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  - sentence-transformers
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  - feature-extraction
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  - sentence-similarity
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- - alibi
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  datasets:
11
- - allenai/c4
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  language: en
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  license: apache-2.0
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  model-index:
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- - name: jina-embedding-s-en-v2
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17
  ---
18
  <!-- TODO: add evaluation results here -->
19
  <br><br>
 
6
  - sentence-transformers
7
  - feature-extraction
8
  - sentence-similarity
 
9
  datasets:
10
+ - jinaai/negation-dataset
11
  language: en
12
  license: apache-2.0
13
  model-index:
14
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15
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18
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19
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20
+ name: MTEB AmazonCounterfactualClassification (en)
21
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22
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33
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+ type: mteb/amazon_polarity
35
+ name: MTEB AmazonPolarityClassification
36
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37
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38
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+ type: mteb/amazon_reviews_multi
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51
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2204
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2205
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2206
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2207
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2208
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2209
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2210
+ - type: recall_at_3
2211
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2212
+ - type: recall_at_5
2213
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2214
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2215
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2216
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2217
+ type: mteb/sprintduplicatequestions-pairclassification
2218
+ name: MTEB SprintDuplicateQuestions
2219
+ config: default
2220
+ split: test
2221
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2222
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2223
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2224
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2225
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2226
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2227
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2229
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2230
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2231
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2233
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2234
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2235
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2236
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2237
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2238
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2239
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2240
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2241
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2242
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2243
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2244
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2245
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2246
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2247
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2249
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2250
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2251
+ - type: euclidean_recall
2252
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2253
+ - type: manhattan_accuracy
2254
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2255
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2256
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2257
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2258
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2259
+ - type: manhattan_precision
2260
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2261
+ - type: manhattan_recall
2262
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2263
+ - type: max_accuracy
2264
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2265
+ - type: max_ap
2266
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2267
+ - type: max_f1
2268
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2269
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2270
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2271
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2272
+ type: mteb/stackexchange-clustering
2273
+ name: MTEB StackExchangeClustering
