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@@ -23,11 +23,11 @@ model-index:
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  revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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  metrics:
25
  - type: accuracy
26
- value: 66.58208955223881
27
  - type: ap
28
- value: 28.455148149555754
29
  - type: f1
30
- value: 59.973775371110385
31
  - task:
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  type: Classification
33
  dataset:
@@ -38,11 +38,11 @@ model-index:
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  revision: e2d317d38cd51312af73b3d32a06d1a08b442046
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  metrics:
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  - type: accuracy
41
- value: 65.09505
42
  - type: ap
43
- value: 61.387245649832614
44
  - type: f1
45
- value: 62.96831291412068
46
  - task:
47
  type: Classification
48
  dataset:
@@ -53,1066 +53,1066 @@ model-index:
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  revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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  metrics:
55
  - type: accuracy
56
- value: 30.633999999999993
57
  - type: f1
58
- value: 29.638828990078647
59
  - task:
60
  type: Retrieval
61
  dataset:
62
- type: BeIR/cqadupstack
63
- name: MTEB CQADupstackAndroidRetrieval
64
  config: default
65
  split: test
66
  revision: None
67
  metrics:
68
  - type: map_at_1
69
- value: 34.027
70
  - type: map_at_10
71
- value: 45.284
72
  - type: map_at_100
73
- value: 46.845
74
  - type: map_at_1000
75
- value: 46.961999999999996
76
  - type: map_at_3
77
- value: 41.831
78
  - type: map_at_5
79
- value: 43.791000000000004
80
  - type: mrr_at_1
81
- value: 41.488
82
  - type: mrr_at_10
83
- value: 51.383
84
  - type: mrr_at_100
85
- value: 52.093
86
  - type: mrr_at_1000
87
- value: 52.132
88
  - type: mrr_at_3
89
- value: 48.927
90
  - type: mrr_at_5
91
- value: 50.601
92
  - type: ndcg_at_1
93
- value: 41.488
94
  - type: ndcg_at_10
95
- value: 51.564
96
  - type: ndcg_at_100
97
- value: 56.806999999999995
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  - type: ndcg_at_1000
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- value: 58.526999999999994
100
  - type: ndcg_at_3
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- value: 46.876
102
  - type: ndcg_at_5
103
- value: 49.198
104
  - type: precision_at_1
105
- value: 41.488
106
  - type: precision_at_10
107
- value: 9.886000000000001
108
  - type: precision_at_100
109
- value: 1.591
110
  - type: precision_at_1000
111
- value: 0.20500000000000002
112
  - type: precision_at_3
113
- value: 22.317999999999998
114
  - type: precision_at_5
115
- value: 16.252
116
  - type: recall_at_1
117
- value: 34.027
118
  - type: recall_at_10
119
- value: 62.668
120
  - type: recall_at_100
121
- value: 84.502
122
  - type: recall_at_1000
123
- value: 95.61800000000001
124
  - type: recall_at_3
125
- value: 49.132999999999996
126
  - type: recall_at_5
127
- value: 55.547000000000004
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - task:
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  type: Retrieval
130
  dataset:
131
  type: BeIR/cqadupstack
132
- name: MTEB CQADupstackEnglishRetrieval
133
  config: default
134
  split: test
135
  revision: None
136
  metrics:
137
  - type: map_at_1
138
- value: 28.871999999999996
139
  - type: map_at_10
140
- value: 38.975
141
  - type: map_at_100
142
- value: 40.186
143
  - type: map_at_1000
144
- value: 40.317
145
  - type: map_at_3
146
- value: 35.942
147
  - type: map_at_5
148
- value: 37.606
149
  - type: mrr_at_1
150
- value: 36.497
151
  - type: mrr_at_10
152
- value: 45.072
153
  - type: mrr_at_100
154
- value: 45.725
155
  - type: mrr_at_1000
156
- value: 45.775
157
  - type: mrr_at_3
158
- value: 42.824
159
  - type: mrr_at_5
160
- value: 44.082
161
  - type: ndcg_at_1
162
- value: 36.497
163
  - type: ndcg_at_10
164
- value: 44.805
165
  - type: ndcg_at_100
166
- value: 49.044
167
  - type: ndcg_at_1000
168
- value: 51.318
169
  - type: ndcg_at_3
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- value: 40.489000000000004
171
  - type: ndcg_at_5
172
- value: 42.399
173
  - type: precision_at_1
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- value: 36.497
175
  - type: precision_at_10
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- value: 8.516
177
  - type: precision_at_100
178
- value: 1.343
179
  - type: precision_at_1000
180
- value: 0.185
181
  - type: precision_at_3
182
- value: 19.66
183
  - type: precision_at_5
184
- value: 13.987
185
  - type: recall_at_1
186
- value: 28.871999999999996
187
  - type: recall_at_10
188
- value: 55.286
189
  - type: recall_at_100
190
- value: 73.041
191
  - type: recall_at_1000
192
- value: 87.66000000000001
193
  - type: recall_at_3
194
- value: 42.498000000000005
195
  - type: recall_at_5
196
- value: 47.827999999999996
197
  - task:
198
  type: Retrieval
199
  dataset:
200
  type: BeIR/cqadupstack
201
- name: MTEB CQADupstackGamingRetrieval
202
  config: default
203
  split: test
204
  revision: None
205
  metrics:
206
  - type: map_at_1
207
- value: 38.635999999999996
208
  - type: map_at_10
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- value: 50.629000000000005
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  - type: map_at_100
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- value: 51.690999999999995
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  - type: map_at_1000
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- value: 51.748000000000005
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  - type: map_at_3
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- value: 47.421
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  - type: map_at_5
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- value: 49.297000000000004
218
  - type: mrr_at_1
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- value: 44.074999999999996
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  - type: mrr_at_10
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- value: 53.93
222
  - type: mrr_at_100
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- value: 54.637
224
  - type: mrr_at_1000
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- value: 54.663
226
  - type: mrr_at_3
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- value: 51.526
228
  - type: mrr_at_5
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- value: 52.949
230
  - type: ndcg_at_1
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- value: 44.074999999999996
232
  - type: ndcg_at_10
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- value: 56.355999999999995
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  - type: ndcg_at_100
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- value: 60.504999999999995
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  - type: ndcg_at_1000
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- value: 61.553999999999995
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  - type: ndcg_at_3
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- value: 51.007000000000005
