Commit
•
b00f33d
0
Parent(s):
Initial commit
Browse files- .gitattributes +35 -0
- 1_Pooling/config.json +10 -0
- README.md +1868 -0
- angle_config.json +14 -0
- config.json +26 -0
- config_sentence_transformers.json +10 -0
- model.safetensors +3 -0
- modules.json +14 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -0
- vocab.txt +0 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tar filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- mteb
|
| 4 |
+
base_model: mixedbread-ai/mxbai-embed-mini-v1
|
| 5 |
+
library_name: sentence-transformers
|
| 6 |
+
model-index:
|
| 7 |
+
- name: mxbai-embed-xsmall-v1
|
| 8 |
+
results:
|
| 9 |
+
- task:
|
| 10 |
+
type: Retrieval
|
| 11 |
+
dataset:
|
| 12 |
+
type: arguana
|
| 13 |
+
name: MTEB ArguAna
|
| 14 |
+
config: default
|
| 15 |
+
split: test
|
| 16 |
+
revision: None
|
| 17 |
+
metrics:
|
| 18 |
+
- type: ndcg_at_1
|
| 19 |
+
value: 25.18
|
| 20 |
+
- type: ndcg_at_3
|
| 21 |
+
value: 39.22
|
| 22 |
+
- type: ndcg_at_5
|
| 23 |
+
value: 43.93
|
| 24 |
+
- type: ndcg_at_10
|
| 25 |
+
value: 49.58
|
| 26 |
+
- type: ndcg_at_30
|
| 27 |
+
value: 53.41
|
| 28 |
+
- type: ndcg_at_100
|
| 29 |
+
value: 54.11
|
| 30 |
+
- type: map_at_1
|
| 31 |
+
value: 25.18
|
| 32 |
+
- type: map_at_3
|
| 33 |
+
value: 35.66
|
| 34 |
+
- type: map_at_5
|
| 35 |
+
value: 38.25
|
| 36 |
+
- type: map_at_10
|
| 37 |
+
value: 40.58
|
| 38 |
+
- type: map_at_30
|
| 39 |
+
value: 41.6
|
| 40 |
+
- type: map_at_100
|
| 41 |
+
value: 41.69
|
| 42 |
+
- type: recall_at_1
|
| 43 |
+
value: 25.18
|
| 44 |
+
- type: recall_at_3
|
| 45 |
+
value: 49.57
|
| 46 |
+
- type: recall_at_5
|
| 47 |
+
value: 61.09
|
| 48 |
+
- type: recall_at_10
|
| 49 |
+
value: 78.59
|
| 50 |
+
- type: recall_at_30
|
| 51 |
+
value: 94.03
|
| 52 |
+
- type: recall_at_100
|
| 53 |
+
value: 97.94
|
| 54 |
+
- type: precision_at_1
|
| 55 |
+
value: 25.18
|
| 56 |
+
- type: precision_at_3
|
| 57 |
+
value: 16.52
|
| 58 |
+
- type: precision_at_5
|
| 59 |
+
value: 12.22
|
| 60 |
+
- type: precision_at_10
|
| 61 |
+
value: 7.86
|
| 62 |
+
- type: precision_at_30
|
| 63 |
+
value: 3.13
|
| 64 |
+
- type: precision_at_100
|
| 65 |
+
value: 0.98
|
| 66 |
+
- type: accuracy_at_3
|
| 67 |
+
value: 49.57
|
| 68 |
+
- type: accuracy_at_5
|
| 69 |
+
value: 61.09
|
| 70 |
+
- type: accuracy_at_10
|
| 71 |
+
value: 78.59
|
| 72 |
+
- task:
|
| 73 |
+
type: Retrieval
|
| 74 |
+
dataset:
|
| 75 |
+
type: BeIR/cqadupstack
|
| 76 |
+
name: MTEB CQADupstackAndroidRetrieval
|
| 77 |
+
config: default
|
| 78 |
+
split: test
|
| 79 |
+
revision: None
|
| 80 |
+
metrics:
|
| 81 |
+
- type: ndcg_at_1
|
| 82 |
+
value: 44.35
|
| 83 |
+
- type: ndcg_at_3
|
| 84 |
+
value: 49.64
|
| 85 |
+
- type: ndcg_at_5
|
| 86 |
+
value: 51.73
|
| 87 |
+
- type: ndcg_at_10
|
| 88 |
+
value: 54.82
|
| 89 |
+
- type: ndcg_at_30
|
| 90 |
+
value: 57.64
|
| 91 |
+
- type: ndcg_at_100
|
| 92 |
+
value: 59.77
|
| 93 |
+
- type: map_at_1
|
| 94 |
+
value: 36.26
|
| 95 |
+
- type: map_at_3
|
| 96 |
+
value: 44.35
|
| 97 |
+
- type: map_at_5
|
| 98 |
+
value: 46.26
|
| 99 |
+
- type: map_at_10
|
| 100 |
+
value: 48.24
|
| 101 |
+
- type: map_at_30
|
| 102 |
+
value: 49.34
|
| 103 |
+
- type: map_at_100
|
| 104 |
+
value: 49.75
|
| 105 |
+
- type: recall_at_1
|
| 106 |
+
value: 36.26
|
| 107 |
+
- type: recall_at_3
|
| 108 |
+
value: 51.46
|
| 109 |
+
- type: recall_at_5
|
| 110 |
+
value: 57.78
|
| 111 |
+
- type: recall_at_10
|
| 112 |
+
value: 66.5
|
| 113 |
+
- type: recall_at_30
|
| 114 |
+
value: 77.19
|
| 115 |
+
- type: recall_at_100
|
| 116 |
+
value: 87.53
|
| 117 |
+
- type: precision_at_1
|
| 118 |
+
value: 44.35
|
| 119 |
+
- type: precision_at_3
|
| 120 |
+
value: 23.65
|
| 121 |
+
- type: precision_at_5
|
| 122 |
+
value: 16.88
|
| 123 |
+
- type: precision_at_10
|
| 124 |
+
value: 10.7
|
| 125 |
+
- type: precision_at_30
|
| 126 |
+
value: 4.53
|
| 127 |
+
- type: precision_at_100
|
| 128 |
+
value: 1.65
|
| 129 |
+
- type: accuracy_at_3
|
| 130 |
+
value: 60.51
|
| 131 |
+
- type: accuracy_at_5
|
| 132 |
+
value: 67.67
|
| 133 |
+
- type: accuracy_at_10
|
| 134 |
+
value: 74.68
|
| 135 |
+
- task:
|
| 136 |
+
type: Retrieval
|
| 137 |
+
dataset:
|
| 138 |
+
type: BeIR/cqadupstack
|
| 139 |
+
name: MTEB CQADupstackEnglishRetrieval
|
| 140 |
+
config: default
|
| 141 |
+
split: test
|
| 142 |
+
revision: None
|
| 143 |
+
metrics:
|
| 144 |
+
- type: ndcg_at_1
|
| 145 |
+
value: 39.43
|
| 146 |
+
- type: ndcg_at_3
|
| 147 |
+
value: 44.13
|
| 148 |
+
- type: ndcg_at_5
|
| 149 |
+
value: 46.06
|
| 150 |
+
- type: ndcg_at_10
|
| 151 |
+
value: 48.31
|
| 152 |
+
- type: ndcg_at_30
|
| 153 |
+
value: 51.06
|
| 154 |
+
- type: ndcg_at_100
|
| 155 |
+
value: 53.07
|
| 156 |
+
- type: map_at_1
|
| 157 |
+
value: 31.27
|
| 158 |
+
- type: map_at_3
|
| 159 |
+
value: 39.07
|
| 160 |
+
- type: map_at_5
|
| 161 |
+
value: 40.83
|
| 162 |
+
- type: map_at_10
|
| 163 |
+
value: 42.23
|
| 164 |
+
- type: map_at_30
|
| 165 |
+
value: 43.27
|
| 166 |
+
- type: map_at_100
|
| 167 |
+
value: 43.66
|
| 168 |
+
- type: recall_at_1
|
| 169 |
+
value: 31.27
|
| 170 |
+
- type: recall_at_3
|
| 171 |
+
value: 45.89
|
| 172 |
+
- type: recall_at_5
|
| 173 |
+
value: 51.44
|
| 174 |
+
- type: recall_at_10
|
| 175 |
+
value: 58.65
|
| 176 |
+
- type: recall_at_30
|
| 177 |
+
value: 69.12
|
| 178 |
+
- type: recall_at_100
|
| 179 |
+
value: 78.72
|
| 180 |
+
- type: precision_at_1
|
| 181 |
+
value: 39.43
|
| 182 |
+
- type: precision_at_3
|
| 183 |
+
value: 21.61
|
| 184 |
+
- type: precision_at_5
|
| 185 |
+
value: 15.34
|
| 186 |
+
- type: precision_at_10
|
| 187 |
+
value: 9.27
|
| 188 |
+
- type: precision_at_30
|
| 189 |
+
value: 4.01
|
| 190 |
+
- type: precision_at_100
|
| 191 |
+
value: 1.52
|
| 192 |
+
- type: accuracy_at_3
|
| 193 |
+
value: 55.48
|
| 194 |
+
- type: accuracy_at_5
|
| 195 |
+
value: 60.76
|
| 196 |
+
- type: accuracy_at_10
|
| 197 |
+
value: 67.45
|
| 198 |
+
- task:
|
| 199 |
+
type: Retrieval
|
| 200 |
+
dataset:
|
| 201 |
+
type: BeIR/cqadupstack
|
| 202 |
+
name: MTEB CQADupstackGamingRetrieval
|
| 203 |
+
config: default
|
| 204 |
+
split: test
|
| 205 |
+
revision: None
|
| 206 |
+
metrics:
|
| 207 |
+
- type: ndcg_at_1
|
| 208 |
+
value: 45.58
|
| 209 |
+
- type: ndcg_at_3
|
| 210 |
+
value: 52.68
|
| 211 |
+
- type: ndcg_at_5
|
| 212 |
+
value: 55.28
|
| 213 |
+
- type: ndcg_at_10
|
| 214 |
+
value: 57.88
|
| 215 |
+
- type: ndcg_at_30
|
| 216 |
+
value: 60.6
|
| 217 |
+
- type: ndcg_at_100
|
| 218 |
+
value: 62.03
|
| 219 |
+
- type: map_at_1
|
| 220 |
+
value: 39.97
|
| 221 |
+
- type: map_at_3
|
| 222 |
+
value: 49.06
|
| 223 |
+
- type: map_at_5
|
| 224 |
+
value: 50.87
|
| 225 |
+
- type: map_at_10
|
| 226 |
+
value: 52.2
|
| 227 |
+
- type: map_at_30
|
| 228 |
+
value: 53.06
|
| 229 |
+
- type: map_at_100
|
| 230 |
+
value: 53.28
|
| 231 |
+
- type: recall_at_1
|
| 232 |
+
value: 39.97
|
| 233 |
+
- type: recall_at_3
|
| 234 |
+
value: 57.4
|
| 235 |
+
- type: recall_at_5
|
| 236 |
+
value: 63.83
|
| 237 |
+
- type: recall_at_10
|
| 238 |
+
value: 71.33
|
| 239 |
+
- type: recall_at_30
|
| 240 |
+
value: 81.81
|
| 241 |
+
- type: recall_at_100
|
| 242 |
+
value: 89.0
|
| 243 |
+
- type: precision_at_1
|
| 244 |
+
value: 45.58
|
| 245 |
+
- type: precision_at_3
|
| 246 |
+
value: 23.55
|
| 247 |
+
- type: precision_at_5
|
| 248 |
+
value: 16.01
|
| 249 |
+
- type: precision_at_10
|
| 250 |
+
value: 9.25
|
| 251 |
+
- type: precision_at_30
|
| 252 |
+
value: 3.67
|
| 253 |
+
- type: precision_at_100
|
| 254 |
+
value: 1.23
|
| 255 |
+
- type: accuracy_at_3
|
| 256 |
+
value: 62.76
|
| 257 |
+
- type: accuracy_at_5
|
| 258 |
+
value: 68.84