2274
+ config: default
2275
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2276
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2277
+ metrics:
2278
+ - type: v_measure
2279
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2281
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2282
+ dataset:
2283
+ type: mteb/stackexchange-clustering-p2p
2284
+ name: MTEB StackExchangeClusteringP2P
2285
+ config: default
2286
+ split: test
2287
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2288
+ metrics:
2289
+ - type: v_measure
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+ value: 34.421291695525
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+ - task:
2292
+ type: Reranking
2293
+ dataset:
2294
+ type: mteb/stackoverflowdupquestions-reranking
2295
+ name: MTEB StackOverflowDupQuestions
2296
+ config: default
2297
+ split: test
2298
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2299
+ metrics:
2300
+ - type: map
2301
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2302
+ - type: mrr
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+ - task:
2305
+ type: Summarization
2306
+ dataset:
2307
+ type: mteb/summeval
2308
+ name: MTEB SummEval
2309
+ config: default
2310
+ split: test
2311
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2312
+ metrics:
2313
+ - type: cos_sim_pearson
2314
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+ - type: dot_pearson
2318
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+ - type: dot_spearman
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+ - task:
2322
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2323
+ dataset:
2324
+ type: trec-covid
2325
+ name: MTEB TRECCOVID
2326
+ config: default
2327
+ split: test
2328
+ revision: None
2329
+ metrics:
2330
+ - type: map_at_1
2331
+ value: 0.207
2332
+ - type: map_at_10
2333
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2334
+ - type: map_at_100
2335
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2336
+ - type: map_at_1000
2337
+ value: 20.213
2338
+ - type: map_at_3
2339
+ value: 0.585
2340
+ - type: map_at_5
2341
+ value: 0.9039999999999999
2342
+ - type: mrr_at_1
2343
+ value: 78.0
2344
+ - type: mrr_at_10
2345
+ value: 87.4
2346
+ - type: mrr_at_100
2347
+ value: 87.4
2348
+ - type: mrr_at_1000
2349
+ value: 87.4
2350
+ - type: mrr_at_3
2351
+ value: 86.667
2352
+ - type: mrr_at_5
2353
+ value: 87.06700000000001
2354
+ - type: ndcg_at_1
2355
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2356
+ - type: ndcg_at_10
2357
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2358
+ - type: ndcg_at_100
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2360
+ - type: ndcg_at_1000
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2362
+ - type: ndcg_at_3
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2364
+ - type: ndcg_at_5
2365
+ value: 69.271
2366
+ - type: precision_at_1
2367
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2368
+ - type: precision_at_10
2369
+ value: 69.19999999999999
2370
+ - type: precision_at_100
2371
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2372
+ - type: precision_at_1000
2373
+ value: 19.426
2374
+ - type: precision_at_3
2375
+ value: 77.333
2376
+ - type: precision_at_5
2377
+ value: 74.0
2378
+ - type: recall_at_1
2379
+ value: 0.207
2380
+ - type: recall_at_10
2381
+ value: 1.822
2382
+ - type: recall_at_100
2383
+ value: 11.849
2384
+ - type: recall_at_1000
2385
+ value: 40.492
2386
+ - type: recall_at_3
2387
+ value: 0.622
2388
+ - type: recall_at_5
2389
+ value: 0.9809999999999999
2390
+ - task:
2391
+ type: Retrieval
2392
+ dataset:
2393
+ type: webis-touche2020
2394
+ name: MTEB Touche2020
2395
+ config: default
2396
+ split: test
2397
+ revision: None
2398
+ metrics:
2399
+ - type: map_at_1
2400
+ value: 2.001
2401
+ - type: map_at_10
2402
+ value: 10.376000000000001
2403
+ - type: map_at_100
2404
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2405
+ - type: map_at_1000
2406
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2407
+ - type: map_at_3
2408
+ value: 5.335999999999999
2409
+ - type: map_at_5
2410
+ value: 7.374
2411
+ - type: mrr_at_1
2412
+ value: 20.408
2413
+ - type: mrr_at_10
2414
+ value: 38.29
2415
+ - type: mrr_at_100
2416
+ value: 39.33
2417
+ - type: mrr_at_1000
2418
+ value: 39.347
2419
+ - type: mrr_at_3
2420
+ value: 32.993
2421
+ - type: mrr_at_5
2422
+ value: 36.973
2423
+ - type: ndcg_at_1
2424
+ value: 17.347
2425
+ - type: ndcg_at_10
2426
+ value: 23.515
2427
+ - type: ndcg_at_100
2428
+ value: 37.457
2429
+ - type: ndcg_at_1000
2430
+ value: 49.439
2431
+ - type: ndcg_at_3
2432
+ value: 22.762999999999998
2433
+ - type: ndcg_at_5
2434
+ value: 22.622
2435
+ - type: precision_at_1
2436
+ value: 20.408
2437
+ - type: precision_at_10
2438
+ value: 22.448999999999998
2439
+ - type: precision_at_100
2440
+ value: 8.184
2441
+ - type: precision_at_1000
2442
+ value: 1.608
2443
+ - type: precision_at_3
2444
+ value: 25.85
2445
+ - type: precision_at_5
2446
+ value: 25.306
2447
+ - type: recall_at_1
2448
+ value: 2.001
2449
+ - type: recall_at_10
2450
+ value: 17.422
2451
+ - type: recall_at_100
2452
+ value: 51.532999999999994
2453
+ - type: recall_at_1000
2454
+ value: 87.466
2455
+ - type: recall_at_3
2456
+ value: 6.861000000000001
2457
+ - type: recall_at_5