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  - type: ndcg_at_5
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- value: 53.689
242
  - type: precision_at_1
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- value: 44.074999999999996
244
  - type: precision_at_10
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- value: 9.072
246
  - type: precision_at_100
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- value: 1.208
248
  - type: precision_at_1000
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- value: 0.134
250
  - type: precision_at_3
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- value: 22.8
252
  - type: precision_at_5
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- value: 15.674
254
  - type: recall_at_1
255
- value: 38.635999999999996
256
  - type: recall_at_10
257
- value: 69.88600000000001
258
  - type: recall_at_100
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- value: 87.82600000000001
260
  - type: recall_at_1000
261
- value: 95.06
262
  - type: recall_at_3
263
- value: 55.52
264
  - type: recall_at_5
265
- value: 62.077000000000005
266
  - task:
267
  type: Retrieval
268
  dataset:
269
  type: BeIR/cqadupstack
270
- name: MTEB CQADupstackGisRetrieval
271
  config: default
272
  split: test
273
  revision: None
274
  metrics:
275
  - type: map_at_1
276
- value: 25.418000000000003
277
  - type: map_at_10
278
- value: 33.894999999999996
279
  - type: map_at_100
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- value: 34.876000000000005
281
  - type: map_at_1000
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- value: 34.949000000000005
283
  - type: map_at_3
284
- value: 31.147999999999996
285
  - type: map_at_5
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- value: 32.558
287
  - type: mrr_at_1
288
- value: 27.458
289
  - type: mrr_at_10
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- value: 36.065999999999995
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  - type: mrr_at_100
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- value: 36.924
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  - type: mrr_at_1000
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- value: 36.980000000000004
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  - type: mrr_at_3
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- value: 33.409
297
  - type: mrr_at_5
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- value: 34.878
299
  - type: ndcg_at_1
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- value: 27.458
301
  - type: ndcg_at_10
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- value: 39.064
303
  - type: ndcg_at_100
304
- value: 44.027
305
  - type: ndcg_at_1000
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- value: 46.024
307
  - type: ndcg_at_3
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- value: 33.623
309
  - type: ndcg_at_5
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- value: 36.071
311
  - type: precision_at_1
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- value: 27.458
313
  - type: precision_at_10
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- value: 6.101999999999999
315
  - type: precision_at_100
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- value: 0.905
317
  - type: precision_at_1000
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- value: 0.11199999999999999
319
  - type: precision_at_3
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- value: 14.237
321
  - type: precision_at_5
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- value: 9.989
323
  - type: recall_at_1
324
- value: 25.418000000000003
325
  - type: recall_at_10
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- value: 52.952999999999996
327
  - type: recall_at_100
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- value: 76.19200000000001
329
  - type: recall_at_1000
330
- value: 91.388
331
  - type: recall_at_3
332
- value: 38.271
333
  - type: recall_at_5
334
- value: 44.144
335
  - task:
336
  type: Retrieval
337
  dataset:
338
  type: BeIR/cqadupstack
339
- name: MTEB CQADupstackMathematicaRetrieval
340
  config: default
341
  split: test
342
  revision: None
343
  metrics:
344
  - type: map_at_1
345
- value: 15.977
346
  - type: map_at_10
347
- value: 23.580000000000002
348
  - type: map_at_100
349
- value: 24.779999999999998
350
  - type: map_at_1000
351
- value: 24.897
352
  - type: map_at_3
353
- value: 21.115000000000002
354
  - type: map_at_5
355
- value: 22.339000000000002
356
  - type: mrr_at_1
357
- value: 19.652
358
  - type: mrr_at_10
359
- value: 28.053
360
  - type: mrr_at_100
361
- value: 29.008
362
  - type: mrr_at_1000
363
- value: 29.075
364
  - type: mrr_at_3
365
- value: 25.643
366
  - type: mrr_at_5
367
- value: 26.862000000000002
368
  - type: ndcg_at_1
369
- value: 19.652
370
  - type: ndcg_at_10
371
- value: 28.709
372
  - type: ndcg_at_100
373
- value: 34.5
374
  - type: ndcg_at_1000
375
- value: 37.425999999999995
376
  - type: ndcg_at_3
377
- value: 24.07
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  - type: ndcg_at_5
379
- value: 25.932
380
  - type: precision_at_1
381
- value: 19.652
382
  - type: precision_at_10
383
- value: 5.485
384
  - type: precision_at_100
385
- value: 0.959
386
  - type: precision_at_1000
387
- value: 0.134
388
  - type: precision_at_3
389
- value: 11.816
390
  - type: precision_at_5
391
- value: 8.632
392
  - type: recall_at_1
393
- value: 15.977
394
  - type: recall_at_10
395
- value: 40.127
396
  - type: recall_at_100
397
- value: 65.669
398
  - type: recall_at_1000
399
- value: 86.82600000000001
400
  - type: recall_at_3
401
- value: 26.978
402
  - type: recall_at_5
403
- value: 31.812
404
  - task:
405
  type: Retrieval
406
  dataset:
407
  type: BeIR/cqadupstack
408
- name: MTEB CQADupstackPhysicsRetrieval
409
  config: default
410
  split: test
411
  revision: None
412
  metrics:
413
  - type: map_at_1
414
- value: 27.126
415
  - type: map_at_10
416
- value: 37.077
417
  - type: map_at_100
418
- value: 38.421
419
  - type: map_at_1000
420
- value: 38.541
421
  - type: map_at_3
422
- value: 33.694
423
  - type: map_at_5
424
- value: 35.714
425
  - type: mrr_at_1
426
- value: 33.589999999999996
427
  - type: mrr_at_10
428
- value: 42.784
429
  - type: mrr_at_100
430
- value: 43.635000000000005
431
  - type: mrr_at_1000
432
- value: 43.682
433
  - type: mrr_at_3
434
- value: 39.926
435
  - type: mrr_at_5
436
- value: 41.846
437
  - type: ndcg_at_1
438
- value: 33.589999999999996
439
  - type: ndcg_at_10
440
- value: 43.131
441
  - type: ndcg_at_100
442
- value: 48.788
443
  - type: ndcg_at_1000
444
- value: 50.9
445
  - type: ndcg_at_3
446
- value: 37.736
447
  - type: ndcg_at_5
448
- value: 40.634
449
  - type: precision_at_1
450
- value: 33.589999999999996
451
  - type: precision_at_10
452
- value: 7.979
453
  - type: precision_at_100
454
- value: 1.269
455
  - type: precision_at_1000
456
- value: 0.166
457
  - type: precision_at_3
458
- value: 17.965999999999998
459
  - type: precision_at_5
460
- value: 13.224
461
  - type: recall_at_1
462
- value: 27.126
463
  - type: recall_at_10
464
- value: 55.525000000000006
465
  - type: recall_at_100
466
- value: 79.438
467
  - type: recall_at_1000
468