|
| 259 |
+
- type: accuracy_at_10
|
| 260 |
+
value: 75.8
|
| 261 |
+
- task:
|
| 262 |
+
type: Retrieval
|
| 263 |
+
dataset:
|
| 264 |
+
type: BeIR/cqadupstack
|
| 265 |
+
name: MTEB CQADupstackGisRetrieval
|
| 266 |
+
config: default
|
| 267 |
+
split: test
|
| 268 |
+
revision: None
|
| 269 |
+
metrics:
|
| 270 |
+
- type: ndcg_at_1
|
| 271 |
+
value: 27.35
|
| 272 |
+
- type: ndcg_at_3
|
| 273 |
+
value: 34.23
|
| 274 |
+
- type: ndcg_at_5
|
| 275 |
+
value: 37.1
|
| 276 |
+
- type: ndcg_at_10
|
| 277 |
+
value: 40.26
|
| 278 |
+
- type: ndcg_at_30
|
| 279 |
+
value: 43.54
|
| 280 |
+
- type: ndcg_at_100
|
| 281 |
+
value: 45.9
|
| 282 |
+
- type: map_at_1
|
| 283 |
+
value: 25.28
|
| 284 |
+
- type: map_at_3
|
| 285 |
+
value: 31.68
|
| 286 |
+
- type: map_at_5
|
| 287 |
+
value: 33.38
|
| 288 |
+
- type: map_at_10
|
| 289 |
+
value: 34.79
|
| 290 |
+
- type: map_at_30
|
| 291 |
+
value: 35.67
|
| 292 |
+
- type: map_at_100
|
| 293 |
+
value: 35.96
|
| 294 |
+
- type: recall_at_1
|
| 295 |
+
value: 25.28
|
| 296 |
+
- type: recall_at_3
|
| 297 |
+
value: 38.95
|
| 298 |
+
- type: recall_at_5
|
| 299 |
+
value: 45.82
|
| 300 |
+
- type: recall_at_10
|
| 301 |
+
value: 55.11
|
| 302 |
+
- type: recall_at_30
|
| 303 |
+
value: 68.13
|
| 304 |
+
- type: recall_at_100
|
| 305 |
+
value: 80.88
|
| 306 |
+
- type: precision_at_1
|
| 307 |
+
value: 27.35
|
| 308 |
+
- type: precision_at_3
|
| 309 |
+
value: 14.65
|
| 310 |
+
- type: precision_at_5
|
| 311 |
+
value: 10.44
|
| 312 |
+
- type: precision_at_10
|
| 313 |
+
value: 6.37
|
| 314 |
+
- type: precision_at_30
|
| 315 |
+
value: 2.65
|
| 316 |
+
- type: precision_at_100
|
| 317 |
+
value: 0.97
|
| 318 |
+
- type: accuracy_at_3
|
| 319 |
+
value: 42.15
|
| 320 |
+
- type: accuracy_at_5
|
| 321 |
+
value: 49.15
|
| 322 |
+
- type: accuracy_at_10
|
| 323 |
+
value: 58.53
|
| 324 |
+
- task:
|
| 325 |
+
type: Retrieval
|
| 326 |
+
dataset:
|
| 327 |
+
type: BeIR/cqadupstack
|
| 328 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
| 329 |
+
config: default
|
| 330 |
+
split: test
|
| 331 |
+
revision: None
|
| 332 |
+
metrics:
|
| 333 |
+
- type: ndcg_at_1
|
| 334 |
+
value: 18.91
|
| 335 |
+
- type: ndcg_at_3
|
| 336 |
+
value: 24.37
|
| 337 |
+
- type: ndcg_at_5
|
| 338 |
+
value: 26.11
|
| 339 |
+
- type: ndcg_at_10
|
| 340 |
+
value: 29.37
|
| 341 |
+
- type: ndcg_at_30
|
| 342 |
+
value: 33.22
|
| 343 |
+
- type: ndcg_at_100
|
| 344 |
+
value: 35.73
|
| 345 |
+
- type: map_at_1
|
| 346 |
+
value: 15.23
|
| 347 |
+
- type: map_at_3
|
| 348 |
+
value: 21.25
|
| 349 |
+
- type: map_at_5
|
| 350 |
+
value: 22.38
|
| 351 |
+
- type: map_at_10
|
| 352 |
+
value: 23.86
|
| 353 |
+
- type: map_at_30
|
| 354 |
+
value: 24.91
|
| 355 |
+
- type: map_at_100
|
| 356 |
+
value: 25.24
|
| 357 |
+
- type: recall_at_1
|
| 358 |
+
value: 15.23
|
| 359 |
+
- type: recall_at_3
|
| 360 |
+
value: 28.28
|
| 361 |
+
- type: recall_at_5
|
| 362 |
+
value: 32.67
|
| 363 |
+
- type: recall_at_10
|
| 364 |
+
value: 42.23
|
| 365 |
+
- type: recall_at_30
|
| 366 |
+
value: 56.87
|
| 367 |
+
- type: recall_at_100
|
| 368 |
+
value: 69.44
|
| 369 |
+
- type: precision_at_1
|
| 370 |
+
value: 18.91
|
| 371 |
+
- type: precision_at_3
|
| 372 |
+
value: 11.9
|
| 373 |
+
- type: precision_at_5
|
| 374 |
+
value: 8.48
|
| 375 |
+
- type: precision_at_10
|
| 376 |
+
value: 5.63
|
| 377 |
+
- type: precision_at_30
|
| 378 |
+
value: 2.64
|
| 379 |
+
- type: precision_at_100
|
| 380 |
+
value: 1.02
|
| 381 |
+
- type: accuracy_at_3
|
| 382 |
+
value: 33.95
|
| 383 |
+
- type: accuracy_at_5
|
| 384 |
+
value: 38.81
|
| 385 |
+
- type: accuracy_at_10
|
| 386 |
+
value: 49.13
|
| 387 |
+
- task:
|
| 388 |
+
type: Retrieval
|
| 389 |
+
dataset:
|
| 390 |
+
type: BeIR/cqadupstack
|
| 391 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
| 392 |
+
config: default
|
| 393 |
+
split: test
|
| 394 |
+
revision: None
|
| 395 |
+
metrics:
|
| 396 |
+
- type: ndcg_at_1
|
| 397 |
+
value: 36.96
|
| 398 |
+
- type: ndcg_at_3
|
| 399 |
+
value: 42.48
|
| 400 |
+
- type: ndcg_at_5
|
| 401 |
+
value: 44.57
|
| 402 |
+
- type: ndcg_at_10
|
| 403 |
+
value: 47.13
|
| 404 |
+
- type: ndcg_at_30
|
| 405 |
+
value: 50.65
|
| 406 |
+
- type: ndcg_at_100
|
| 407 |
+
value: 53.14
|
| 408 |
+
- type: map_at_1
|
| 409 |
+
value: 30.1
|
| 410 |
+
- type: map_at_3
|
| 411 |
+
value: 37.97
|
| 412 |
+
- type: map_at_5
|
| 413 |
+
value: 39.62
|
| 414 |
+
- type: map_at_10
|
| 415 |
+
value: 41.06
|
| 416 |
+
- type: map_at_30
|
| 417 |
+
value: 42.13
|
| 418 |
+
- type: map_at_100
|
| 419 |
+
value: 42.53
|
| 420 |
+
- type: recall_at_1
|
| 421 |
+
value: 30.1
|
| 422 |
+
- type: recall_at_3
|
| 423 |
+
value: 45.98
|
| 424 |
+
- type: recall_at_5
|
| 425 |
+
value: 51.58
|
| 426 |
+
- type: recall_at_10
|
| 427 |
+
value: 59.24
|
| 428 |
+
- type: recall_at_30
|
| 429 |
+
value: 72.47
|
| 430 |
+
- type: recall_at_100
|
| 431 |
+
value: 84.53
|
| 432 |
+
- type: precision_at_1
|
| 433 |
+
value: 36.96
|
| 434 |
+
- type: precision_at_3
|
| 435 |
+
value: 20.5
|
| 436 |
+
- type: precision_at_5
|
| 437 |
+
value: 14.4
|
| 438 |
+
- type: precision_at_10
|
| 439 |
+
value: 8.62
|
| 440 |
+
- type: precision_at_30
|
| 441 |
+
value: 3.67
|
| 442 |
+
- type: precision_at_100
|
| 443 |
+
value: 1.38
|
| 444 |
+
- type: accuracy_at_3
|
| 445 |
+
value: 54.09
|
| 446 |
+
- type: accuracy_at_5
|
| 447 |
+
value: 60.25
|
| 448 |
+
- type: accuracy_at_10
|
| 449 |
+
value: 67.37
|
| 450 |
+
- task:
|
| 451 |
+
type: Retrieval
|
| 452 |
+
dataset:
|
| 453 |
+
type: BeIR/cqadupstack
|
| 454 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
| 455 |
+
config: default
|
| 456 |
+
split: test
|
| 457 |
+
revision: None
|
| 458 |
+
metrics:
|
| 459 |
+
- type: ndcg_at_1
|
| 460 |
+
value: 28.65
|
| 461 |
+
- type: ndcg_at_3
|
| 462 |
+
value: 34.3
|
| 463 |
+
- type: ndcg_at_5
|
| 464 |
+
value: 36.8
|
| 465 |
+
- type: ndcg_at_10
|
| 466 |
+
value: 39.92
|
| 467 |
+
- type: ndcg_at_30
|
| 468 |
+
value: 42.97
|
| 469 |
+
- type: ndcg_at_100
|
| 470 |
+
value: 45.45
|
| 471 |
+
- type: map_at_1
|
| 472 |
+
value: 23.35
|
| 473 |
+
- type: map_at_3
|
| 474 |
+
value: 30.36
|
| 475 |
+
- type: map_at_5
|
| 476 |
+
value: 32.15
|
| 477 |
+
- type: map_at_10
|
| 478 |
+
value: 33.74
|
| 479 |
+
- type: map_at_30
|
| 480 |
+
value: 34.69
|
| 481 |
+
- type: map_at_100
|
| 482 |
+
value: 35.02
|
| 483 |
+
- type: recall_at_1
|
| 484 |
+
value: 23.35
|
| 485 |
+
- type: recall_at_3
|
| 486 |
+
value: 37.71
|
| 487 |
+
- type: recall_at_5
|
| 488 |
+
value: 44.23
|
| 489 |
+
- type: recall_at_10
|
| 490 |
+
value: 53.6
|
| 491 |
+
- type: recall_at_30
|
| 492 |
+
value: 64.69
|
| 493 |
+
- type: recall_at_100
|
| 494 |
+
value: 77.41
|
| 495 |
+
- type: precision_at_1
|
| 496 |
+
value: 28.65
|
| 497 |
+
- type: precision_at_3
|
| 498 |
+
value: 16.74
|
| 499 |
+
- type: precision_at_5
|
| 500 |
+
value: 12.21
|
| 501 |
+
- type: precision_at_10
|
| 502 |
+
value: 7.61
|
| 503 |
+
- type: precision_at_30
|
| 504 |
+
value: 3.29
|
| 505 |
+
- type: precision_at_100
|
| 506 |
+
value: 1.22
|
| 507 |
+
- type: accuracy_at_3
|
| 508 |
+
value: 44.86
|
| 509 |
+
- type: accuracy_at_5
|
| 510 |
+
value: 52.4
|
| 511 |
+
- type: accuracy_at_10
|
| 512 |
+
value: 61.07
|
| 513 |
+
- task:
|
| 514 |
+
type: Retrieval
|
| 515 |
+
dataset:
|
| 516 |
+
type: BeIR/cqadupstack
|
| 517 |
+
name: MTEB CQADupstackStatsRetrieval
|
| 518 |
+
config: default
|
| 519 |
+
split: test
|
| 520 |
+
revision: None
|
| 521 |
+
metrics:
|
| 522 |
+
- type: ndcg_at_1
|
| 523 |
+
value: 26.07
|
| 524 |
+
- type: ndcg_at_3
|
| 525 |
+
value: 31.62
|
| 526 |
+
- type: ndcg_at_5