2458
+ value: 10.502
2459
+ - task:
2460
+ type: Classification
2461
+ dataset:
2462
+ type: mteb/toxic_conversations_50k
2463
+ name: MTEB ToxicConversationsClassification
2464
+ config: default
2465
+ split: test
2466
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2467
+ metrics:
2468
+ - type: accuracy
2469
+ value: 71.54419999999999
2470
+ - type: ap
2471
+ value: 14.372170450843907
2472
+ - type: f1
2473
+ value: 54.94420257390529
2474
+ - task:
2475
+ type: Classification
2476
+ dataset:
2477
+ type: mteb/tweet_sentiment_extraction
2478
+ name: MTEB TweetSentimentExtractionClassification
2479
+ config: default
2480
+ split: test
2481
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2482
+ metrics:
2483
+ - type: accuracy
2484
+ value: 59.402942840973395
2485
+ - type: f1
2486
+ value: 59.4166538875571
2487
+ - task:
2488
+ type: Clustering
2489
+ dataset:
2490
+ type: mteb/twentynewsgroups-clustering
2491
+ name: MTEB TwentyNewsgroupsClustering
2492
+ config: default
2493
+ split: test
2494
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2495
+ metrics:
2496
+ - type: v_measure
2497
+ value: 41.569064336457906
2498
+ - task:
2499
+ type: PairClassification
2500
+ dataset:
2501
+ type: mteb/twittersemeval2015-pairclassification
2502
+ name: MTEB TwitterSemEval2015
2503
+ config: default
2504
+ split: test
2505
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2506
+ metrics:
2507
+ - type: cos_sim_accuracy
2508
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2509
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2510
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2511
+ - type: cos_sim_f1
2512
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2514
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2515
+ - type: cos_sim_recall
2516
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2519
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2520
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2521
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2522
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2523
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2524
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2525
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2526
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2527
+ - type: euclidean_accuracy
2528
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2529
+ - type: euclidean_ap
2530
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2531
+ - type: euclidean_f1
2532
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2533
+ - type: euclidean_precision
2534
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2535
+ - type: euclidean_recall
2536
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2537
+ - type: manhattan_accuracy
2538
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2539
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2540
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2541
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2542
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2543
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2544
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2545
+ - type: manhattan_recall
2546
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2547
+ - type: max_accuracy
2548
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2549
+ - type: max_ap
2550
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2551
+ - type: max_f1
2552
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2553
+ - task:
2554
+ type: PairClassification
2555
+ dataset:
2556
+ type: mteb/twitterurlcorpus-pairclassification
2557
+ name: MTEB TwitterURLCorpus
2558
+ config: default
2559
+ split: test
2560
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2561
+ metrics:
2562
+ - type: cos_sim_accuracy
2563
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2564
+ - type: cos_sim_ap
2565
+ value: 86.05430786440826
2566
+ - type: cos_sim_f1
2567
+ value: 78.27759692216631
2568
+ - type: cos_sim_precision
2569
+ value: 75.33466248931929
2570
+ - type: cos_sim_recall
2571
+ value: 81.45980905451185
2572
+ - type: dot_accuracy
2573
+ value: 89.12950673341872
2574
+ - type: dot_ap
2575
+ value: 86.05431161145492
2576
+ - type: dot_f1
2577
+ value: 78.27759692216631
2578
+ - type: dot_precision
2579
+ value: 75.33466248931929
2580
+ - type: dot_recall
2581
+ value: 81.45980905451185
2582
+ - type: euclidean_accuracy
2583
+ value: 89.12756626693057
2584
+ - type: euclidean_ap
2585
+ value: 86.05431303247397
2586
+ - type: euclidean_f1
2587
+ value: 78.27759692216631
2588
+ - type: euclidean_precision
2589
+ value: 75.33466248931929
2590
+ - type: euclidean_recall
2591
+ value: 81.45980905451185
2592
+ - type: manhattan_accuracy
2593
+ value: 89.04994760740482
2594
+ - type: manhattan_ap
2595
+ value: 86.00860610892074
2596
+ - type: manhattan_f1
2597
+ value: 78.1846776005392
2598
+ - type: manhattan_precision
2599
+ value: 76.10438839480975
2600
+ - type: manhattan_recall
2601
+ value: 80.3818909762858
2602
+ - type: max_accuracy
2603
+ value: 89.12950673341872
2604
+ - type: max_ap
2605
+ value: 86.05431303247397
2606
+ - type: max_f1
2607
+ value: 78.27759692216631
2608
  ---
2609
  <!-- TODO: add evaluation results here -->
2610
  <br><br>