- value: 93.061
469
  - type: recall_at_3
470
- value: 40.510000000000005
471
  - type: recall_at_5
472
- value: 47.935
473
  - task:
474
  type: Retrieval
475
  dataset:
476
  type: BeIR/cqadupstack
477
- name: MTEB CQADupstackProgrammersRetrieval
478
  config: default
479
  split: test
480
  revision: None
481
  metrics:
482
  - type: map_at_1
483
- value: 26.823000000000004
484
  - type: map_at_10
485
- value: 35.67
486
  - type: map_at_100
487
- value: 36.968
488
  - type: map_at_1000
489
- value: 37.1
490
  - type: map_at_3
491
- value: 33.213
492
  - type: map_at_5
493
- value: 34.407
494
  - type: mrr_at_1
495
- value: 32.877
496
  - type: mrr_at_10
497
- value: 41.109
498
  - type: mrr_at_100
499
- value: 41.984
500
  - type: mrr_at_1000
501
- value: 42.056
502
  - type: mrr_at_3
503
- value: 39.174
504
  - type: mrr_at_5
505
- value: 40.099000000000004
506
  - type: ndcg_at_1
507
- value: 32.877
508
  - type: ndcg_at_10
509
- value: 40.995
510
  - type: ndcg_at_100
511
- value: 46.479
512
  - type: ndcg_at_1000
513
- value: 49.157000000000004
514
  - type: ndcg_at_3
515
- value: 37.052
516
  - type: ndcg_at_5
517
- value: 38.456
518
  - type: precision_at_1
519
- value: 32.877
520
  - type: precision_at_10
521
- value: 7.226000000000001
522
  - type: precision_at_100
523
- value: 1.17
524
  - type: precision_at_1000
525
- value: 0.159
526
  - type: precision_at_3
527
- value: 17.580000000000002
528
  - type: precision_at_5
529
- value: 11.985999999999999
530
  - type: recall_at_1
531
- value: 26.823000000000004
532
  - type: recall_at_10
533
- value: 51.651
534
  - type: recall_at_100
535
- value: 74.80799999999999
536
  - type: recall_at_1000
537
- value: 92.887
538
  - type: recall_at_3
539
- value: 39.928999999999995
540
  - type: recall_at_5
541
- value: 43.998
542
  - task:
543
  type: Retrieval
544
  dataset:
545
  type: BeIR/cqadupstack
546
- name: MTEB CQADupstackRetrieval
547
  config: default
548
  split: test
549
  revision: None
550
  metrics:
551
  - type: map_at_1
552
- value: 25.948
553
  - type: map_at_10
554
- value: 34.89458333333334
555
  - type: map_at_100
556
- value: 36.0865
557
  - type: map_at_1000
558
- value: 36.20658333333333
559
  - type: map_at_3
560
- value: 32.09133333333334
561
  - type: map_at_5
562
- value: 33.5975
563
  - type: mrr_at_1
564
- value: 30.68441666666667
565
  - type: mrr_at_10
566
- value: 39.060916666666664
567
  - type: mrr_at_100
568
- value: 39.88725
569
  - type: mrr_at_1000
570
- value: 39.946916666666674
571
  - type: mrr_at_3
572
- value: 36.66891666666666
573
  - type: mrr_at_5
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- value: 38.00875
575
  - type: ndcg_at_1
576
- value: 30.68441666666667
577
  - type: ndcg_at_10
578
- value: 40.24416666666667
579
  - type: ndcg_at_100
580
- value: 45.29891666666666
581
  - type: ndcg_at_1000
582
- value: 47.62883333333334
583
  - type: ndcg_at_3
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- value: 35.48391666666666
585
  - type: ndcg_at_5
586
- value: 37.613
587
  - type: precision_at_1
588
- value: 30.68441666666667
589
  - type: precision_at_10
590
- value: 7.098750000000001
591
  - type: precision_at_100
592
- value: 1.1458333333333333
593
  - type: precision_at_1000
594
- value: 0.15400000000000003
595
  - type: precision_at_3
596
- value: 16.351583333333334
597
  - type: precision_at_5
598
- value: 11.620666666666668
599
  - type: recall_at_1
600
- value: 25.948
601
  - type: recall_at_10
602
- value: 51.91491666666666
603
  - type: recall_at_100
604
- value: 74.13791666666665
605
  - type: recall_at_1000
606
- value: 90.30833333333332
607
  - type: recall_at_3
608
- value: 38.521
609
  - type: recall_at_5
610
- value: 44.02825
611
  - task:
612
  type: Retrieval
613
  dataset:
614
  type: BeIR/cqadupstack
615
- name: MTEB CQADupstackStatsRetrieval
616
  config: default
617
  split: test
618
  revision: None
619
  metrics:
620
  - type: map_at_1
621
- value: 22.831000000000003
622
  - type: map_at_10
623
- value: 29.503
624
  - type: map_at_100
625
- value: 30.358
626
  - type: map_at_1000
627
- value: 30.462
628
  - type: map_at_3
629
- value: 27.344
630
  - type: map_at_5
631
- value: 28.408
632
  - type: mrr_at_1
633
- value: 25.153
634
  - type: mrr_at_10
635
- value: 31.726
636
  - type: mrr_at_100
637
- value: 32.491
638
  - type: mrr_at_1000
639
- value: 32.568999999999996
640
  - type: mrr_at_3
641
- value: 29.601
642
  - type: mrr_at_5
643
- value: 30.667
644
  - type: ndcg_at_1
645
- value: 25.153
646
  - type: ndcg_at_10
647
- value: 33.629
648
  - type: ndcg_at_100
649
- value: 37.979
650
  - type: ndcg_at_1000
651
- value: 40.63
652
  - type: ndcg_at_3
653
- value: 29.455
654
  - type: ndcg_at_5
655
- value: 31.072
656
  - type: precision_at_1
657
- value: 25.153
658
  - type: precision_at_10
659
- value: 5.367999999999999
660
  - type: precision_at_100
661
- value: 0.8160000000000001
662
  - type: precision_at_1000
663
- value: 0.11299999999999999
664
  - type: precision_at_3
665
- value: 12.679000000000002
666
  - type: precision_at_5
667
- value: 8.773
668
  - type: recall_at_1
669
- value: 22.831000000000003
670
  - type: recall_at_10
671
- value: 44.194
672
  - type: recall_at_100
673
- value: 64.11
674
  - type: recall_at_1000
675
- value: 83.588
676
  - type: recall_at_3
677
- value: 32.402
678
  - type: recall_at_5
679
- value: 36.39
680
  - task:
681
  type: Retrieval
682
  dataset:
683
  type: BeIR/cqadupstack
684
- name: MTEB CQADupstackTexRetrieval
685
  config: default
686
  split: test
687
  revision: None
688
  metrics:
689
  - type: map_at_1
690
- value: 17.53
691
  - type: map_at_10
692
- value: 24.656
693
  - type: map_at_100
694
- value: 25.751
695
  - type: map_at_1000
696
- value: 25.881
697
  - type: map_at_3
698
- value: 22.387999999999998
699
  - type: map_at_5
700
- value: 23.536
701
  - type: mrr_at_1
702
- value: 21.266
703
  - type: mrr_at_10
704
- value: 28.355000000000004
705
  - type: mrr_at_100
706
- value: 29.281000000000002
707
  - type: mrr_at_1000
708
- value: 29.359
709
  - type: mrr_at_3
710
- value: 26.222
711
  - type: mrr_at_5
712
- value: 27.361
713
  - type: ndcg_at_1
714
- value: 21.266
715
  - type: ndcg_at_10
716
- value: 29.195999999999998
717
  - type: ndcg_at_100
718
- value: 34.477999999999994
719
  - type: ndcg_at_1000
720
- value: 37.509
721
  - type: ndcg_at_3
722
- value: 25.081999999999997
723
  - type: ndcg_at_5
724
- value: 26.781
725
  - type: precision_at_1
726
- value: 21.266
727
  - type: precision_at_10
728
- value: 5.306
729
  - type: precision_at_100
730
- value: 0.936
731
  - type: precision_at_1000
732
- value: 0.13799999999999998
733
  - type: precision_at_3
734
- value: 11.769
735
  - type: precision_at_5
736
- value: 8.437999999999999
737
  - type: recall_at_1
738
- value: 17.53
739
  - type: recall_at_10
740
- value: 39.205
741
  - type: recall_at_100
742
- value: 62.936
743
  - type: recall_at_1000
744
- value: 84.576
745
  - type: recall_at_3
746
- value: 27.624
747
  - type: recall_at_5
748
- value: 32.025999999999996
749
  - task:
750
  type: Retrieval
751
  dataset:
752
  type: BeIR/cqadupstack
753
- name: MTEB CQADupstackUnixRetrieval
754
  config: default
755
  split: test
756
  revision: None