|
| 527 |
+
value: 33.23
|
| 528 |
+
- type: ndcg_at_10
|
| 529 |
+
value: 35.62
|
| 530 |
+
- type: ndcg_at_30
|
| 531 |
+
value: 38.41
|
| 532 |
+
- type: ndcg_at_100
|
| 533 |
+
value: 40.81
|
| 534 |
+
- type: map_at_1
|
| 535 |
+
value: 22.96
|
| 536 |
+
- type: map_at_3
|
| 537 |
+
value: 28.85
|
| 538 |
+
- type: map_at_5
|
| 539 |
+
value: 29.97
|
| 540 |
+
- type: map_at_10
|
| 541 |
+
value: 31.11
|
| 542 |
+
- type: map_at_30
|
| 543 |
+
value: 31.86
|
| 544 |
+
- type: map_at_100
|
| 545 |
+
value: 32.15
|
| 546 |
+
- type: recall_at_1
|
| 547 |
+
value: 22.96
|
| 548 |
+
- type: recall_at_3
|
| 549 |
+
value: 35.14
|
| 550 |
+
- type: recall_at_5
|
| 551 |
+
value: 39.22
|
| 552 |
+
- type: recall_at_10
|
| 553 |
+
value: 46.52
|
| 554 |
+
- type: recall_at_30
|
| 555 |
+
value: 57.58
|
| 556 |
+
- type: recall_at_100
|
| 557 |
+
value: 70.57
|
| 558 |
+
- type: precision_at_1
|
| 559 |
+
value: 26.07
|
| 560 |
+
- type: precision_at_3
|
| 561 |
+
value: 14.11
|
| 562 |
+
- type: precision_at_5
|
| 563 |
+
value: 9.69
|
| 564 |
+
- type: precision_at_10
|
| 565 |
+
value: 5.81
|
| 566 |
+
- type: precision_at_30
|
| 567 |
+
value: 2.45
|
| 568 |
+
- type: precision_at_100
|
| 569 |
+
value: 0.92
|
| 570 |
+
- type: accuracy_at_3
|
| 571 |
+
value: 39.42
|
| 572 |
+
- type: accuracy_at_5
|
| 573 |
+
value: 43.41
|
| 574 |
+
- type: accuracy_at_10
|
| 575 |
+
value: 50.92
|
| 576 |
+
- task:
|
| 577 |
+
type: Retrieval
|
| 578 |
+
dataset:
|
| 579 |
+
type: BeIR/cqadupstack
|
| 580 |
+
name: MTEB CQADupstackTexRetrieval
|
| 581 |
+
config: default
|
| 582 |
+
split: test
|
| 583 |
+
revision: None
|
| 584 |
+
metrics:
|
| 585 |
+
- type: ndcg_at_1
|
| 586 |
+
value: 21.78
|
| 587 |
+
- type: ndcg_at_3
|
| 588 |
+
value: 25.74
|
| 589 |
+
- type: ndcg_at_5
|
| 590 |
+
value: 27.86
|
| 591 |
+
- type: ndcg_at_10
|
| 592 |
+
value: 30.3
|
| 593 |
+
- type: ndcg_at_30
|
| 594 |
+
value: 33.51
|
| 595 |
+
- type: ndcg_at_100
|
| 596 |
+
value: 36.12
|
| 597 |
+
- type: map_at_1
|
| 598 |
+
value: 17.63
|
| 599 |
+
- type: map_at_3
|
| 600 |
+
value: 22.7
|
| 601 |
+
- type: map_at_5
|
| 602 |
+
value: 24.14
|
| 603 |
+
- type: map_at_10
|
| 604 |
+
value: 25.31
|
| 605 |
+
- type: map_at_30
|
| 606 |
+
value: 26.22
|
| 607 |
+
- type: map_at_100
|
| 608 |
+
value: 26.56
|
| 609 |
+
- type: recall_at_1
|
| 610 |
+
value: 17.63
|
| 611 |
+
- type: recall_at_3
|
| 612 |
+
value: 28.37
|
| 613 |
+
- type: recall_at_5
|
| 614 |
+
value: 33.99
|
| 615 |
+
- type: recall_at_10
|
| 616 |
+
value: 41.23
|
| 617 |
+
- type: recall_at_30
|
| 618 |
+
value: 53.69
|
| 619 |
+
- type: recall_at_100
|
| 620 |
+
value: 67.27
|
| 621 |
+
- type: precision_at_1
|
| 622 |
+
value: 21.78
|
| 623 |
+
- type: precision_at_3
|
| 624 |
+
value: 12.41
|
| 625 |
+
- type: precision_at_5
|
| 626 |
+
value: 9.07
|
| 627 |
+
- type: precision_at_10
|
| 628 |
+
value: 5.69
|
| 629 |
+
- type: precision_at_30
|
| 630 |
+
value: 2.61
|
| 631 |
+
- type: precision_at_100
|
| 632 |
+
value: 1.03
|
| 633 |
+
- type: accuracy_at_3
|
| 634 |
+
value: 33.62
|
| 635 |
+
- type: accuracy_at_5
|
| 636 |
+
value: 39.81
|
| 637 |
+
- type: accuracy_at_10
|
| 638 |
+
value: 47.32
|
| 639 |
+
- task:
|
| 640 |
+
type: Retrieval
|
| 641 |
+
dataset:
|
| 642 |
+
type: BeIR/cqadupstack
|
| 643 |
+
name: MTEB CQADupstackUnixRetrieval
|
| 644 |
+
config: default
|
| 645 |
+
split: test
|
| 646 |
+
revision: None
|
| 647 |
+
metrics:
|
| 648 |
+
- type: ndcg_at_1
|
| 649 |
+
value: 30.97
|
| 650 |
+
- type: ndcg_at_3
|
| 651 |
+
value: 36.13
|
| 652 |
+
- type: ndcg_at_5
|
| 653 |
+
value: 39.0
|
| 654 |
+
- type: ndcg_at_10
|
| 655 |
+
value: 41.78
|
| 656 |
+
- type: ndcg_at_30
|
| 657 |
+
value: 44.96
|
| 658 |
+
- type: ndcg_at_100
|
| 659 |
+
value: 47.52
|
| 660 |
+
- type: map_at_1
|
| 661 |
+
value: 26.05
|
| 662 |
+
- type: map_at_3
|
| 663 |
+
value: 32.77
|
| 664 |
+
- type: map_at_5
|
| 665 |
+
value: 34.6
|
| 666 |
+
- type: map_at_10
|
| 667 |
+
value: 35.93
|
| 668 |
+
- type: map_at_30
|
| 669 |
+
value: 36.88
|
| 670 |
+
- type: map_at_100
|
| 671 |
+
value: 37.22
|
| 672 |
+
- type: recall_at_1
|
| 673 |
+
value: 26.05
|
| 674 |
+
- type: recall_at_3
|
| 675 |
+
value: 40.0
|
| 676 |
+
- type: recall_at_5
|
| 677 |
+
value: 47.34
|
| 678 |
+
- type: recall_at_10
|
| 679 |
+
value: 55.34
|
| 680 |
+
- type: recall_at_30
|
| 681 |
+
value: 67.08
|
| 682 |
+
- type: recall_at_100
|
| 683 |
+
value: 80.2
|
| 684 |
+
- type: precision_at_1
|
| 685 |
+
value: 30.97
|
| 686 |
+
- type: precision_at_3
|
| 687 |
+
value: 16.6
|
| 688 |
+
- type: precision_at_5
|
| 689 |
+
value: 12.03
|
| 690 |
+
- type: precision_at_10
|
| 691 |
+
value: 7.3
|
| 692 |
+
- type: precision_at_30
|
| 693 |
+
value: 3.08
|
| 694 |
+
- type: precision_at_100
|
| 695 |
+
value: 1.15
|
| 696 |
+
- type: accuracy_at_3
|
| 697 |
+
value: 45.62
|
| 698 |
+
- type: accuracy_at_5
|
| 699 |
+
value: 53.64
|
| 700 |
+
- type: accuracy_at_10
|
| 701 |
+
value: 61.66
|
| 702 |
+
- task:
|
| 703 |
+
type: Retrieval
|
| 704 |
+
dataset:
|
| 705 |
+
type: BeIR/cqadupstack
|
| 706 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
| 707 |
+
config: default
|
| 708 |
+
split: test
|
| 709 |
+
revision: None
|
| 710 |
+
metrics:
|
| 711 |
+
- type: ndcg_at_1
|
| 712 |
+
value: 29.64
|
| 713 |
+
- type: ndcg_at_3
|
| 714 |
+
value: 35.49
|
| 715 |
+
- type: ndcg_at_5
|
| 716 |
+
value: 37.77
|
| 717 |
+
- type: ndcg_at_10
|
| 718 |
+
value: 40.78
|
| 719 |
+
- type: ndcg_at_30
|
| 720 |
+
value: 44.59
|
| 721 |
+
- type: ndcg_at_100
|
| 722 |
+
value: 46.97
|
| 723 |
+
- type: map_at_1
|
| 724 |
+
value: 24.77
|
| 725 |
+
- type: map_at_3
|
| 726 |
+
value: 31.33
|
| 727 |
+
- type: map_at_5
|
| 728 |
+
value: 32.95
|
| 729 |
+
- type: map_at_10
|
| 730 |
+
value: 34.47
|
| 731 |
+
- type: map_at_30
|
| 732 |
+
value: 35.7
|
| 733 |
+
- type: map_at_100
|
| 734 |
+
value: 36.17
|
| 735 |
+
- type: recall_at_1
|
| 736 |
+
value: 24.77
|
| 737 |
+
- type: recall_at_3
|
| 738 |
+
value: 38.16
|
| 739 |
+
- type: recall_at_5
|
| 740 |
+
value: 44.1
|
| 741 |
+
- type: recall_at_10
|
| 742 |
+
value: 53.31
|
| 743 |
+
- type: recall_at_30
|
| 744 |
+
value: 68.43
|
| 745 |
+
- type: recall_at_100
|
| 746 |
+
value: 80.24
|
| 747 |
+
- type: precision_at_1
|
| 748 |
+
value: 29.64
|
| 749 |
+
- type: precision_at_3
|
| 750 |
+
value: 16.8
|
| 751 |
+
- type: precision_at_5
|
| 752 |
+
value: 12.21
|
| 753 |
+
- type: precision_at_10
|
| 754 |
+
value: 7.83
|
| 755 |
+
- type: precision_at_30
|
| 756 |
+
value: 3.89
|
| 757 |
+
- type: precision_at_100
|
| 758 |
+
value: 1.63
|
| 759 |
+
- type: accuracy_at_3
|
| 760 |
+
value: 45.45
|
| 761 |
+
- type: accuracy_at_5
|
| 762 |
+
value: 51.58
|
| 763 |
+
- type: accuracy_at_10
|
| 764 |
+
value: 61.07
|
| 765 |
+
- task:
|
| 766 |
+
type: Retrieval
|
| 767 |
+
dataset:
|
| 768 |
+
type: BeIR/cqadupstack
|
| 769 |
+
name: MTEB CQADupstackWordpressRetrieval
|
| 770 |
+
config: default
|
| 771 |
+
split: test
|
| 772 |
+
revision: None
|
| 773 |
+
metrics:
|
| 774 |
+
- type: ndcg_at_1
|
| 775 |
+
value: 23.47
|
| 776 |
+
- type: ndcg_at_3
|
| 777 |
+
value: 27.98
|
| 778 |
+
- type: ndcg_at_5
|
| 779 |
+
value: 30.16
|
| 780 |
+
- type: ndcg_at_10
|
| 781 |
+
value: 32.97
|
| 782 |
+
- type: ndcg_at_30
|
| 783 |
+
value: 36.3
|
| 784 |
+
- type: ndcg_at_100
|
| 785 |
+
value: 38.47
|
| 786 |
+
- type: map_at_1
|
| 787 |
+
value: 21.63
|
| 788 |
+
- type: map_at_3
|
| 789 |
+
value: 26.02
|
| 790 |
+
- type: map_at_5
|
| 791 |
+
value: 27.32
|
| 792 |
+
- type: map_at_10
|
| 793 |
+
value: 28.51
|
| 794 |
+
- type: map_at_30
|
| 795 |
+
