 |
+
- type: precision_at_1000
|
| 1764 |
+
value: 0.2168
|
| 1765 |
+
- type: precision_at_3
|
| 1766 |
+
value: 0.72667
|
| 1767 |
+
- type: precision_at_5
|
| 1768 |
+
value: 0.716
|
| 1769 |
+
- type: recall_at_1
|
| 1770 |
+
value: 0.00211
|
| 1771 |
+
- type: recall_at_10
|
| 1772 |
+
value: 0.01748
|
| 1773 |
+
- type: recall_at_100
|
| 1774 |
+
value: 0.12448
|
| 1775 |
+
- type: recall_at_1000
|
| 1776 |
+
value: 0.46795
|
| 1777 |
+
- type: recall_at_3
|
| 1778 |
+
value: 0.00593
|
| 1779 |
+
- type: recall_at_5
|
| 1780 |
+
value: 0.00962
|
| 1781 |
+
pipeline_tag: sentence-similarity
|
| 1782 |
---
|
| 1783 |
+
# Granite-Embedding-30m-English
|
| 1784 |
+
|
| 1785 |
+
**Model Summary:**
|
| 1786 |
+
Granite-Embedding-30m-English is a 30M parameter model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. This model is developed using retrieval oriented pretraining, contrastive finetuning, knowledge distillation and model merging for improved performance.
|
| 1787 |
+
|
| 1788 |
+
- **Developers:** Granite Embedding Team, IBM
|
| 1789 |
+
- **GitHub Repository:**
|
| 1790 |
+
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
|
| 1791 |
+
- **Paper:**
|
| 1792 |
+
- **Release Date**: December 18th, 2024
|
| 1793 |
+
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
|
| 1794 |
+
|
| 1795 |
+
**Supported Languages:**
|
| 1796 |
+
English.
|
| 1797 |
+
|
| 1798 |
+
**Intended use:**
|
| 1799 |
+
The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications.
|
| 1800 |
+
|
| 1801 |
+
**Usage with Sentence Transformers:**
|
| 1802 |
+
The model is compatible with SentenceTransformer library and is very easy to use:
|
| 1803 |
+
|
| 1804 |
+
First, install the sentence transformers library
|
| 1805 |
+
```shell
|
| 1806 |
+
pip install sentence_transformers
|
| 1807 |
+
```
|
| 1808 |
+
|
| 1809 |
+
The model can then be used to encode pairs of text and find the similarity between their representations
|
| 1810 |
+
|
| 1811 |
+
```python
|
| 1812 |
+
from sentence_transformers import SentenceTransformer, util
|
| 1813 |
+
|
| 1814 |
+
model_path = "ibm-granite/granite-embedding-30m-english"
|
| 1815 |
+
# Load the Sentence Transformer model
|
| 1816 |
+
model = SentenceTransformer(model_path)
|
| 1817 |
+
|
| 1818 |
+
input_queries = [
|
| 1819 |
+
' Who made the song My achy breaky heart? ',
|
| 1820 |
+
'summit define'
|
| 1821 |
+
]
|
| 1822 |
+
|
| 1823 |
+
input_passages = [
|
| 1824 |
+
"Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
|
| 1825 |
+
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
|
| 1826 |
+
]
|
| 1827 |
+
|
| 1828 |
+
# encode queries and passages
|
| 1829 |
+
query_embeddings = model.encode(input_queries)
|
| 1830 |
+
passage_embeddings = model.encode(input_passages)
|
| 1831 |
+
|
| 1832 |
+
# calculate cosine similarity
|
| 1833 |
+
print(util.cos_sim(query_embeddings, passage_embeddings))
|
| 1834 |
+
```
|
| 1835 |
+
|
| 1836 |
+
**Usage with Huggingface Transformers:**
|
| 1837 |
+
This is a simple example of how to use the Granite-Embedding-30m-English model with the Transformers library and PyTorch.