757
  metrics:
758
  - type: map_at_1
759
- value: 27.221
760
  - type: map_at_10
761
- value: 35.442
762
  - type: map_at_100
763
- value: 36.602000000000004
764
  - type: map_at_1000
765
- value: 36.714999999999996
766
  - type: map_at_3
767
- value: 32.667
768
  - type: map_at_5
769
- value: 34.07
770
  - type: mrr_at_1
771
- value: 32.183
772
  - type: mrr_at_10
773
- value: 39.811
774
  - type: mrr_at_100
775
- value: 40.666999999999994
776
  - type: mrr_at_1000
777
- value: 40.739
778
  - type: mrr_at_3
779
- value: 37.438
780
  - type: mrr_at_5
781
- value: 38.692
782
  - type: ndcg_at_1
783
- value: 32.183
784
  - type: ndcg_at_10
785
- value: 40.652
786
  - type: ndcg_at_100
787
- value: 45.749
788
  - type: ndcg_at_1000
789
- value: 48.284
790
  - type: ndcg_at_3
791
- value: 35.716
792
  - type: ndcg_at_5
793
- value: 37.767
794
  - type: precision_at_1
795
- value: 32.183
796
  - type: precision_at_10
797
- value: 6.81
798
  - type: precision_at_100
799
- value: 1.049
800
  - type: precision_at_1000
801
- value: 0.13799999999999998
802
  - type: precision_at_3
803
- value: 16.169
804
  - type: precision_at_5
805
- value: 11.213
806
  - type: recall_at_1
807
- value: 27.221
808
  - type: recall_at_10
809
- value: 52.364
810
  - type: recall_at_100
811
- value: 74.28399999999999
812
  - type: recall_at_1000
813
- value: 92.01899999999999
814
  - type: recall_at_3
815
- value: 38.391999999999996
816
  - type: recall_at_5
817
- value: 43.934
818
  - task:
819
  type: Retrieval
820
  dataset:
821
  type: BeIR/cqadupstack
822
- name: MTEB CQADupstackWebmastersRetrieval
823
  config: default
824
  split: test
825
  revision: None
826
  metrics:
827
  - type: map_at_1
828
- value: 26.113999999999997
829
  - type: map_at_10
830
- value: 35.785
831
  - type: map_at_100
832
- value: 37.297999999999995
833
  - type: map_at_1000
834
- value: 37.545
835
  - type: map_at_3
836
- value: 32.58
837
  - type: map_at_5
838
- value: 34.39
839
  - type: mrr_at_1
840
- value: 31.423000000000002
841
  - type: mrr_at_10
842
- value: 40.21
843
  - type: mrr_at_100
844
- value: 41.03
845
  - type: mrr_at_1000
846
- value: 41.095
847
  - type: mrr_at_3
848
- value: 37.549
849
  - type: mrr_at_5
850
- value: 39.032
851
  - type: ndcg_at_1
852
- value: 31.423000000000002
853
  - type: ndcg_at_10
854
- value: 42.01
855
  - type: ndcg_at_100
856
- value: 47.098
857
  - type: ndcg_at_1000
858
- value: 49.577
859
  - type: ndcg_at_3
860
- value: 36.862
861
  - type: ndcg_at_5
862
- value: 39.312000000000005
863
  - type: precision_at_1
864
- value: 31.423000000000002
865
  - type: precision_at_10
866
- value: 8.241
867
  - type: precision_at_100
868
- value: 1.644
869
  - type: precision_at_1000
870
- value: 0.246
871
  - type: precision_at_3
872
- value: 17.457
873
  - type: precision_at_5
874
- value: 12.925
875
  - type: recall_at_1
876
- value: 26.113999999999997
877
  - type: recall_at_10
878
- value: 54.071000000000005
879
  - type: recall_at_100
880
- value: 77.102
881
  - type: recall_at_1000
882
- value: 92.453
883
  - type: recall_at_3
884
- value: 39.306999999999995
885
  - type: recall_at_5
886
- value: 45.778
887
  - task:
888
  type: Retrieval
889
  dataset:
890
  type: BeIR/cqadupstack
891
- name: MTEB CQADupstackWordpressRetrieval
892
  config: default
893
  split: test
894
  revision: None
895
  metrics:
896
  - type: map_at_1
897
- value: 20.801
898
  - type: map_at_10
899
- value: 28.238999999999997
900
  - type: map_at_100
901
- value: 29.262
902
  - type: map_at_1000
903
- value: 29.362
904
  - type: map_at_3
905
- value: 25.752999999999997
906
  - type: map_at_5
907
- value: 27.054000000000002
908
  - type: mrr_at_1
909
- value: 22.551
910
  - type: mrr_at_10
911
- value: 30.232
912
  - type: mrr_at_100
913
- value: 31.172
914
  - type: mrr_at_1000
915
- value: 31.238
916
  - type: mrr_at_3
917
- value: 27.788
918
  - type: mrr_at_5
919
- value: 29.036
920
  - type: ndcg_at_1
921
- value: 22.551
922
  - type: ndcg_at_10
923
- value: 32.818999999999996
924
  - type: ndcg_at_100
925
- value: 38.133
926
  - type: ndcg_at_1000
927
- value: 40.64
928
  - type: ndcg_at_3
929
- value: 27.839000000000002
930
  - type: ndcg_at_5
931
- value: 30.044999999999998
932
  - type: precision_at_1
933
- value: 22.551
934
  - type: precision_at_10
935
- value: 5.194
936
  - type: precision_at_100
937
- value: 0.86
938
  - type: precision_at_1000
939
- value: 0.11800000000000001
940
  - type: precision_at_3
941
- value: 11.768
942
  - type: precision_at_5
943
- value: 8.355
944
  - type: recall_at_1
945
- value: 20.801
946
  - type: recall_at_10
947
- value: 45.049
948
  - type: recall_at_100
949
- value: 69.747
950
  - type: recall_at_1000
951
- value: 88.564
952
  - type: recall_at_3
953
- value: 31.688
954
  - type: recall_at_5
955
- value: 36.870000000000005
956
  - task:
957
  type: Retrieval
958
  dataset:
959
- type: arguana
960
- name: MTEB ArguAna
961
  config: default
962
  split: test
963
  revision: None
964
  metrics:
965
  - type: map_at_1
966
- value: 25.889
967
  - type: map_at_10
968
- value: 40.604
969
  - type: map_at_100
970
- value: 41.697
971
  - type: map_at_1000
972
- value: 41.705999999999996
973
  - type: map_at_3
974
- value: 35.217999999999996
975
  - type: map_at_5
976
- value: 38.326
977
  - type: mrr_at_1
978
- value: 26.245
979
  - type: mrr_at_10
980
- value: 40.736
981
  - type: mrr_at_100
982
- value: 41.829
983
  - type: mrr_at_1000
984
- value: 41.837999999999994
985
  - type: mrr_at_3
986
- value: 35.349000000000004
987
  - type: mrr_at_5
988
- value: 38.425
989
  - type: ndcg_at_1
990
- value: 25.889
991
  - type: ndcg_at_10
992
- value: 49.347
993
  - type: ndcg_at_100
994
- value: 53.956
995
  - type: ndcg_at_1000
996
- value: 54.2
997
  - type: ndcg_at_3
998
- value: 38.282
999
  - type: ndcg_at_5
1000
- value: 43.895
1001
  - type: precision_at_1
1002
- value: 25.889
1003
  - type: precision_at_10
1004
- value: 7.752000000000001
1005
  - type: precision_at_100
1006
- value: 0.976
1007
  - type: precision_at_1000
1008
- value: 0.1
1009
  - type: precision_at_3
1010
- value: 15.717999999999998
1011
  - type: precision_at_5
1012
- value: 12.162
1013
  - type: recall_at_1
1014
- value: 25.889
1015
  - type: recall_at_10
1016
- value: 77.525
1017
  - type: recall_at_100
1018
- value: 97.58200000000001
1019
  - type: recall_at_1000
1020
- value: 99.502
1021
  - type: recall_at_3
1022
- value: 47.155
1023
  - type: recall_at_5
1024
- value: 60.81100000000001
1025
- - task:
1026
- type: Clustering
1027
- dataset:
1028
- type: mteb/arxiv-clustering-p2p
1029
- name: MTEB ArxivClusteringP2P
1030
- config: default
1031
- split: test
1032
- revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
1033
- metrics:
1034
- - type: v_measure
1035
- value: 39.2179862062943
1036
- - task:
1037
- type: Clustering
1038
- dataset:
1039
- type: mteb/arxiv-clustering-s2s
1040
- name: MTEB ArxivClusteringS2S
1041
- config: default
1042
- split: test
1043
- revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
1044
- metrics:
1045
- - type: v_measure
1046
- value: 29.87826673088078
1047
- - task:
1048
- type: Reranking
1049
- dataset:
1050
- type: mteb/askubuntudupquestions-reranking
1051
- name: MTEB AskUbuntuDupQuestions