value: 29.39
|
| 796 |
+
- type: map_at_100
|
| 797 |
+
value: 29.66
|
| 798 |
+
- type: recall_at_1
|
| 799 |
+
value: 21.63
|
| 800 |
+
- type: recall_at_3
|
| 801 |
+
value: 31.47
|
| 802 |
+
- type: recall_at_5
|
| 803 |
+
value: 36.69
|
| 804 |
+
- type: recall_at_10
|
| 805 |
+
value: 44.95
|
| 806 |
+
- type: recall_at_30
|
| 807 |
+
value: 58.2
|
| 808 |
+
- type: recall_at_100
|
| 809 |
+
value: 69.83
|
| 810 |
+
- type: precision_at_1
|
| 811 |
+
value: 23.47
|
| 812 |
+
- type: precision_at_3
|
| 813 |
+
value: 11.71
|
| 814 |
+
- type: precision_at_5
|
| 815 |
+
value: 8.32
|
| 816 |
+
- type: precision_at_10
|
| 817 |
+
value: 5.23
|
| 818 |
+
- type: precision_at_30
|
| 819 |
+
value: 2.29
|
| 820 |
+
- type: precision_at_100
|
| 821 |
+
value: 0.86
|
| 822 |
+
- type: accuracy_at_3
|
| 823 |
+
value: 34.01
|
| 824 |
+
- type: accuracy_at_5
|
| 825 |
+
value: 39.37
|
| 826 |
+
- type: accuracy_at_10
|
| 827 |
+
value: 48.24
|
| 828 |
+
- task:
|
| 829 |
+
type: Retrieval
|
| 830 |
+
dataset:
|
| 831 |
+
type: climate-fever
|
| 832 |
+
name: MTEB ClimateFEVER
|
| 833 |
+
config: default
|
| 834 |
+
split: test
|
| 835 |
+
revision: None
|
| 836 |
+
metrics:
|
| 837 |
+
- type: ndcg_at_1
|
| 838 |
+
value: 19.8
|
| 839 |
+
- type: ndcg_at_3
|
| 840 |
+
value: 17.93
|
| 841 |
+
- type: ndcg_at_5
|
| 842 |
+
value: 19.39
|
| 843 |
+
- type: ndcg_at_10
|
| 844 |
+
value: 22.42
|
| 845 |
+
- type: ndcg_at_30
|
| 846 |
+
value: 26.79
|
| 847 |
+
- type: ndcg_at_100
|
| 848 |
+
value: 29.84
|
| 849 |
+
- type: map_at_1
|
| 850 |
+
value: 9.09
|
| 851 |
+
- type: map_at_3
|
| 852 |
+
value: 12.91
|
| 853 |
+
- type: map_at_5
|
| 854 |
+
value: 14.12
|
| 855 |
+
- type: map_at_10
|
| 856 |
+
value: 15.45
|
| 857 |
+
- type: map_at_30
|
| 858 |
+
value: 16.73
|
| 859 |
+
- type: map_at_100
|
| 860 |
+
value: 17.21
|
| 861 |
+
- type: recall_at_1
|
| 862 |
+
value: 9.09
|
| 863 |
+
- type: recall_at_3
|
| 864 |
+
value: 16.81
|
| 865 |
+
- type: recall_at_5
|
| 866 |
+
value: 20.9
|
| 867 |
+
- type: recall_at_10
|
| 868 |
+
value: 27.65
|
| 869 |
+
- type: recall_at_30
|
| 870 |
+
value: 41.23
|
| 871 |
+
- type: recall_at_100
|
| 872 |
+
value: 53.57
|
| 873 |
+
- type: precision_at_1
|
| 874 |
+
value: 19.8
|
| 875 |
+
- type: precision_at_3
|
| 876 |
+
value: 13.36
|
| 877 |
+
- type: precision_at_5
|
| 878 |
+
value: 10.33
|
| 879 |
+
- type: precision_at_10
|
| 880 |
+
value: 7.15
|
| 881 |
+
- type: precision_at_30
|
| 882 |
+
value: 3.66
|
| 883 |
+
- type: precision_at_100
|
| 884 |
+
value: 1.49
|
| 885 |
+
- type: accuracy_at_3
|
| 886 |
+
value: 36.22
|
| 887 |
+
- type: accuracy_at_5
|
| 888 |
+
value: 44.1
|
| 889 |
+
- type: accuracy_at_10
|
| 890 |
+
value: 55.11
|
| 891 |
+
- task:
|
| 892 |
+
type: Retrieval
|
| 893 |
+
dataset:
|
| 894 |
+
type: dbpedia-entity
|
| 895 |
+
name: MTEB DBPedia
|
| 896 |
+
config: default
|
| 897 |
+
split: test
|
| 898 |
+
revision: None
|
| 899 |
+
metrics:
|
| 900 |
+
- type: ndcg_at_1
|
| 901 |
+
value: 42.75
|
| 902 |
+
- type: ndcg_at_3
|
| 903 |
+
value: 35.67
|
| 904 |
+
- type: ndcg_at_5
|
| 905 |
+
value: 33.58
|
| 906 |
+
- type: ndcg_at_10
|
| 907 |
+
value: 32.19
|
| 908 |
+
- type: ndcg_at_30
|
| 909 |
+
value: 31.82
|
| 910 |
+
- type: ndcg_at_100
|
| 911 |
+
value: 35.87
|
| 912 |
+
- type: map_at_1
|
| 913 |
+
value: 7.05
|
| 914 |
+
- type: map_at_3
|
| 915 |
+
value: 10.5
|
| 916 |
+
- type: map_at_5
|
| 917 |
+
value: 12.06
|
| 918 |
+
- type: map_at_10
|
| 919 |
+
value: 14.29
|
| 920 |
+
- type: map_at_30
|
| 921 |
+
value: 17.38
|
| 922 |
+
- type: map_at_100
|
| 923 |
+
value: 19.58
|
| 924 |
+
- type: recall_at_1
|
| 925 |
+
value: 7.05
|
| 926 |
+
- type: recall_at_3
|
| 927 |
+
value: 11.89
|
| 928 |
+
- type: recall_at_5
|
| 929 |
+
value: 14.7
|
| 930 |
+
- type: recall_at_10
|
| 931 |
+
value: 19.78
|
| 932 |
+
- type: recall_at_30
|
| 933 |
+
value: 29.88
|
| 934 |
+
- type: recall_at_100
|
| 935 |
+
value: 42.4
|
| 936 |
+
- type: precision_at_1
|
| 937 |
+
value: 54.25
|
| 938 |
+
- type: precision_at_3
|
| 939 |
+
value: 39.42
|
| 940 |
+
- type: precision_at_5
|
| 941 |
+
value: 33.15
|
| 942 |
+
- type: precision_at_10
|
| 943 |
+
value: 25.95
|
| 944 |
+
- type: precision_at_30
|
| 945 |
+
value: 15.51
|
| 946 |
+
- type: precision_at_100
|
| 947 |
+
value: 7.9
|
| 948 |
+
- type: accuracy_at_3
|
| 949 |
+
value: 72.0
|
| 950 |
+
- type: accuracy_at_5
|
| 951 |
+
value: 77.75
|
| 952 |
+
- type: accuracy_at_10
|
| 953 |
+
value: 83.5
|
| 954 |
+
- task:
|
| 955 |
+
type: Retrieval
|
| 956 |
+
dataset:
|
| 957 |
+
type: fever
|
| 958 |
+
name: MTEB FEVER
|
| 959 |
+
config: default
|
| 960 |
+
split: test
|
| 961 |
+
revision: None
|
| 962 |
+
metrics:
|
| 963 |
+
- type: ndcg_at_1
|
| 964 |
+
value: 40.19
|
| 965 |
+
- type: ndcg_at_3
|
| 966 |
+
value: 50.51
|
| 967 |
+
- type: ndcg_at_5
|
| 968 |
+
value: 53.51
|
| 969 |
+
- type: ndcg_at_10
|
| 970 |
+
value: 56.45
|
| 971 |
+
- type: ndcg_at_30
|
| 972 |
+
value: 58.74
|
| 973 |
+
- type: ndcg_at_100
|
| 974 |
+
value: 59.72
|
| 975 |
+
- type: map_at_1
|
| 976 |
+
value: 37.56
|
| 977 |
+
- type: map_at_3
|
| 978 |
+
value: 46.74
|
| 979 |
+
- type: map_at_5
|
| 980 |
+
value: 48.46
|
| 981 |
+
- type: map_at_10
|
| 982 |
+
value: 49.7
|
| 983 |
+
- type: map_at_30
|
| 984 |
+
value: 50.31
|
| 985 |
+
- type: map_at_100
|
| 986 |
+
value: 50.43
|
| 987 |
+
- type: recall_at_1
|
| 988 |
+
value: 37.56
|
| 989 |
+
- type: recall_at_3
|
| 990 |
+
value: 58.28
|
| 991 |
+
- type: recall_at_5
|
| 992 |
+
value: 65.45
|
| 993 |
+
- type: recall_at_10
|
| 994 |
+
value: 74.28
|
| 995 |
+
- type: recall_at_30
|
| 996 |
+
value: 83.42
|
| 997 |
+
- type: recall_at_100
|
| 998 |
+
value: 88.76
|
| 999 |
+
- type: precision_at_1
|
| 1000 |
+
value: 40.19
|
| 1001 |
+
- type: precision_at_3
|
| 1002 |
+
value: 20.99
|
| 1003 |
+
- type: precision_at_5
|
| 1004 |
+
value: 14.24
|
| 1005 |
+
- type: precision_at_10
|
| 1006 |
+
value: 8.12
|
| 1007 |
+
- type: precision_at_30
|
| 1008 |
+
value: 3.06
|
| 1009 |
+
- type: precision_at_100
|
| 1010 |
+
value: 0.98
|
| 1011 |
+
- type: accuracy_at_3
|
| 1012 |
+
value: 62.3
|
| 1013 |
+
- type: accuracy_at_5
|
| 1014 |
+
value: 69.94
|
| 1015 |
+
- type: accuracy_at_10
|
| 1016 |
+
value: 79.13
|
| 1017 |
+
- task:
|
| 1018 |
+
type: Retrieval
|
| 1019 |
+
dataset:
|
| 1020 |
+
type: fiqa
|
| 1021 |
+
name: MTEB FiQA2018
|
| 1022 |
+
config: default
|
| 1023 |
+
split: test
|
| 1024 |
+
revision: None
|
| 1025 |
+
metrics:
|
| 1026 |
+
- type: ndcg_at_1
|
| 1027 |
+
value: 34.41
|
| 1028 |
+
- type: ndcg_at_3
|
| 1029 |
+
value: 33.2
|
| 1030 |
+
- type: ndcg_at_5
|
| 1031 |
+
value: 34.71
|
| 1032 |
+
- type: ndcg_at_10
|
| 1033 |
+
value: 37.1
|
| 1034 |
+
- type: ndcg_at_30
|
| 1035 |
+
value: 40.88
|
| 1036 |
+
- type: ndcg_at_100
|
| 1037 |
+
value: 44.12
|
| 1038 |
+
- type: map_at_1
|
| 1039 |
+
value: 17.27
|
| 1040 |
+
- type: map_at_3
|
| 1041 |
+
value: 25.36
|
| 1042 |
+
- type: map_at_5
|
| 1043 |
+
value: 27.76
|
| 1044 |
+
- type: map_at_10
|
| 1045 |
+
value: 29.46
|
| 1046 |
+
- type: map_at_30
|
| 1047 |
+
value: 30.74
|
| 1048 |
+
- type: map_at_100
|
| 1049 |
+
value: 31.29
|
| 1050 |
+
- type: recall_at_1
|
| 1051 |
+
value: 17.27
|
| 1052 |
+
- type: recall_at_3
|
| 1053 |
+
value: 30.46
|
| 1054 |
+
- type: recall_at_5
|
| 1055 |
+
value: 36.91
|
| 1056 |
+
- type: recall_at_10
|
| 1057 |
+
value: 44.47
|
| 1058 |
+
- type: recall_at_30
|
| 1059 |
+
value: 56.71
|
| 1060 |
+
- type: recall_at_100
|
| 1061 |
+
value: 70.72
|
| 1062 |
+
- type: precision_at_1
|
| 1063 |
+
value: 34.41
|
| 1064 |
+
- type: precision_at_3
|
| 1065 |