|
| 1838 |
+
|
| 1839 |
+
First, install the required libraries
|
| 1840 |
+
```shell
|
| 1841 |
+
pip install transformers torch
|
| 1842 |
+
```
|
| 1843 |
+
|
| 1844 |
+
The model can then be used to encode pairs of text
|
| 1845 |
+
|
| 1846 |
+
```python
|
| 1847 |
+
import torch
|
| 1848 |
+
from transformers import AutoModel, AutoTokenizer
|
| 1849 |
+
|
| 1850 |
+
model_path = "ibm-granite/granite-embedding-30m-english"
|
| 1851 |
+
|
| 1852 |
+
# Load the model and tokenizer
|
| 1853 |
+
model = AutoModel.from_pretrained(model_path)
|
| 1854 |
+
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
| 1855 |
+
model.eval()
|
| 1856 |
+
|
| 1857 |
+
input_queries = [
|
| 1858 |
+
' Who made the song My achy breaky heart? ',
|
| 1859 |
+
'summit define'
|
| 1860 |
+
]
|
| 1861 |
+
|
| 1862 |
+
# tokenize inputs
|
| 1863 |
+
tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
|
| 1864 |
+
|
| 1865 |
+
# encode queries
|
| 1866 |
+
with torch.no_grad():
|
| 1867 |
+
# Queries
|
| 1868 |
+
model_output = model(**tokenized_queries)
|
| 1869 |
+
# Perform pooling. granite-embedding-30m-english uses CLS Pooling
|
| 1870 |
+
query_embeddings = model_output[0][:, 0]
|
| 1871 |
+
|
| 1872 |
+
# normalize the embeddings
|
| 1873 |
+
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
|
| 1874 |
+
|
| 1875 |
+
```
|
| 1876 |
+
**Evaluation:**
|
| 1877 |
+
|
| 1878 |
+
Granite-Embedding-30M-English is twice as fast as other models with similar embedding dimensions, while maintaining competitive performance. The performance of the Granite-Embedding-30M-English model on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below. MTEB Retrieval(14) indicates the average BEIR performance excluding MS-MARCO task as, unlike all other models, Granite-Embedding-30M-English did not train on MS-MARCO due to the dataset's non-commercial license. The average time required to encode and retrieve per query is also reported.
|
| 1879 |
+
|
| 1880 |
+
| Model | Paramters (M)| Embedding Dimension | MTEB Retrieval (15) | MTEB Retrieval (14) | CoIR (10) | Retrieval Time (seconds/query)|
|
| 1881 |
+
|---------------------------------|-------------:|--------------------:|--------------------:|---------------------:|----------:|------------------------------:|
|
| 1882 |
+
|granite-embedding-30m-english |30 |384 |49.1 |50.4 |47.0 | 0.16 |
|
| 1883 |
+
|
| 1884 |
+
|
| 1885 |
+
**Model Architecture:**
|
| 1886 |
+
Granite-Embedding-30m-English is based on an encoder-only RoBERTa like transformer architecture, trained internally at IBM Research.