1052
- config: default
1053
- split: test
1054
- revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
1055
- metrics:
1056
- - type: map
1057
- value: 62.72401299412015
1058
- - type: mrr
1059
- value: 75.45167743921206
1060
- - task:
1061
- type: STS
1062
- dataset:
1063
- type: mteb/biosses-sts
1064
- name: MTEB BIOSSES
1065
- config: default
1066
- split: test
1067
- revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
1068
- metrics:
1069
- - type: cos_sim_pearson
1070
- value: 85.96510928112639
1071
- - type: cos_sim_spearman
1072
- value: 82.64224450538681
1073
- - type: euclidean_pearson
1074
- value: 52.03458755006108
1075
- - type: euclidean_spearman
1076
- value: 52.83192670285616
1077
- - type: manhattan_pearson
1078
- value: 52.14561955040935
1079
- - type: manhattan_spearman
1080
- value: 52.9584356095438
1081
- - task:
1082
- type: Classification
1083
- dataset:
1084
- type: mteb/banking77
1085
- name: MTEB Banking77Classification
1086
- config: default
1087
- split: test
1088
- revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
1089
- metrics:
1090
- - type: accuracy
1091
- value: 84.11363636363636
1092
- - type: f1
1093
- value: 84.01098114920124
1094
- - task:
1095
- type: Clustering
1096
- dataset:
1097
- type: mteb/biorxiv-clustering-p2p
1098
- name: MTEB BiorxivClusteringP2P
1099
- config: default
1100
- split: test
1101
- revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
1102
- metrics:
1103
- - type: v_measure
1104
- value: 32.991971466919026
1105
- - task:
1106
- type: Clustering
1107
- dataset:
1108
- type: mteb/biorxiv-clustering-s2s
1109
- name: MTEB BiorxivClusteringS2S
1110
- config: default
1111
- split: test
1112
- revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
1113
- metrics:
1114
- - type: v_measure
1115
- value: 26.48807922559519
1116
  - task:
1117
  type: Retrieval
1118
  dataset:
@@ -1123,65 +1123,65 @@ model-index:
1123
  revision: None
1124
  metrics:
1125
  - type: map_at_1
1126
- value: 8.014000000000001
1127
  - type: map_at_10
1128
- value: 14.149999999999999
1129
  - type: map_at_100
1130
- value: 15.539
1131
  - type: map_at_1000
1132
- value: 15.711
1133
  - type: map_at_3
1134
- value: 11.913
1135
  - type: map_at_5
1136
- value: 12.982
1137
  - type: mrr_at_1
1138
- value: 18.046
1139
  - type: mrr_at_10
1140
- value: 28.224
1141
  - type: mrr_at_100
1142
- value: 29.293000000000003
1143
  - type: mrr_at_1000
1144
- value: 29.348999999999997
1145
  - type: mrr_at_3
1146
- value: 25.179000000000002
1147
  - type: mrr_at_5
1148
- value: 26.827
1149
  - type: ndcg_at_1
1150
- value: 18.046
1151
  - type: ndcg_at_10
1152
- value: 20.784
1153
  - type: ndcg_at_100
1154
- value: 26.939999999999998
1155
  - type: ndcg_at_1000
1156
- value: 30.453999999999997
1157
  - type: ndcg_at_3
1158
- value: 16.694
1159
  - type: ndcg_at_5
1160
- value: 18.049
1161
  - type: precision_at_1
1162
- value: 18.046
1163
  - type: precision_at_10
1164
- value: 6.5280000000000005
1165
  - type: precision_at_100
1166
- value: 1.2959999999999998
1167
  - type: precision_at_1000
1168
- value: 0.19499999999999998
1169
  - type: precision_at_3
1170
- value: 12.465
1171
  - type: precision_at_5
1172
- value: 9.511
1173
  - type: recall_at_1
1174
- value: 8.014000000000001
1175
  - type: recall_at_10
1176
- value: 26.021
1177
  - type: recall_at_100
1178
- value: 47.692
1179
  - type: recall_at_1000
1180
- value: 67.63
1181
  - type: recall_at_3
1182
- value: 16.122
1183
  - type: recall_at_5
1184
- value: 19.817
1185
  - task:
1186
  type: Retrieval
1187
  dataset:
@@ -1192,65 +1192,65 @@ model-index:
1192
  revision: None
1193
  metrics:
1194
  - type: map_at_1
1195
- value: 7.396
1196
  - type: map_at_10
1197
- value: 14.543000000000001
1198
  - type: map_at_100
1199
- value: 19.235
1200
  - type: map_at_1000
1201
- value: 20.384
1202
  - type: map_at_3
1203
- value: 10.886
1204
  - type: map_at_5
1205
- value: 12.61
1206
  - type: mrr_at_1
1207
- value: 55.50000000000001
1208
  - type: mrr_at_10
1209
- value: 63.731
1210
  - type: mrr_at_100
1211
- value: 64.256
1212
  - type: mrr_at_1000
1213
- value: 64.27000000000001
1214
  - type: mrr_at_3
1215
- value: 61.583
1216
  - type: mrr_at_5
1217
- value: 62.92100000000001
1218
  - type: ndcg_at_1
1219
- value: 43.375
1220
  - type: ndcg_at_10
1221
- value: 31.352000000000004
1222
  - type: ndcg_at_100
1223
- value: 34.717999999999996
1224
  - type: ndcg_at_1000
1225
- value: 41.959
1226
  - type: ndcg_at_3
1227
- value: 35.319
1228
  - type: ndcg_at_5
1229
- value: 33.222
1230
  - type: precision_at_1
1231
- value: 55.50000000000001
1232
  - type: precision_at_10
1233
- value: 24.15
1234
  - type: precision_at_100
1235
- value: 7.42
1236
  - type: precision_at_1000
1237
- value: 1.66
1238
  - type: precision_at_3
1239
- value: 37.917
1240
  - type: precision_at_5
1241
- value: 31.900000000000002
1242
  - type: recall_at_1
1243
- value: 7.396
1244
  - type: recall_at_10
1245
- value: 19.686999999999998
1246
  - type: recall_at_100
1247
- value: 40.465
1248
  - type: recall_at_1000
1249
- value: 63.79899999999999
1250
  - type: recall_at_3
1251
- value: 12.124
1252
  - type: recall_at_5
1253
- value: 15.28
1254
  - task:
1255
  type: Classification
1256
  dataset:
@@ -1261,9 +1261,9 @@ model-index:
1261
  revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
1262
  metrics:
1263
  - type: accuracy
1264
- value: 41.33
1265
  - type: f1
1266
- value: 37.682972473685496
1267
  - task:
1268
  type: Retrieval
1269
  dataset:
@@ -1274,65 +1274,65 @@ model-index:
1274
  revision: None
1275
  metrics:
1276
  - type: map_at_1
1277
- value: 49.019
1278
  - type: map_at_10
1279
- value: 61.219
1280
  - type: map_at_100
1281
- value: 61.753
1282
  - type: map_at_1000
1283
- value: 61.771
1284
  - type: map_at_3
1285
- value: 58.952000000000005
1286
  - type: map_at_5
1287
- value: 60.239
1288
  - type: mrr_at_1
1289
- value: 53
1290
  - type: mrr_at_10
1291
- value: 65.678
1292
  - type: mrr_at_100
1293
- value: 66.147
1294
  - type: mrr_at_1000
1295
- value: 66.155
1296
  - type: mrr_at_3
1297
- value: 63.495999999999995
1298
  - type: mrr_at_5
1299
- value: 64.75800000000001
1300
  - type: ndcg_at_1
1301
- value: 53
1302
  - type: ndcg_at_10
1303
- value: 67.587
1304
  - type: ndcg_at_100
1305
- value: 69.877
1306
  - type: ndcg_at_1000
1307
- value: 70.25200000000001
1308
  - type: ndcg_at_3
1309
- value: 63.174
1310
  - type: ndcg_at_5
1311
- value: 65.351
1312
  - type: precision_at_1
1313
- value: 53
1314
  - type: precision_at_10
1315
- value: 9.067
1316
  - type: precision_at_100
1317
- value: 1.026
1318
  - type: precision_at_1000
1319
  value: 0.107
1320
  - type: precision_at_3
1321
- value: 25.728
1322
  - type: precision_at_5
1323
- value: 16.637
1324
  - type: recall_at_1
1325
- value: 49.019
1326
  - type: recall_at_10
1327
- value: 82.962
1328
  - type: recall_at_100
1329
- value: 92.917
1330
  - type: recall_at_1000
1331
- value: 95.511
1332
  - type: recall_at_3
1333
- value: 70.838
1334
  - type: recall_at_5
1335
- value: 76.201
1336
  - task:
1337
  type: Retrieval
1338
  dataset:
@@ -1343,65 +1343,65 @@ model-index:
1343
  revision: None
1344
  metrics:
1345
  - type: map_at_1
1346