+
value: 22.32
|
| 1066 |
+
- type: precision_at_5
|
| 1067 |
+
value: 16.91
|
| 1068 |
+
- type: precision_at_10
|
| 1069 |
+
value: 10.53
|
| 1070 |
+
- type: precision_at_30
|
| 1071 |
+
value: 4.62
|
| 1072 |
+
- type: precision_at_100
|
| 1073 |
+
value: 1.79
|
| 1074 |
+
- type: accuracy_at_3
|
| 1075 |
+
value: 50.77
|
| 1076 |
+
- type: accuracy_at_5
|
| 1077 |
+
value: 57.56
|
| 1078 |
+
- type: accuracy_at_10
|
| 1079 |
+
value: 65.12
|
| 1080 |
+
- task:
|
| 1081 |
+
type: Retrieval
|
| 1082 |
+
dataset:
|
| 1083 |
+
type: hotpotqa
|
| 1084 |
+
name: MTEB HotpotQA
|
| 1085 |
+
config: default
|
| 1086 |
+
split: test
|
| 1087 |
+
revision: None
|
| 1088 |
+
metrics:
|
| 1089 |
+
- type: ndcg_at_1
|
| 1090 |
+
value: 57.93
|
| 1091 |
+
- type: ndcg_at_3
|
| 1092 |
+
value: 44.21
|
| 1093 |
+
- type: ndcg_at_5
|
| 1094 |
+
value: 46.4
|
| 1095 |
+
- type: ndcg_at_10
|
| 1096 |
+
value: 48.37
|
| 1097 |
+
- type: ndcg_at_30
|
| 1098 |
+
value: 50.44
|
| 1099 |
+
- type: ndcg_at_100
|
| 1100 |
+
value: 51.86
|
| 1101 |
+
- type: map_at_1
|
| 1102 |
+
value: 28.97
|
| 1103 |
+
- type: map_at_3
|
| 1104 |
+
value: 36.79
|
| 1105 |
+
- type: map_at_5
|
| 1106 |
+
value: 38.31
|
| 1107 |
+
- type: map_at_10
|
| 1108 |
+
value: 39.32
|
| 1109 |
+
- type: map_at_30
|
| 1110 |
+
value: 39.99
|
| 1111 |
+
- type: map_at_100
|
| 1112 |
+
value: 40.2
|
| 1113 |
+
- type: recall_at_1
|
| 1114 |
+
value: 28.97
|
| 1115 |
+
- type: recall_at_3
|
| 1116 |
+
value: 41.01
|
| 1117 |
+
- type: recall_at_5
|
| 1118 |
+
value: 45.36
|
| 1119 |
+
- type: recall_at_10
|
| 1120 |
+
value: 50.32
|
| 1121 |
+
- type: recall_at_30
|
| 1122 |
+
value: 57.38
|
| 1123 |
+
- type: recall_at_100
|
| 1124 |
+
value: 64.06
|
| 1125 |
+
- type: precision_at_1
|
| 1126 |
+
value: 57.93
|
| 1127 |
+
- type: precision_at_3
|
| 1128 |
+
value: 27.34
|
| 1129 |
+
- type: precision_at_5
|
| 1130 |
+
value: 18.14
|
| 1131 |
+
- type: precision_at_10
|
| 1132 |
+
value: 10.06
|
| 1133 |
+
- type: precision_at_30
|
| 1134 |
+
value: 3.82
|
| 1135 |
+
- type: precision_at_100
|
| 1136 |
+
value: 1.28
|
| 1137 |
+
- type: accuracy_at_3
|
| 1138 |
+
value: 71.03
|
| 1139 |
+
- type: accuracy_at_5
|
| 1140 |
+
value: 75.14
|
| 1141 |
+
- type: accuracy_at_10
|
| 1142 |
+
value: 79.84
|
| 1143 |
+
- task:
|
| 1144 |
+
type: Retrieval
|
| 1145 |
+
dataset:
|
| 1146 |
+
type: msmarco
|
| 1147 |
+
name: MTEB MSMARCO
|
| 1148 |
+
config: default
|
| 1149 |
+
split: dev
|
| 1150 |
+
revision: None
|
| 1151 |
+
metrics:
|
| 1152 |
+
- type: ndcg_at_1
|
| 1153 |
+
value: 19.74
|
| 1154 |
+
- type: ndcg_at_3
|
| 1155 |
+
value: 29.47
|
| 1156 |
+
- type: ndcg_at_5
|
| 1157 |
+
value: 32.99
|
| 1158 |
+
- type: ndcg_at_10
|
| 1159 |
+
value: 36.76
|
| 1160 |
+
- type: ndcg_at_30
|
| 1161 |
+
value: 40.52
|
| 1162 |
+
- type: ndcg_at_100
|
| 1163 |
+
value: 42.78
|
| 1164 |
+
- type: map_at_1
|
| 1165 |
+
value: 19.2
|
| 1166 |
+
- type: map_at_3
|
| 1167 |
+
value: 26.81
|
| 1168 |
+
- type: map_at_5
|
| 1169 |
+
value: 28.78
|
| 1170 |
+
- type: map_at_10
|
| 1171 |
+
value: 30.35
|
| 1172 |
+
- type: map_at_30
|
| 1173 |
+
value: 31.3
|
| 1174 |
+
- type: map_at_100
|
| 1175 |
+
value: 31.57
|
| 1176 |
+
- type: recall_at_1
|
| 1177 |
+
value: 19.2
|
| 1178 |
+
- type: recall_at_3
|
| 1179 |
+
value: 36.59
|
| 1180 |
+
- type: recall_at_5
|
| 1181 |
+
value: 45.08
|
| 1182 |
+
- type: recall_at_10
|
| 1183 |
+
value: 56.54
|
| 1184 |
+
- type: recall_at_30
|
| 1185 |
+
value: 72.05
|
| 1186 |
+
- type: recall_at_100
|
| 1187 |
+
value: 84.73
|
| 1188 |
+
- type: precision_at_1
|
| 1189 |
+
value: 19.74
|
| 1190 |
+
- type: precision_at_3
|
| 1191 |
+
value: 12.61
|
| 1192 |
+
- type: precision_at_5
|
| 1193 |
+
value: 9.37
|
| 1194 |
+
- type: precision_at_10
|
| 1195 |
+
value: 5.89
|
| 1196 |
+
- type: precision_at_30
|
| 1197 |
+
value: 2.52
|
| 1198 |
+
- type: precision_at_100
|
| 1199 |
+
value: 0.89
|
| 1200 |
+
- type: accuracy_at_3
|
| 1201 |
+
value: 37.38
|
| 1202 |
+
- type: accuracy_at_5
|
| 1203 |
+
value: 46.06
|
| 1204 |
+
- type: accuracy_at_10
|
| 1205 |
+
value: 57.62
|
| 1206 |
+
- task:
|
| 1207 |
+
type: Retrieval
|
| 1208 |
+
dataset:
|
| 1209 |
+
type: nq
|
| 1210 |
+
name: MTEB NQ
|
| 1211 |
+
config: default
|
| 1212 |
+
split: test
|
| 1213 |
+
revision: None
|
| 1214 |
+
metrics:
|
| 1215 |
+
- type: ndcg_at_1
|
| 1216 |
+
value: 25.9
|
| 1217 |
+
- type: ndcg_at_3
|
| 1218 |
+
value: 35.97
|
| 1219 |
+
- type: ndcg_at_5
|
| 1220 |
+
value: 40.27
|
| 1221 |
+
- type: ndcg_at_10
|
| 1222 |
+
value: 44.44
|
| 1223 |
+
- type: ndcg_at_30
|
| 1224 |
+
value: 48.31
|
| 1225 |
+
- type: ndcg_at_100
|
| 1226 |
+
value: 50.14
|
| 1227 |
+
- type: map_at_1
|
| 1228 |
+
value: 23.03
|
| 1229 |
+
- type: map_at_3
|
| 1230 |
+
value: 32.45
|
| 1231 |
+
- type: map_at_5
|
| 1232 |
+
value: 34.99
|
| 1233 |
+
- type: map_at_10
|
| 1234 |
+
value: 36.84
|
| 1235 |
+
- type: map_at_30
|
| 1236 |
+
value: 37.92
|
| 1237 |
+
- type: map_at_100
|
| 1238 |
+
value: 38.16
|
| 1239 |
+
- type: recall_at_1
|
| 1240 |
+
value: 23.03
|
| 1241 |
+
- type: recall_at_3
|
| 1242 |
+
value: 43.49
|
| 1243 |
+
- type: recall_at_5
|
| 1244 |
+
value: 53.41
|
| 1245 |
+
- type: recall_at_10
|
| 1246 |
+
value: 65.65
|
| 1247 |
+
- type: recall_at_30
|
| 1248 |
+
value: 80.79
|
| 1249 |
+
- type: recall_at_100
|
| 1250 |
+
value: 90.59
|
| 1251 |
+
- type: precision_at_1
|
| 1252 |
+
value: 25.9
|
| 1253 |
+
- type: precision_at_3
|
| 1254 |
+
value: 16.76
|
| 1255 |
+
- type: precision_at_5
|
| 1256 |
+
value: 12.54
|
| 1257 |
+
- type: precision_at_10
|
| 1258 |
+
value: 7.78
|
| 1259 |
+
- type: precision_at_30
|
| 1260 |
+
value: 3.23
|
| 1261 |
+
- type: precision_at_100
|
| 1262 |
+
value: 1.1
|
| 1263 |
+
- type: accuracy_at_3
|
| 1264 |
+
value: 47.31
|
| 1265 |
+
- type: accuracy_at_5
|
| 1266 |
+
value: 57.16
|
| 1267 |
+
- type: accuracy_at_10
|
| 1268 |
+
value: 69.09
|
| 1269 |
+
- task:
|
| 1270 |
+
type: Retrieval
|
| 1271 |
+
dataset:
|
| 1272 |
+
type: nfcorpus
|
| 1273 |
+
name: MTEB NFCorpus
|
| 1274 |
+
config: default
|
| 1275 |
+
split: test
|
| 1276 |
+
revision: None
|
| 1277 |
+
metrics:
|
| 1278 |
+
- type: ndcg_at_1
|
| 1279 |
+
value: 40.87
|
| 1280 |
+
- type: ndcg_at_3
|
| 1281 |
+
value: 36.79
|
| 1282 |
+
- type: ndcg_at_5
|
| 1283 |
+
value: 34.47
|
| 1284 |
+
- type: ndcg_at_10
|
| 1285 |
+
value: 32.05
|
| 1286 |
+
- type: ndcg_at_30
|
| 1287 |
+
value: 29.23
|
| 1288 |
+
- type: ndcg_at_100
|
| 1289 |
+
value: 29.84
|
| 1290 |
+
- type: map_at_1
|
| 1291 |
+
value: 5.05
|
| 1292 |
+
- type: map_at_3
|
| 1293 |
+
value: 8.5
|
| 1294 |
+
- type: map_at_5
|
| 1295 |
+
value: 9.87
|
| 1296 |
+
- type: map_at_10
|
| 1297 |
+
value: 11.71
|
| 1298 |
+
- type: map_at_30
|
| 1299 |
+
value: 13.48
|
| 1300 |
+
- type: map_at_100
|
| 1301 |
+
value: 14.86
|
| 1302 |
+
- type: recall_at_1
|
| 1303 |
+
value: 5.05
|
| 1304 |
+
- type: recall_at_3
|
| 1305 |
+
value: 9.55
|
| 1306 |
+
- type: recall_at_5
|
| 1307 |
+
value: 11.91
|
| 1308 |
+
- type: recall_at_10
|
| 1309 |
+
value: 16.07
|
| 1310 |
+
- type: recall_at_30
|
| 1311 |
+
value: 22.13
|
| 1312 |
+
- type: recall_at_100
|
| 1313 |
+
value: 30.7
|
| 1314 |
+
- type: precision_at_1
|
| 1315 |
+
value: 42.72
|
| 1316 |
+
- type: precision_at_3
|
| 1317 |
+
value: 34.78
|
| 1318 |
+
- type: precision_at_5
|
| 1319 |
+
value: 30.03
|
| 1320 |
+
- type: precision_at_10
|
| 1321 |
+
value: 23.93
|
| 1322 |
+
- type: precision_at_30
|
| 1323 |
+
value: 14.61
|
| 1324 |
+
- type: precision_at_100
|
| 1325 |
+
value: 7.85
|
| 1326 |
+
- type: accuracy_at_3
|
| 1327 |
+
value: 58.2
|
| 1328 |
+