|
| 1887 |
+
|
| 1888 |
+
| Model | granite-embedding-30m-english | granite-embedding-125m-english | granite-embedding-107m-multilingual | granite-embedding-278m-multilingual |
|
| 1889 |
+
| :--------- | :-------:| :--------: | :-----:| :-----:|
|
| 1890 |
+
| Embedding size | **384** | 768 | 384 | 768 |
|
| 1891 |
+
| Number of layers | **6** | 12 | 6 | 12 |
|
| 1892 |
+
| Number of attention heads | **12** | 12 | 12 | 12 |
|
| 1893 |
+
| Intermediate size | **1536** | 3072 | 1536 | 3072 |
|
| 1894 |
+
| Activation Function | **GeLU** | GeLU | GeLU | GeLU |
|
| 1895 |
+
| Vocabulary Size | **50265**| 50265 | 250002 | 250002 |
|
| 1896 |
+
| Max. Sequence Length | **512** | 512 | 512 | 512 |
|
| 1897 |
+
| # Parameters | **30M** | 125M | 107M | 278M |
|
| 1898 |
+
|
| 1899 |
+
|
| 1900 |
+
**Training Data:**
|
| 1901 |
+
Overall, the training data consists of four key sources: (1) unsupervised title-body paired data scraped from the web, (2) publicly available paired with permissive, enterprise-friendly license, (3) IBM-internal paired data targetting specific technical domains, and (4) IBM-generated synthetic data. The data is listed below:
|
| 1902 |
+
|
| 1903 |
+
| **Dataset** | **Num. Pairs** |
|
| 1904 |
+
|----------------------------------------------------|:---------------:|
|
| 1905 |
+
| SPECTER citation triplets | 684,100 |
|
| 1906 |
+
| Stack Exchange Duplicate questions (titles) | 304,525 |
|
| 1907 |
+
| Stack Exchange Duplicate questions (bodies) | 250,519 |
|
| 1908 |
+
| Stack Exchange Duplicate questions (titles+bodies) | 250,460 |
|
| 1909 |
+
| Natural Questions (NQ) | 100,231 |
|
| 1910 |
+
| SQuAD2.0 | 87,599 |
|
| 1911 |
+
| PAQ (Question, Answer) pairs | 64,371,441 |
|
| 1912 |
+
| Stack Exchange (Title, Answer) pairs | 4,067,139 |
|
| 1913 |
+
| Stack Exchange (Title, Body) pairs | 23,978,013 |
|
| 1914 |
+
| Stack Exchange (Title+Body, Answer) pairs | 187,195 |
|
| 1915 |
+
| S2ORC Citation pairs (Titles) | 52,603,982 |
|
| 1916 |
+
| S2ORC (Title, Abstract) | 41,769,185 |
|
| 1917 |
+
| S2ORC (Citations, abstracts) | 52,603,982 |
|
| 1918 |
+
| WikiAnswers Duplicate question pairs | 77,427,422 |
|
| 1919 |
+
| SearchQA | 582,261 |
|
| 1920 |
+
| HotpotQA | 85,000 |
|
| 1921 |
+
| Fever | 109,810 |
|
| 1922 |
+
| Arxiv | 2,358,545 |
|
| 1923 |
+
| Wikipedia | 20,745,403 |
|
| 1924 |
+
| PubMed | 20,000,000 |
|
| 1925 |
+
| Miracl En Pairs | 9,016 |
|
| 1926 |
+
| DBPedia Title-Body Pairs | 4,635,922 |
|
| 1927 |
+
| Synthetic: Query-Wikipedia Passage | 1,879,093 |
|
| 1928 |
+
| Synthetic: Fact Verification | 9,888 |
|
| 1929 |
+
| IBM Internal Triples | 40,290 |
|
| 1930 |
+
| IBM Internal Title-Body Pairs | 1,524,586 |
|
| 1931 |
+
|
| 1932 |
+
Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license, while other open-source models train on this dataset due to its high quality.
|
| 1933 |
+
|
| 1934 |
+
**Infrastructure:**
|
| 1935 |
+
We train Granite Embedding Models using IBM's computing cluster, Cognitive Compute Cluster, which is outfitted with NVIDIA A100 80gb GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.
|
| 1936 |
+
|
| 1937 |
+
**Ethical Considerations and Limitations:**
|
| 1938 |
+
The data used to train the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-Embedding-30m-English is trained only for English texts, and has a context length of 512 tokens (longer texts will be truncated to this size).
|
| 1939 |
+
|
| 1940 |
+
|
| 1941 |
+
<!-- ## Citation
|
| 1942 |
+
```
|
| 1943 |
+
@misc{granite-embedding-models,
|
| 1944 |
+
author = {author 1, author2, ...},
|
| 1945 |
+
title = {},
|
| 1946 |
+
journal = {},
|
| 1947 |
+
volume = {},
|
| 1948 |
+
year = {2024},
|
| 1949 |
+
url = {https://arxiv.org/abs/0000.00000},
|
| 1950 |
+
}
|
| 1951 |
+
``` -->
|