- value: 16.714000000000002
1347
  - type: map_at_10
1348
- value: 28.041
1349
  - type: map_at_100
1350
- value: 29.75
1351
  - type: map_at_1000
1352
- value: 29.944
1353
  - type: map_at_3
1354
- value: 23.884
1355
  - type: map_at_5
1356
- value: 26.468000000000004
1357
  - type: mrr_at_1
1358
- value: 33.796
1359
  - type: mrr_at_10
1360
- value: 42.757
1361
  - type: mrr_at_100
1362
- value: 43.705
1363
  - type: mrr_at_1000
1364
- value: 43.751
1365
  - type: mrr_at_3
1366
- value: 40.406
1367
  - type: mrr_at_5
1368
- value: 41.88
1369
  - type: ndcg_at_1
1370
- value: 33.796
1371
  - type: ndcg_at_10
1372
- value: 35.482
1373
  - type: ndcg_at_100
1374
- value: 42.44
1375
  - type: ndcg_at_1000
1376
- value: 45.903
1377
  - type: ndcg_at_3
1378
- value: 31.922
1379
  - type: ndcg_at_5
1380
- value: 33.516
1381
  - type: precision_at_1
1382
- value: 33.796
1383
  - type: precision_at_10
1384
- value: 10.108
1385
  - type: precision_at_100
1386
- value: 1.735
1387
  - type: precision_at_1000
1388
- value: 0.23500000000000001
1389
  - type: precision_at_3
1390
- value: 21.759
1391
  - type: precision_at_5
1392
- value: 16.605
1393
  - type: recall_at_1
1394
- value: 16.714000000000002
1395
  - type: recall_at_10
1396
- value: 42.38
1397
  - type: recall_at_100
1398
- value: 68.84700000000001
1399
  - type: recall_at_1000
1400
- value: 90.036
1401
  - type: recall_at_3
1402
- value: 28.776000000000003
1403
  - type: recall_at_5
1404
- value: 35.606
1405
  - task:
1406
  type: Retrieval
1407
  dataset:
@@ -1412,65 +1412,65 @@ model-index:
1412
  revision: None
1413
  metrics:
1414
  - type: map_at_1
1415
- value: 29.534
1416
  - type: map_at_10
1417
- value: 40.857
1418
  - type: map_at_100
1419
- value: 41.715999999999994
1420
  - type: map_at_1000
1421
- value: 41.795
1422
  - type: map_at_3
1423
- value: 38.415
1424
  - type: map_at_5
1425
- value: 39.833
1426
  - type: mrr_at_1
1427
- value: 59.068
1428
  - type: mrr_at_10
1429
- value: 66.034
1430
  - type: mrr_at_100
1431
- value: 66.479
1432
  - type: mrr_at_1000
1433
- value: 66.50399999999999
1434
  - type: mrr_at_3
1435
- value: 64.38000000000001
1436
  - type: mrr_at_5
1437
- value: 65.40599999999999
1438
  - type: ndcg_at_1
1439
- value: 59.068
1440
  - type: ndcg_at_10
1441
- value: 49.638
1442
  - type: ndcg_at_100
1443
- value: 53.093999999999994
1444
  - type: ndcg_at_1000
1445
- value: 54.813
1446
  - type: ndcg_at_3
1447
- value: 45.537
1448
  - type: ndcg_at_5
1449
- value: 47.671
1450
  - type: precision_at_1
1451
- value: 59.068
1452
  - type: precision_at_10
1453
- value: 10.313
1454
  - type: precision_at_100
1455
- value: 1.304
1456
  - type: precision_at_1000
1457
- value: 0.153
1458
  - type: precision_at_3
1459
- value: 28.278
1460
  - type: precision_at_5
1461
- value: 18.658
1462
  - type: recall_at_1
1463
- value: 29.534
1464
  - type: recall_at_10
1465
- value: 51.56699999999999
1466
  - type: recall_at_100
1467
- value: 65.199
1468
  - type: recall_at_1000
1469
- value: 76.678
1470
  - type: recall_at_3
1471
- value: 42.417
1472
  - type: recall_at_5
1473
- value: 46.644000000000005
1474
  - task:
1475
  type: Classification
1476
  dataset:
@@ -1481,11 +1481,11 @@ model-index:
1481
  revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
1482
  metrics:
1483
  - type: accuracy
1484
- value: 65.74719999999999
1485
  - type: ap
1486
- value: 60.57322504947344
1487
  - type: f1
1488
- value: 65.37875006542282
1489
  - task:
1490
  type: Retrieval
1491
  dataset:
@@ -1496,65 +1496,65 @@ model-index:
1496
  revision: None
1497
  metrics:
1498
  - type: map_at_1
1499
- value: 15.695999999999998
1500
  - type: map_at_10
1501
- value: 26.661
1502
  - type: map_at_100
1503
- value: 27.982000000000003
1504
  - type: map_at_1000
1505
- value: 28.049000000000003
1506
  - type: map_at_3
1507
- value: 23.057
1508
  - type: map_at_5
1509
- value: 25.079
1510
  - type: mrr_at_1
1511
- value: 16.16
1512
  - type: mrr_at_10
1513
- value: 27.150999999999996
1514
  - type: mrr_at_100
1515
- value: 28.423
1516
  - type: mrr_at_1000
1517
- value: 28.483999999999998
1518
  - type: mrr_at_3
1519
- value: 23.577
1520
  - type: mrr_at_5
1521
- value: 25.585
1522
  - type: ndcg_at_1
1523
- value: 16.16
1524
  - type: ndcg_at_10
1525
- value: 33.017
1526
  - type: ndcg_at_100
1527
- value: 39.582
1528
  - type: ndcg_at_1000
1529
- value: 41.28
1530
  - type: ndcg_at_3
1531
- value: 25.607000000000003
1532
  - type: ndcg_at_5
1533
- value: 29.214000000000002
1534
  - type: precision_at_1
1535
- value: 16.16
1536
  - type: precision_at_10
1537
- value: 5.506
1538
  - type: precision_at_100
1539
- value: 0.882
1540
  - type: precision_at_1000
1541
  value: 0.10300000000000001
1542
  - type: precision_at_3
1543
- value: 11.199
1544
  - type: precision_at_5
1545
- value: 8.55
1546
  - type: recall_at_1
1547
- value: 15.695999999999998
1548
  - type: recall_at_10
1549
- value: 52.736000000000004
1550
  - type: recall_at_100
1551
- value: 83.523
1552
  - type: recall_at_1000
1553
- value: 96.588
1554
  - type: recall_at_3
1555
- value: 32.484
1556
  - type: recall_at_5
1557
- value: 41.117
1558
  - task:
1559
  type: Classification
1560
  dataset:
@@ -1565,9 +1565,9 @@ model-index:
1565
  revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
1566
  metrics:
1567
  - type: accuracy
1568
- value: 91.71682626538988
1569
  - type: f1
1570
- value: 91.60647677401211
1571
  - task:
1572
  type: Classification
1573
  dataset:
@@ -1578,9 +1578,9 @@ model-index:
1578
  revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
1579
  metrics:
1580
  - type: accuracy
1581
- value: 74.94756041951665
1582
  - type: f1
1583
- value: 57.26936028487369
1584
  - task:
1585
  type: Classification
1586
  dataset:
@@ -1591,9 +1591,9 @@ model-index:
1591
  revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
1592
  metrics:
1593
  - type: accuracy
1594
- value: 71.43241425689307
1595
  - type: f1
1596
- value: 68.80370629448252
1597
  - task:
1598
  type: Classification
1599
  dataset:
@@ -1604,9 +1604,9 @@ model-index:
1604
  revision: 7d571f92784cd94a019292a1f45445077d0ef634
1605
  metrics:
1606
  - type: accuracy
1607
- value: 77.04774714189642
1608
  - type: f1
1609
- value: 76.93545888412446
1610
  - task:
1611
  type: Clustering
1612
  dataset:
@@ -1617,7 +1617,7 @@ model-index:
1617
  revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
1618
  metrics:
1619
  - type: v_measure
1620
- value: 30.009784989313765
1621
  - task:
1622
  type: Clustering
1623
  dataset:
@@ -1628,7 +1628,7 @@ model-index:
1628
  revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
1629
  metrics:
1630
  - type: v_measure
1631
- value: 25.568442512328872
1632
  - task:
1633
  type: Reranking
1634
  dataset:
@@ -1639,9 +1639,9 @@ model-index:
1639
  revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
1640
  metrics:
1641
  - type: map
1642
- value: 31.013959341949697
1643
  - type: mrr
1644
- value: 31.998487836684575
1645
  - task:
1646
  type: Retrieval
1647
  dataset:
@@ -1652,65 +1652,65 @@ model-index:
1652
  revision: None
1653
  metrics:
1654
  - type: map_at_1
1655
- value: 4.316
1656
  - type: map_at_10
1657
- value: 10.287
1658
  - type: map_at_100
1659
- value: 12.817