- type: accuracy_at_5
|
| 1329 |
+
value: 64.09
|
| 1330 |
+
- type: accuracy_at_10
|
| 1331 |
+
value: 69.35
|
| 1332 |
+
- task:
|
| 1333 |
+
type: Retrieval
|
| 1334 |
+
dataset:
|
| 1335 |
+
type: quora
|
| 1336 |
+
name: MTEB QuoraRetrieval
|
| 1337 |
+
config: default
|
| 1338 |
+
split: test
|
| 1339 |
+
revision: None
|
| 1340 |
+
metrics:
|
| 1341 |
+
- type: ndcg_at_1
|
| 1342 |
+
value: 80.62
|
| 1343 |
+
- type: ndcg_at_3
|
| 1344 |
+
value: 84.62
|
| 1345 |
+
- type: ndcg_at_5
|
| 1346 |
+
value: 86.25
|
| 1347 |
+
- type: ndcg_at_10
|
| 1348 |
+
value: 87.7
|
| 1349 |
+
- type: ndcg_at_30
|
| 1350 |
+
value: 88.63
|
| 1351 |
+
- type: ndcg_at_100
|
| 1352 |
+
value: 88.95
|
| 1353 |
+
- type: map_at_1
|
| 1354 |
+
value: 69.91
|
| 1355 |
+
- type: map_at_3
|
| 1356 |
+
value: 80.7
|
| 1357 |
+
- type: map_at_5
|
| 1358 |
+
value: 82.57
|
| 1359 |
+
- type: map_at_10
|
| 1360 |
+
value: 83.78
|
| 1361 |
+
- type: map_at_30
|
| 1362 |
+
value: 84.33
|
| 1363 |
+
- type: map_at_100
|
| 1364 |
+
value: 84.44
|
| 1365 |
+
- type: recall_at_1
|
| 1366 |
+
value: 69.91
|
| 1367 |
+
- type: recall_at_3
|
| 1368 |
+
value: 86.36
|
| 1369 |
+
- type: recall_at_5
|
| 1370 |
+
value: 90.99
|
| 1371 |
+
- type: recall_at_10
|
| 1372 |
+
value: 95.19
|
| 1373 |
+
- type: recall_at_30
|
| 1374 |
+
value: 98.25
|
| 1375 |
+
- type: recall_at_100
|
| 1376 |
+
value: 99.47
|
| 1377 |
+
- type: precision_at_1
|
| 1378 |
+
value: 80.62
|
| 1379 |
+
- type: precision_at_3
|
| 1380 |
+
value: 37.03
|
| 1381 |
+
- type: precision_at_5
|
| 1382 |
+
value: 24.36
|
| 1383 |
+
- type: precision_at_10
|
| 1384 |
+
value: 13.4
|
| 1385 |
+
- type: precision_at_30
|
| 1386 |
+
value: 4.87
|
| 1387 |
+
- type: precision_at_100
|
| 1388 |
+
value: 1.53
|
| 1389 |
+
- type: accuracy_at_3
|
| 1390 |
+
value: 92.25
|
| 1391 |
+
- type: accuracy_at_5
|
| 1392 |
+
value: 95.29
|
| 1393 |
+
- type: accuracy_at_10
|
| 1394 |
+
value: 97.74
|
| 1395 |
+
- task:
|
| 1396 |
+
type: Retrieval
|
| 1397 |
+
dataset:
|
| 1398 |
+
type: scidocs
|
| 1399 |
+
name: MTEB SCIDOCS
|
| 1400 |
+
config: default
|
| 1401 |
+
split: test
|
| 1402 |
+
revision: None
|
| 1403 |
+
metrics:
|
| 1404 |
+
- type: ndcg_at_1
|
| 1405 |
+
value: 24.1
|
| 1406 |
+
- type: ndcg_at_3
|
| 1407 |
+
value: 20.18
|
| 1408 |
+
- type: ndcg_at_5
|
| 1409 |
+
value: 17.72
|
| 1410 |
+
- type: ndcg_at_10
|
| 1411 |
+
value: 21.5
|
| 1412 |
+
- type: ndcg_at_30
|
| 1413 |
+
value: 26.66
|
| 1414 |
+
- type: ndcg_at_100
|
| 1415 |
+
value: 30.95
|
| 1416 |
+
- type: map_at_1
|
| 1417 |
+
value: 4.88
|
| 1418 |
+
- type: map_at_3
|
| 1419 |
+
value: 9.09
|
| 1420 |
+
- type: map_at_5
|
| 1421 |
+
value: 10.99
|
| 1422 |
+
- type: map_at_10
|
| 1423 |
+
value: 12.93
|
| 1424 |
+
- type: map_at_30
|
| 1425 |
+
value: 14.71
|
| 1426 |
+
- type: map_at_100
|
| 1427 |
+
value: 15.49
|
| 1428 |
+
- type: recall_at_1
|
| 1429 |
+
value: 4.88
|
| 1430 |
+
- type: recall_at_3
|
| 1431 |
+
value: 11.55
|
| 1432 |
+
- type: recall_at_5
|
| 1433 |
+
value: 15.91
|
| 1434 |
+
- type: recall_at_10
|
| 1435 |
+
value: 22.82
|
| 1436 |
+
- type: recall_at_30
|
| 1437 |
+
value: 35.7
|
| 1438 |
+
- type: recall_at_100
|
| 1439 |
+
value: 50.41
|
| 1440 |
+
- type: precision_at_1
|
| 1441 |
+
value: 24.1
|
| 1442 |
+
- type: precision_at_3
|
| 1443 |
+
value: 19.0
|
| 1444 |
+
- type: precision_at_5
|
| 1445 |
+
value: 15.72
|
| 1446 |
+
- type: precision_at_10
|
| 1447 |
+
value: 11.27
|
| 1448 |
+
- type: precision_at_30
|
| 1449 |
+
value: 5.87
|
| 1450 |
+
- type: precision_at_100
|
| 1451 |
+
value: 2.49
|
| 1452 |
+
- type: accuracy_at_3
|
| 1453 |
+
value: 43.0
|
| 1454 |
+
- type: accuracy_at_5
|
| 1455 |
+
value: 51.6
|
| 1456 |
+
- type: accuracy_at_10
|
| 1457 |
+
value: 62.7
|
| 1458 |
+
- task:
|
| 1459 |
+
type: Retrieval
|
| 1460 |
+
dataset:
|
| 1461 |
+
type: scifact
|
| 1462 |
+
name: MTEB SciFact
|
| 1463 |
+
config: default
|
| 1464 |
+
split: test
|
| 1465 |
+
revision: None
|
| 1466 |
+
metrics:
|
| 1467 |
+
- type: ndcg_at_1
|
| 1468 |
+
value: 52.33
|
| 1469 |
+
- type: ndcg_at_3
|
| 1470 |
+
value: 61.47
|
| 1471 |
+
- type: ndcg_at_5
|
| 1472 |
+
value: 63.82
|
| 1473 |
+
- type: ndcg_at_10
|
| 1474 |
+
value: 65.81
|
| 1475 |
+
- type: ndcg_at_30
|
| 1476 |
+
value: 67.75
|
| 1477 |
+
- type: ndcg_at_100
|
| 1478 |
+
value: 68.96
|
| 1479 |
+
- type: map_at_1
|
| 1480 |
+
value: 50.46
|
| 1481 |
+
- type: map_at_3
|
| 1482 |
+
value: 58.51
|
| 1483 |
+
- type: map_at_5
|
| 1484 |
+
value: 60.12
|
| 1485 |
+
- type: map_at_10
|
| 1486 |
+
value: 61.07
|
| 1487 |
+
- type: map_at_30
|
| 1488 |
+
value: 61.64
|
| 1489 |
+
- type: map_at_100
|
| 1490 |
+
value: 61.8
|
| 1491 |
+
- type: recall_at_1
|
| 1492 |
+
value: 50.46
|
| 1493 |
+
- type: recall_at_3
|
| 1494 |
+
value: 67.81
|
| 1495 |
+
- type: recall_at_5
|
| 1496 |
+
value: 73.6
|
| 1497 |
+
- type: recall_at_10
|
| 1498 |
+
value: 79.31
|
| 1499 |
+
- type: recall_at_30
|
| 1500 |
+
value: 86.8
|
| 1501 |
+
- type: recall_at_100
|
| 1502 |
+
value: 93.5
|
| 1503 |
+
- type: precision_at_1
|
| 1504 |
+
value: 52.33
|
| 1505 |
+
- type: precision_at_3
|
| 1506 |
+
value: 24.56
|
| 1507 |
+
- type: precision_at_5
|
| 1508 |
+
value: 16.27
|
| 1509 |
+
- type: precision_at_10
|
| 1510 |
+
value: 8.9
|
| 1511 |
+
- type: precision_at_30
|
| 1512 |
+
value: 3.28
|
| 1513 |
+
- type: precision_at_100
|
| 1514 |
+
value: 1.06
|
| 1515 |
+
- type: accuracy_at_3
|
| 1516 |
+
value: 69.67
|
| 1517 |
+
- type: accuracy_at_5
|
| 1518 |
+
value: 75.0
|
| 1519 |
+
- type: accuracy_at_10
|
| 1520 |
+
value: 80.67
|
| 1521 |
+
- task:
|
| 1522 |
+
type: Retrieval
|
| 1523 |
+
dataset:
|
| 1524 |
+
type: trec-covid
|
| 1525 |
+
name: MTEB TRECCOVID
|
| 1526 |
+
config: default
|
| 1527 |
+
split: test
|
| 1528 |
+
revision: None
|
| 1529 |
+
metrics:
|
| 1530 |
+
- type: ndcg_at_1
|
| 1531 |
+
value: 57.0
|
| 1532 |
+
- type: ndcg_at_3
|
| 1533 |
+
value: 53.78
|
| 1534 |
+
- type: ndcg_at_5
|
| 1535 |
+
value: 52.62
|
| 1536 |
+
- type: ndcg_at_10
|
| 1537 |
+
value: 48.9
|
| 1538 |
+
- type: ndcg_at_30
|
| 1539 |
+
value: 44.2
|
| 1540 |
+
- type: ndcg_at_100
|
| 1541 |
+
value: 36.53
|
| 1542 |
+
- type: map_at_1
|
| 1543 |
+
value: 0.16
|
| 1544 |
+
- type: map_at_3
|
| 1545 |
+
value: 0.41
|
| 1546 |
+
- type: map_at_5
|
| 1547 |
+
value: 0.62
|
| 1548 |
+
- type: map_at_10
|
| 1549 |
+
value: 1.07
|
| 1550 |
+
- type: map_at_30
|
| 1551 |
+
value: 2.46
|
| 1552 |
+
- type: map_at_100
|
| 1553 |
+
value: 5.52
|
| 1554 |
+
- type: recall_at_1
|
| 1555 |
+
value: 0.16
|
| 1556 |
+
- type: recall_at_3
|
| 1557 |
+
value: 0.45
|
| 1558 |
+
- type: recall_at_5
|
| 1559 |
+
value: 0.72
|
| 1560 |
+
- type: recall_at_10
|
| 1561 |
+
value: 1.33
|
| 1562 |
+
- type: recall_at_30
|
| 1563 |
+
value: 3.46
|
| 1564 |
+
- type: recall_at_100
|
| 1565 |
+
value: 8.73
|
| 1566 |
+
- type: precision_at_1
|
| 1567 |
+
value: 62.0
|
| 1568 |
+
- type: precision_at_3
|
| 1569 |
+
value: 57.33
|
| 1570 |
+
- type: precision_at_5
|
| 1571 |
+
value: 56.0
|
| 1572 |
+
- type: precision_at_10
|
| 1573 |
+
value: 52.0
|
| 1574 |
+
- type: precision_at_30
|
| 1575 |
+
value: 46.2
|
| 1576 |
+
- type: precision_at_100
|
| 1577 |
+
value: 37.22
|
| 1578 |
+
- type: accuracy_at_3
|
| 1579 |
+
value: 82.0
|
| 1580 |
+
- type: accuracy_at_5
|
| 1581 |
+
value: 90.0
|
| 1582 |
+
- type: accuracy_at_10
|
| 1583 |
+
value: 92.0
|
| 1584 |
+
- task:
|
| 1585 |
+
type: Retrieval
|
| 1586 |
+
dataset:
|
| 1587 |
+
type: webis-touche2020
|
| 1588 |
+
name: MTEB Touche2020
|
| 1589 |
+
config: default
|
| 1590 |
+
split: test
|
| 1591 |
+
revision: None
|
| 1592 |
+
metrics:
|
| 1593 |
+
- type: ndcg_at_1
|
| 1594 |
+
value: 20.41
|
| 1595 |
+
- type: ndcg_at_3
|
| 1596 |
+
value: 17.62
|
| 1597 |
+
- type: ndcg_at_5
|
| 1598 |
+
value: 17.16
|
| 1599 |
+
- type: ndcg_at_10
|
| 1600 |
+
value: 17.09
|
| 1601 |
+
- type: ndcg_at_30
|
| 1602 |
+
value: 20.1
|
| 1603 |
+
- type: ndcg_at_100
|
| 1604 |
+
value: 26.33
|
| 1605 |
+
- type: map_at_1
|
| 1606 |
+
value: 2.15
|
| 1607 |
+
- type: map_at_3
|
| 1608 |
+
value: 3.59
|
| 1609 |
+
- type: map_at_5
|
| 1610 |
+
value: 5.07
|
| 1611 |
+
- type: map_at_10
|
| 1612 |
+
value: 6.95
|
| 1613 |
+
- type: map_at_30
|
| 1614 |
+
value: 9.01
|
| 1615 |
+
- type: map_at_100
|
| 1616 |
+
value: 10.54
|
| 1617 |
+
- type: recall_at_1
|
| 1618 |
+
value: 2.15
|
| 1619 |
+
- type: recall_at_3
|
| 1620 |
+
value: 4.5
|
| 1621 |
+
- type: recall_at_5
|
| 1622 |
+
value: 7.54
|
| 1623 |
+
- type: recall_at_10
|
| 1624 |
+
value: 12.46
|
| 1625 |
+
- type: recall_at_30
|
| 1626 |
+
value: 21.9
|
| 1627 |
+
- type: recall_at_100
|
| 1628 |
+
value: 36.58
|
| 1629 |
+
- type: precision_at_1
|
| 1630 |
+
value: 22.45
|
| 1631 |
+
- type: precision_at_3
|
| 1632 |
+
value: 19.05
|
| 1633 |
+
- type: precision_at_5
|
| 1634 |
+
value: 17.55
|
| 1635 |
+
- type: precision_at_10
|
| 1636 |
+
value: 15.51
|
| 1637 |
+
- type: precision_at_30
|
| 1638 |
+
value: 10.07
|
| 1639 |
+
- type: precision_at_100
|
| 1640 |
+
value: 5.57
|
| 1641 |
+
- type: accuracy_at_3
|
| 1642 |
+
value: 42.86
|
| 1643 |
+
- type: accuracy_at_5
|
| 1644 |
+
value: 53.06
|
| 1645 |
+
- type: accuracy_at_10
|
| 1646 |
+
value: 69.39
|
| 1647 |
+
- task:
|
| 1648 |
+
type: Retrieval
|
| 1649 |
+
dataset:
|
| 1650 |
+
type: BeIR/cqadupstack
|
| 1651 |
+
name: MTEB CQADupstackRetrieval
|
| 1652 |
+
config: default
|
| 1653 |
+
split: test
|
| 1654 |
+
revision: None
|
| 1655 |
+
metrics:
|
| 1656 |
+
- type: ndcg_at_10
|
| 1657 |
+
value: 41.59
|
| 1658 |
+
license: apache-2.0
|
| 1659 |
+
language:
|
| 1660 |
+
- en
|
| 1661 |
+
pipeline_tag: feature-extraction
|
| 1662 |
+
---
|
| 1663 |
+
|
| 1664 |
+
|
| 1665 |
+
<p align="center">
|
| 1666 |
+
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21.967c-.442.422-1.212.488-1.813.757l-23.113 10.389-9.875 4.514c-2.305-6.09-4.609-12.181-6.614-18.676 7.64-4.837 15.567-8.54 22.18-13.873 9.697-7.821 18.931-16.361 27.443-25.455 5.613-5.998 12.679-11.331 14.201-20.475.699-4.2 2.384-8.235 3.623-12.345z"></path><path fill="#e95514" d="M815.103 467.384c3.356-1.894 6.641-3.415 9.94-4.903l23.113-10.389c.6-.269 1.371-.335 1.813-.757l22.808-21.967c2.008-1.962 3.593-4.371 5.668-6.248 8.272-7.48 12.854-16.394 12.73-27.863-.049-4.505 1.828-9.031 2.847-13.956 5.427.559 10.836 1.526 16.609 2.68-1.863 17.245-10.272 32.119-16.91 47.544-2.387 5.546-7.239 10.223-11.588 14.69-7.753 7.962-15.978 15.464-24.011 23.152-.478.458-.944 1.002-1.525 1.269l-36.069 16.355c-2.076-6.402-3.783-12.81-5.425-19.607z"></path><path fill="#eb620b" d="M783.944 404.402c9.499-8.388 19.556-15.905 28.437-24.621 6.631-6.508 11.744-14.564 17.575-22.273 9.271 4.016 18.501 8.375 27.893 13.43-4.134 7.07-8.017 13.778-12.833 19.731-5.785 7.15-12.109 13.917-18.666 20.376-7.99 7.869-16.466 15.244-24.731 22.832l-17.674-29.475z"></path><path fill="#ea544c" d="M1197.986 854.686c-9.756-3.309-16.79-10.044-22.88-18.059l-28.001-36.417c8.601-5.939 17.348-11.563 26.758-17.075 1.615 1.026 2.639 1.876 3.505 2.865l26.664 30.44c3.723 4.139 7.995 7.785 12.017 11.656l-18.064 26.591z"></path><path fill="#ec6333" d="M1351.41 332.903c-5.667-9.409-10.361-19.149-16.445-27.926-2.833-4.087-8.504-6.128-12.62-9.432-3.184-2.555-5.849-5.745-8.926-8.447-3.454-3.033-6.756-6.52-10.753-8.598-9.391-4.88-19.157-9.039-29.138-13.499 1.18-5.441 2.727-10.873 4.81-16.607 11.918 4.674 24.209 8.261 34.464 14.962 14.239 9.304 29.011 18.453 39.595 32.464 2.386 3.159 5.121 6.077 7.884 8.923 6.564 6.764 10.148 14.927 11.723 24.093l-20.594 4.067z"></path><path fill="#eb5e5b" d="M1117 536.549c-6.113-4.702-9.965-11.44-11.917-18.955-2.292-8.819-4.066-17.74-9.467-25.337-4.327-6.085-3.122-13.382-4.6-20.088l-4.55-21.241c-1.59-8.054-3.172-16.118-4.422-24.23l-5.037-36.129c6.382-1.43 12.777-2.462 19.582-3.443 1.906 11.646 3.426 23.24 4.878 34.842.307 2.453.717 4.973.477 7.402-1.86 18.84 2.834 36.934 5.347 55.352 1.474 10.806 4.885 20.848 7.101 31.302 1.394 6.579 1.774 13.374 2.609 20.523z"></path><path fill="#ec644b" d="M1263.638 290.071c4.697 2.713 9.183 5.458 13.45 8.509 17.199 12.295 33.158 25.836 43.873 44.907-8.026 4.725-16.095 9.106-24.83 13.372-11.633-15.937-25.648-28.515-41.888-38.689-1.609-1.008-3.555-1.48-5.344-2.2 2.329-3.852 4.766-7.645 6.959-11.573l7.78-14.326z"></path><path fill="#eb5f2d" d="M1372.453 328.903c-2.025-9.233-5.608-17.396-12.172-24.16-2.762-2.846-5.498-5.764-7.884-8.923-10.584-14.01-25.356-23.16-39.595-32.464-10.256-6.701-22.546-10.289-34.284-15.312.325-5.246 1.005-10.444 2.027-15.863l47.529 22.394c.89.428 1.83.901 2.516 1.584l45.564 45.193c7.69 7.233 9.352 16.472 11.849 26.084-5.032.773-10.066 1.154-15.55 1.466z"></path><path fill="#e95a0f" d="M801.776 434.171c8.108-7.882 16.584-15.257 24.573-23.126 6.558-6.459 12.881-13.226 18.666-20.376 4.817-5.953 8.7-12.661 13.011-19.409 5.739 1.338 11.463 3.051 17.581 4.838-.845 4.183-2.53 8.219-3.229 12.418-1.522 9.144-8.588 14.477-14.201 20.475-8.512 9.094-17.745 17.635-27.443 25.455-6.613 5.333-14.54 9.036-22.223 13.51-2.422-4.469-4.499-8.98-6.735-13.786z"></path><path fill="#eb5e5b" d="M1248.533 316.002c2.155.688 4.101 1.159 5.71 2.168 16.24 10.174 30.255 22.752 41.532 38.727-7.166 5.736-14.641 11.319-22.562 16.731-1.16-1.277-1.684-2.585-2.615-3.46l-38.694-36.2 14.203-15.029c.803-.86 1.38-1.93 2.427-2.936z"></path><path fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 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|
| 1667 |
+
</p>
|
| 1668 |
+
|
| 1669 |
+
<p align="center">
|
| 1670 |
+
<b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><b>Mixedbread</b></a>.</b>
|
| 1671 |
+
</p>
|
| 1672 |
+
|
| 1673 |
+
# mixedbread-ai/mxbai-embed-xsmall-v1
|
| 1674 |
+
|
| 1675 |
+
This model is an open-source English embedding model developed by [Mixedbread](https://mixedbread.ai). It's built upon [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) and trained with the [AnglE loss](https://arxiv.org/abs/2309.12871) and [Espresso](https://arxiv.org/abs/2402.14776). Read more details in our [blog post](https://www.mixedbread.ai/blog/mxbai-embed-xsmall-v1).
|
| 1676 |
+
|
| 1677 |
+
**In a bread loaf**:
|
| 1678 |
+
- State-of-the-art performance
|
| 1679 |
+
- Supports both [binary quantization and Matryoshka Representation Learning (MRL)](#binary-quantization-and-matryoshka).
|
| 1680 |
+
- Optimized for retrieval tasks
|
| 1681 |
+
|
| 1682 |
+
## Performance
|
| 1683 |
+
|
| 1684 |
+
|
| 1685 |
+
## Binary Quantization and Matryoshka
|
| 1686 |
+
|
| 1687 |
+
Our model supports both [binary quantization](https://www.mixedbread.ai/blog/binary-quantization) and [Matryoshka Representation Learning (MRL)](https://www.mixedbread.ai/blog/mxbai-embed-2d-large-v1), allowing for significant efficiency gains:
|
| 1688 |
+
|
| 1689 |
+
- Binary quantization: Retains 93.9% of performance while increasing efficiency by a factor of 32
|
| 1690 |
+
- MRL: A 33% reduction in vector size still leaves 96.2% of model performance
|
| 1691 |
+
|
| 1692 |
+
These optimizations can lead to substantial reductions in infrastructure costs for cloud computing and vector databases. Read more [here](https://www.mixedbread.ai/blog/binary-mrl).
|
| 1693 |
+
|
| 1694 |
+
## Quickstart
|
| 1695 |
+
|
| 1696 |
+
Here are several ways to produce German sentence embeddings using our model.