1660
  - type: map_at_1000
1661
- value: 14.141
1662
  - type: map_at_3
1663
- value: 7.728
1664
  - type: map_at_5
1665
- value: 8.876000000000001
1666
  - type: mrr_at_1
1667
- value: 39.628
1668
  - type: mrr_at_10
1669
- value: 48.423
1670
  - type: mrr_at_100
1671
- value: 49.153999999999996
1672
  - type: mrr_at_1000
1673
- value: 49.198
1674
  - type: mrr_at_3
1675
- value: 45.666000000000004
1676
  - type: mrr_at_5
1677
- value: 47.477000000000004
1678
  - type: ndcg_at_1
1679
- value: 36.533
1680
  - type: ndcg_at_10
1681
- value: 29.304000000000002
1682
  - type: ndcg_at_100
1683
- value: 27.078000000000003
1684
  - type: ndcg_at_1000
1685
- value: 36.221
1686
  - type: ndcg_at_3
1687
- value: 33.256
1688
  - type: ndcg_at_5
1689
- value: 31.465
1690
  - type: precision_at_1
1691
- value: 39.009
1692
  - type: precision_at_10
1693
- value: 22.043
1694
  - type: precision_at_100
1695
- value: 7.115
1696
  - type: precision_at_1000
1697
- value: 1.991
1698
  - type: precision_at_3
1699
- value: 31.476
1700
  - type: precision_at_5
1701
- value: 27.616000000000003
1702
  - type: recall_at_1
1703
- value: 4.316
1704
  - type: recall_at_10
1705
- value: 14.507
1706
  - type: recall_at_100
1707
- value: 28.847
1708
  - type: recall_at_1000
1709
- value: 61.758
1710
  - type: recall_at_3
1711
- value: 8.753
1712
  - type: recall_at_5
1713
- value: 11.153
1714
  - task:
1715
  type: Retrieval
1716
  dataset:
@@ -1721,65 +1721,65 @@ model-index:
1721
  revision: None
1722
  metrics:
1723
  - type: map_at_1
1724
- value: 22.374
1725
  - type: map_at_10
1726
- value: 36.095
1727
  - type: map_at_100
1728
- value: 37.413999999999994
1729
  - type: map_at_1000
1730
- value: 37.46
1731
  - type: map_at_3
1732
- value: 31.711
1733
  - type: map_at_5
1734
- value: 34.294999999999995
1735
  - type: mrr_at_1
1736
- value: 25.406000000000002
1737
  - type: mrr_at_10
1738
- value: 38.424
1739
  - type: mrr_at_100
1740
- value: 39.456
1741
  - type: mrr_at_1000
1742
- value: 39.488
1743
  - type: mrr_at_3
1744
- value: 34.613
1745
  - type: mrr_at_5
1746
- value: 36.864999999999995
1747
  - type: ndcg_at_1
1748
- value: 25.406000000000002
1749
  - type: ndcg_at_10
1750
- value: 43.614000000000004
1751
  - type: ndcg_at_100
1752
- value: 49.166
1753
  - type: ndcg_at_1000
1754
- value: 50.212
1755
  - type: ndcg_at_3
1756
- value: 35.221999999999994
1757
  - type: ndcg_at_5
1758
- value: 39.571
1759
  - type: precision_at_1
1760
- value: 25.406000000000002
1761
  - type: precision_at_10
1762
- value: 7.654
1763
  - type: precision_at_100
1764
- value: 1.0699999999999998
1765
  - type: precision_at_1000
1766
  value: 0.117
1767
  - type: precision_at_3
1768
- value: 16.425
1769
  - type: precision_at_5
1770
- value: 12.352
1771
  - type: recall_at_1
1772
- value: 22.374
1773
  - type: recall_at_10
1774
- value: 64.337
1775
  - type: recall_at_100
1776
- value: 88.374
1777
  - type: recall_at_1000
1778
- value: 96.101
1779
  - type: recall_at_3
1780
- value: 42.5
1781
  - type: recall_at_5
1782
- value: 52.556000000000004
1783
  - task:
1784
  type: Retrieval
1785
  dataset:
@@ -1790,65 +1790,65 @@ model-index:
1790
  revision: None
1791
  metrics:
1792
  - type: map_at_1
1793
- value: 69.301
1794
  - type: map_at_10
1795
- value: 83.128
1796
  - type: map_at_100
1797
- value: 83.779
1798
  - type: map_at_1000
1799
- value: 83.798
1800
  - type: map_at_3
1801
- value: 80.11399999999999
1802
  - type: map_at_5
1803
- value: 82.00699999999999
1804
  - type: mrr_at_1
1805
- value: 79.81
1806
  - type: mrr_at_10
1807
- value: 86.28
1808
  - type: mrr_at_100
1809
- value: 86.399
1810
  - type: mrr_at_1000
1811
- value: 86.401
1812
  - type: mrr_at_3
1813
- value: 85.26
1814
  - type: mrr_at_5
1815
- value: 85.93499999999999
1816
  - type: ndcg_at_1
1817
- value: 79.80000000000001
1818
  - type: ndcg_at_10
1819
- value: 87.06700000000001
1820
  - type: ndcg_at_100
1821
- value: 88.41799999999999
1822
  - type: ndcg_at_1000
1823
- value: 88.554
1824
  - type: ndcg_at_3
1825
- value: 84.052
1826
  - type: ndcg_at_5
1827
- value: 85.711
1828
  - type: precision_at_1
1829
- value: 79.80000000000001
1830
  - type: precision_at_10
1831
- value: 13.224
1832
  - type: precision_at_100
1833
- value: 1.5230000000000001
1834
  - type: precision_at_1000
1835
  value: 0.157
1836
  - type: precision_at_3
1837
- value: 36.723
1838
  - type: precision_at_5
1839
- value: 24.192
1840
  - type: recall_at_1
1841
- value: 69.301
1842
  - type: recall_at_10
1843
- value: 94.589
1844
  - type: recall_at_100
1845
- value: 99.29299999999999
1846
  - type: recall_at_1000
1847
- value: 99.965
1848
  - type: recall_at_3
1849
- value: 86.045
1850
  - type: recall_at_5
1851
- value: 90.656
1852
  - task:
1853
  type: Clustering
1854
  dataset:
@@ -1859,7 +1859,7 @@ model-index:
1859
  revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
1860
  metrics:
1861
  - type: v_measure
1862
- value: 43.09903181165838
1863
  - task:
1864
  type: Clustering
1865
  dataset:
@@ -1870,7 +1870,7 @@ model-index:
1870
  revision: 282350215ef01743dc01b456c7f5241fa8937f16
1871
  metrics:
1872
  - type: v_measure
1873
- value: 51.710378422887594
1874
  - task:
1875
  type: Retrieval
1876
  dataset:
@@ -1881,65 +1881,65 @@ model-index:
1881
  revision: None
1882
  metrics:
1883
  - type: map_at_1
1884
- value: 4.138
1885
  - type: map_at_10
1886
- value: 10.419
1887
  - type: map_at_100
1888
- value: 12.321
1889
  - type: map_at_1000
1890
- value: 12.605
1891
  - type: map_at_3
1892
- value: 7.445
1893
  - type: map_at_5
1894
- value: 8.859
1895
  - type: mrr_at_1
1896
- value: 20.4
1897
  - type: mrr_at_10
1898
- value: 30.148999999999997
1899
  - type: mrr_at_100
1900
- value: 31.357000000000003
1901
  - type: mrr_at_1000
1902
- value: 31.424999999999997
1903
  - type: mrr_at_3
1904
- value: 26.983
1905
  - type: mrr_at_5
1906
- value: 28.883
1907
  - type: ndcg_at_1
1908
- value: 20.4
1909
  - type: ndcg_at_10
1910
- value: 17.713
1911
  - type: ndcg_at_100
1912
- value: 25.221
1913
  - type: ndcg_at_1000
1914
- value: 30.381999999999998
1915
  - type: ndcg_at_3
1916
- value: 16.607
1917
  - type: ndcg_at_5
1918
- value: 14.559
1919
  - type: precision_at_1
1920
- value: 20.4
1921
  - type: precision_at_10
1922
- value: 9.3
1923
  - type: precision_at_100
1924
- value: 2.0060000000000002
1925
  - type: precision_at_1000
1926
- value: 0.32399999999999995
1927
  - type: precision_at_3
1928
- value: 15.5
1929
  - type: precision_at_5
1930
- value: 12.839999999999998
1931
  - type: recall_at_1
1932
- value: 4.138
1933
  - type: recall_at_10
1934
- value: 18.813
1935
  - type: recall_at_100
1936
- value: 40.692
1937
  - type: recall_at_1000
1938
- value: 65.835
1939
  - type: recall_at_3
1940
- value: 9.418
1941
  - type: recall_at_5
1942
- value: 12.983
1943
  - task:
1944
  type: STS
1945
  dataset:
@@ -1950,17 +1950,17 @@ model-index:
1950
  revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
1951
  metrics:
1952
  - type: cos_sim_pearson
1953
- value: 83.25944192442188
1954
  - type: cos_sim_spearman
1955
- value: 75.04296759426568
1956
  - type: euclidean_pearson
1957
- value: 74.8130340249869
1958
  - type: euclidean_spearman
1959
- value: 68.40180320816793
1960