|
| 1697 |
+
|
| 1698 |
+
<details>
|
| 1699 |
+
<summary> angle-emb </summary>
|
| 1700 |
+
|
| 1701 |
+
```bash
|
| 1702 |
+
pip install -U angle-emb
|
| 1703 |
+
```
|
| 1704 |
+
|
| 1705 |
+
```python
|
| 1706 |
+
from angle_emb import AnglE
|
| 1707 |
+
from angle_emb.utils import cosine_similarity
|
| 1708 |
+
|
| 1709 |
+
# 1. Specify preferred dimensions
|
| 1710 |
+
dimensions = 384
|
| 1711 |
+
|
| 1712 |
+
# 2. Load model and set pooling strategy to avg
|
| 1713 |
+
model = AnglE.from_pretrained(
|
| 1714 |
+
"mixedbread-ai/mxbai-embed-xsmall-v1",
|
| 1715 |
+
pooling_strategy='avg').cuda()
|
| 1716 |
+
|
| 1717 |
+
query = 'A man is eating a piece of bread'
|
| 1718 |
+
|
| 1719 |
+
docs = [
|
| 1720 |
+
query,
|
| 1721 |
+
"A man is eating food.",
|
| 1722 |
+
"A man is eating pasta.",
|
| 1723 |
+
"The girl is carrying a baby.",
|
| 1724 |
+
"A man is riding a horse.",
|
| 1725 |
+
]
|
| 1726 |
+
|
| 1727 |
+
# 3. Encode
|
| 1728 |
+
embeddings = model.encode(docs, embedding_size=dimensions)
|
| 1729 |
+
|
| 1730 |
+
for doc, emb in zip(docs[1:], embeddings[1:]):
|
| 1731 |
+
print(f'{query} ||| {doc}', cosine_similarity(embeddings[0], emb))
|
| 1732 |
+
```
|
| 1733 |
+
</details>
|
| 1734 |
+
|
| 1735 |
+
<details>
|
| 1736 |
+
<summary> Sentence Transformers </summary>
|
| 1737 |
+
|
| 1738 |
+
```bash
|
| 1739 |
+
python -m pip install -U sentence-transformers
|
| 1740 |
+
```
|
| 1741 |
+
|
| 1742 |
+
```python
|
| 1743 |
+
from sentence_transformers import SentenceTransformer
|
| 1744 |
+
from sentence_transformers.util import cos_sim
|
| 1745 |
+
|
| 1746 |
+
# 1. Specify preferred dimensions
|
| 1747 |
+
dimensions = 384
|
| 1748 |
+
|
| 1749 |
+
# 2. Load model
|
| 1750 |
+
model = SentenceTransformer("mixedbread-ai/mxbai-embed-xsmall-v1", truncate_dim=dimensions)
|
| 1751 |
+
|
| 1752 |
+
query = 'A man is eating a piece of bread'
|
| 1753 |
+
|
| 1754 |
+
docs = [
|
| 1755 |
+
query,
|
| 1756 |
+
"A man is eating food.",
|
| 1757 |
+
"A man is eating pasta.",
|
| 1758 |
+
"The girl is carrying a baby.",
|
| 1759 |
+
"A man is riding a horse.",
|
| 1760 |
+
]
|
| 1761 |
+
|
| 1762 |
+
|
| 1763 |
+
# 3. Encode
|
| 1764 |
+
embeddings = model.encode(docs)
|
| 1765 |
+
|
| 1766 |
+
similarities = cos_sim(embeddings[0], embeddings[1:])
|
| 1767 |
+
print('similarities:', similarities)
|
| 1768 |
+
```
|
| 1769 |
+
</details>
|
| 1770 |
+
|
| 1771 |
+
<details>
|
| 1772 |
+
<summary> transformers </summary>
|
| 1773 |
+
|
| 1774 |
+
```bash
|
| 1775 |
+
pip install -U transformers
|
| 1776 |
+
```
|
| 1777 |
+
|
| 1778 |
+
```python
|
| 1779 |
+
from typing import Dict
|
| 1780 |
+
|
| 1781 |
+
import torch
|
| 1782 |
+
import numpy as np
|
| 1783 |
+
from transformers import AutoModel, AutoTokenizer
|
| 1784 |
+
from sentence_transformers.util import cos_sim
|
| 1785 |
+
|
| 1786 |
+
def pooling(outputs: torch.Tensor, inputs: Dict) -> np.ndarray:
|
| 1787 |
+
outputs = torch.sum(
|
| 1788 |
+
outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"])
|
| 1789 |
+
return outputs.detach().cpu().numpy()
|
| 1790 |
+
|
| 1791 |
+
# 1. Load model
|
| 1792 |
+
model_id = 'mixedbread-ai/mxbai-embed-xsmall-v1'
|
| 1793 |
+
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
| 1794 |
+
model = AutoModel.from_pretrained(model_id).cuda()
|
| 1795 |
+
|
| 1796 |
+
query = 'A man is eating a piece of bread'
|
| 1797 |
+
|
| 1798 |
+
docs = [
|
| 1799 |
+
query,
|
| 1800 |
+
"A man is eating food.",
|
| 1801 |
+
"A man is eating pasta.",
|
| 1802 |
+
"The girl is carrying a baby.",
|
| 1803 |
+
"A man is riding a horse.",
|
| 1804 |
+
]
|
| 1805 |
+
|
| 1806 |
+
# 2. Encode
|
| 1807 |
+
inputs = tokenizer(docs, padding=True, return_tensors='pt')
|
| 1808 |
+
for k, v in inputs.items():
|
| 1809 |
+
inputs[k] = v.cuda()
|
| 1810 |
+
outputs = model(**inputs).last_hidden_state
|
| 1811 |
+
embeddings = pooling(outputs, inputs)
|
| 1812 |
+
|
| 1813 |
+
# 3. Compute similarity scores
|
| 1814 |
+
similarities = cos_sim(embeddings[0], embeddings[1:])
|
| 1815 |
+
print('similarities:', similarities)
|
| 1816 |
+
```
|
| 1817 |
+
</details>
|
| 1818 |
+
|
| 1819 |
+
<details>
|
| 1820 |
+
<summary>Batched API</summary>
|
| 1821 |
+
|
| 1822 |
+
```bash
|
| 1823 |
+
python -m pip install batched
|
| 1824 |
+
```
|
| 1825 |
+
|
| 1826 |
+
```python
|
| 1827 |
+
import uvicorn
|
| 1828 |
+
import batched
|
| 1829 |
+
from fastapi import FastAPI
|
| 1830 |
+
from fastapi.responses import ORJSONResponse
|
| 1831 |
+
from sentence_transformers import SentenceTransformer
|
| 1832 |
+
from pydantic import BaseModel
|
| 1833 |
+
|
| 1834 |
+
app = FastAPI()
|
| 1835 |
+
|
| 1836 |
+
model = SentenceTransformer('mixedbread-ai/mxbai-embed-xsmall-v1')
|
| 1837 |
+
model.encode = batched.aio.dynamically(model.encode)
|
| 1838 |
+
|
| 1839 |
+
class EmbeddingsRequest(BaseModel):
|
| 1840 |
+
input: str | list[str]
|
| 1841 |
+
|
| 1842 |
+
@app.post("/embeddings")
|
| 1843 |
+
async def embeddings(request: EmbeddingsRequest):
|
| 1844 |
+
return ORJSONResponse({"embeddings": await model.encode(request.input)})
|
| 1845 |
+
|
| 1846 |
+
if __name__ == "__main__":
|
| 1847 |
+
uvicorn.run(app, host="0.0.0.0", port=8000)
|
| 1848 |
+
```
|
| 1849 |
+
</details>
|
| 1850 |
+
|
| 1851 |
+
## Community
|
| 1852 |
+
|
| 1853 |
+
Join our [discord community](https://www.mixedbread.ai/redirects/discord) to share your feedback and thoughts. We're here to help and always happy to discuss the exciting field of machine learning!
|
| 1854 |
+
|
| 1855 |
+
## License
|
| 1856 |
+
|
| 1857 |
+
Apache 2.0
|
| 1858 |
+
|
| 1859 |
+
## Citation
|
| 1860 |
+
|
| 1861 |
+
```bibtex
|
| 1862 |
+
@online{xsmall2024mxbai,
|
| 1863 |
+
title={Every Byte Matters: Introducing mxbai-embed-xsmall-v1},
|
| 1864 |
+
author={Sean Lee and Julius Lipp and Rui Huang and Darius Koenig},
|
| 1865 |
+
year={2024},
|
| 1866 |
+
url={https://www.mixedbread.ai/blog/mxbai-embed-xsmall-v1},
|
| 1867 |
+
}
|
| 1868 |
+
```
|
angle_config.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model_name_or_path": "mixedbread-ai/mxbai-embed-xsmall-v1",
|
| 3 |
+
"max_length": 4096,
|
| 4 |
+
"model_kwargs": {},
|
| 5 |
+
"pooling_strategy": "avg",
|
| 6 |
+
"lora_config_kwargs": null,
|
| 7 |
+
"is_llm": 0,
|
| 8 |
+
"apply_billm": 0,
|
| 9 |
+
"billm_model_class": null,
|
| 10 |
+
"apply_lora": 0,
|
| 11 |
+
"tokenizer_padding_side": null,
|
| 12 |
+
"angle_emb_version": "0.4.12",
|
| 13 |
+
"apply_bfloat16": null
|
| 14 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "mixedbread-ai/mxbai-embed-mini-v1",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"BertModel"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"classifier_dropout": null,
|
| 8 |
+
"gradient_checkpointing": false,
|
| 9 |
+
"hidden_act": "gelu",
|
| 10 |
+
"hidden_dropout_prob": 0.1,
|
| 11 |
+
"hidden_size": 384,
|
| 12 |
+
"initializer_range": 0.02,
|
| 13 |
+
"intermediate_size": 1536,
|
| 14 |
+
"layer_norm_eps": 1e-12,
|
| 15 |
+
"max_position_embeddings": 4096,
|
| 16 |
+
"model_type": "bert",
|
| 17 |
+
"num_attention_heads": 12,
|
| 18 |
+
"num_hidden_layers": 6,
|
| 19 |
+
"pad_token_id": 0,
|
| 20 |
+
"position_embedding_type": "absolute",
|
| 21 |
+
"torch_dtype": "float16",
|
| 22 |
+
"transformers_version": "4.45.1",
|
| 23 |
+
"type_vocab_size": 2,
|
| 24 |
+
"use_cache": false,
|
| 25 |
+
"vocab_size": 30522
|
| 26 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "3.2.0",
|
| 4 |
+
"transformers": "4.45.1",
|
| 5 |
+
"pytorch": "2.4.1"
|
| 6 |
+
},
|
| 7 |
+
"prompts": {},
|
| 8 |
+
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": null
|
| 10 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8a1a58f701e103c02ad3a519c04293f57e037b7a47aaac184466d6dd3e76708e
|
| 3 |
+
size 48190272
|
modules.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
}
|
| 14 |
+
]
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cls_token": {
|
| 3 |
+
"content": "[CLS]",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"mask_token": {
|
| 10 |
+
"content": "[MASK]",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "[PAD]",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
},
|
| 23 |
+
"sep_token": {
|
| 24 |
+
"content": "[SEP]",
|
| 25 |
+
"lstrip": false,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
},
|
| 30 |
+
"unk_token": {
|
| 31 |
+
"content": "[UNK]",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false
|
| 36 |
+
}
|
| 37 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "[PAD]",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"100": {
|
| 12 |
+
"content": "[UNK]",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"101": {
|
| 20 |
+
"content": "[CLS]",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"102": {
|
| 28 |
+
"content": "[SEP]",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"103": {
|
| 36 |
+
"content": "[MASK]",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"clean_up_tokenization_spaces": true,
|
| 45 |
+
"cls_token": "[CLS]",
|
| 46 |
+
"do_basic_tokenize": true,
|
| 47 |
+
"do_lower_case": true,
|
| 48 |
+
"mask_token": "[MASK]",
|
| 49 |
+
"max_length": 128,
|
| 50 |
+
"model_max_length": 512,
|
| 51 |
+
"never_split": null,
|
| 52 |
+
"pad_to_multiple_of": null,
|
| 53 |
+
"pad_token": "[PAD]",
|
| 54 |
+
"pad_token_type_id": 0,
|
| 55 |
+
"padding_side": "right",
|
| 56 |
+
"sep_token": "[SEP]",
|
| 57 |
+
"stride": 0,
|
| 58 |
+
"strip_accents": null,
|
| 59 |
+
"tokenize_chinese_chars": true,
|
| 60 |
+
"tokenizer_class": "BertTokenizer",
|
| 61 |
+
"truncation_side": "right",
|
| 62 |
+
"truncation_strategy": "longest_first",
|
| 63 |
+
"unk_token": "[UNK]"
|
| 64 |
+
}
|
vocab.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|