  - type: manhattan_pearson
1961
- value: 74.9149619199144
1962
  - type: manhattan_spearman
1963
- value: 68.52380798258379
1964
  - task:
1965
  type: STS
1966
  dataset:
@@ -1971,17 +1971,17 @@ model-index:
1971
  revision: a0d554a64d88156834ff5ae9920b964011b16384
1972
  metrics:
1973
  - type: cos_sim_pearson
1974
- value: 81.91983072545858
1975
  - type: cos_sim_spearman
1976
- value: 73.5129498787296
1977
  - type: euclidean_pearson
1978
- value: 66.76535523270856
1979
  - type: euclidean_spearman
1980
- value: 56.64797879544097
1981
  - type: manhattan_pearson
1982
- value: 66.12191731384162
1983
  - type: manhattan_spearman
1984
- value: 56.37753861965956
1985
  - task:
1986
  type: STS
1987
  dataset:
@@ -1992,17 +1992,17 @@ model-index:
1992
  revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
1993
  metrics:
1994
  - type: cos_sim_pearson
1995
- value: 77.71164758747632
1996
  - type: cos_sim_spearman
1997
- value: 79.1530762030973
1998
  - type: euclidean_pearson
1999
- value: 69.50621786400177
2000
  - type: euclidean_spearman
2001
- value: 70.44898083428744
2002
  - type: manhattan_pearson
2003
- value: 69.04018458995307
2004
  - type: manhattan_spearman
2005
- value: 70.00888532086853
2006
  - task:
2007
  type: STS
2008
  dataset:
@@ -2013,17 +2013,17 @@ model-index:
2013
  revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
2014
  metrics:
2015
  - type: cos_sim_pearson
2016
- value: 78.90774995778577
2017
  - type: cos_sim_spearman
2018
- value: 75.24229403562713
2019
  - type: euclidean_pearson
2020
- value: 68.5838924571539
2021
  - type: euclidean_spearman
2022
- value: 65.06652398167358
2023
  - type: manhattan_pearson
2024
- value: 68.23143277902628
2025
  - type: manhattan_spearman
2026
- value: 64.79624516012709
2027
  - task:
2028
  type: STS
2029
  dataset:
@@ -2034,17 +2034,17 @@ model-index:
2034
  revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
2035
  metrics:
2036
  - type: cos_sim_pearson
2037
- value: 83.78074322110155
2038
  - type: cos_sim_spearman
2039
- value: 85.12071478276958
2040
  - type: euclidean_pearson
2041
- value: 65.00147804089737
2042
  - type: euclidean_spearman
2043
- value: 66.02559342831921
2044
  - type: manhattan_pearson
2045
- value: 65.01270190203297
2046
  - type: manhattan_spearman
2047
- value: 66.13038450207748
2048
  - task:
2049
  type: STS
2050
  dataset:
@@ -2055,17 +2055,17 @@ model-index:
2055
  revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
2056
  metrics:
2057
  - type: cos_sim_pearson
2058
- value: 77.29395327338185
2059
  - type: cos_sim_spearman
2060
- value: 80.07128686563352
2061
  - type: euclidean_pearson
2062
- value: 65.97939065455975
2063
  - type: euclidean_spearman
2064
- value: 66.80283051081129
2065
  - type: manhattan_pearson
2066
- value: 65.6750450606584
2067
  - type: manhattan_spearman
2068
- value: 66.55805829330733
2069
  - task:
2070
  type: STS
2071
  dataset:
@@ -2076,17 +2076,17 @@ model-index:
2076
  revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
2077
  metrics:
2078
  - type: cos_sim_pearson
2079
- value: 87.64956503192369
2080
  - type: cos_sim_spearman
2081
- value: 87.95719598052727
2082
  - type: euclidean_pearson
2083
- value: 73.35178669405819
2084
  - type: euclidean_spearman
2085
- value: 71.58959083579994
2086
  - type: manhattan_pearson
2087
- value: 73.24156949179472
2088
  - type: manhattan_spearman
2089
- value: 71.35933730170666
2090
  - task:
2091
  type: STS
2092
  dataset:
@@ -2097,17 +2097,17 @@ model-index:
2097
  revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
2098
  metrics:
2099
  - type: cos_sim_pearson
2100
- value: 66.61640922485357
2101
  - type: cos_sim_spearman
2102
- value: 66.08406266387749
2103
  - type: euclidean_pearson
2104
- value: 43.684972836995776
2105
  - type: euclidean_spearman
2106
- value: 60.26686390609082
2107
  - type: manhattan_pearson
2108
- value: 43.694268683941154
2109
  - type: manhattan_spearman
2110
- value: 59.61419719435629
2111
  - task:
2112
  type: STS
2113
  dataset:
@@ -2118,17 +2118,17 @@ model-index:
2118
  revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
2119
  metrics:
2120
  - type: cos_sim_pearson
2121
- value: 81.73624666044613
2122
  - type: cos_sim_spearman
2123
- value: 81.68869881979401
2124
  - type: euclidean_pearson
2125
- value: 72.47205990508046
2126
  - type: euclidean_spearman
2127
- value: 71.02381428101695
2128
  - type: manhattan_pearson
2129
- value: 72.4947870027535
2130
  - type: manhattan_spearman
2131
- value: 71.0789806652577
2132
  - task:
2133
  type: Reranking
2134
  dataset:
@@ -2139,9 +2139,9 @@ model-index:
2139
  revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
2140
  metrics:
2141
  - type: map
2142
- value: 79.53671929012175
2143
  - type: mrr
2144
- value: 93.96566033820936
2145
  - task:
2146
  type: Retrieval
2147
  dataset:
@@ -2152,65 +2152,65 @@ model-index:
2152
  revision: None
2153
  metrics:
2154
  - type: map_at_1
2155
- value: 43.761
2156
  - type: map_at_10
2157
- value: 53.846000000000004
2158
  - type: map_at_100
2159
- value: 54.55799999999999
2160
  - type: map_at_1000
2161
- value: 54.620999999999995
2162
  - type: map_at_3
2163
- value: 51.513
2164
  - type: map_at_5
2165
- value: 52.591
2166
  - type: mrr_at_1
2167
- value: 46.666999999999994
2168
  - type: mrr_at_10
2169
- value: 55.461000000000006
2170
  - type: mrr_at_100
2171
- value: 56.008
2172
  - type: mrr_at_1000
2173
- value: 56.069
2174
  - type: mrr_at_3
2175
- value: 53.5
2176
  - type: mrr_at_5
2177
- value: 54.417
2178
  - type: ndcg_at_1
2179
- value: 46.666999999999994
2180
  - type: ndcg_at_10
2181
- value: 58.599000000000004
2182
  - type: ndcg_at_100
2183
- value: 61.538000000000004
2184
  - type: ndcg_at_1000
2185
- value: 63.22
2186
  - type: ndcg_at_3
2187
- value: 54.254999999999995
2188
  - type: ndcg_at_5
2189
- value: 55.861000000000004
2190
  - type: precision_at_1
2191
- value: 46.666999999999994
2192
  - type: precision_at_10
2193
- value: 8.033
2194
  - type: precision_at_100
2195
- value: 0.963
2196
  - type: precision_at_1000
2197
  value: 0.11
2198
  - type: precision_at_3
2199
- value: 21.667
2200
  - type: precision_at_5
2201
- value: 14.066999999999998
2202
  - type: recall_at_1
2203
- value: 43.761
2204
  - type: recall_at_10
2205
- value: 71.65599999999999
2206
  - type: recall_at_100
2207
- value: 84.433
2208
  - type: recall_at_1000
2209
  value: 97.5
2210
  - type: recall_at_3
2211
- value: 59.522
2212
  - type: recall_at_5
2213
- value: 63.632999999999996
2214
  - task:
2215
  type: PairClassification
2216
  dataset:
@@ -2221,51 +2221,51 @@ model-index:
2221
  revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2222
  metrics:
2223
  - type: cos_sim_accuracy
2224
- value: 99.68811881188118
2225
  - type: cos_sim_ap
2226
- value: 91.08077352794682
2227
  - type: cos_sim_f1
2228
- value: 84.38570729319628
2229
  - type: cos_sim_precision
2230
- value: 82.64621284755513
2231
  - type: cos_sim_recall
2232
- value: 86.2
2233
  - type: dot_accuracy
2234
- value: 99.14653465346535
2235
  - type: dot_ap
2236
- value: 45.24942149367904
2237
  - type: dot_f1
2238
- value: 46.470062555853445
2239
  - type: dot_precision
2240
- value: 42.003231017770595
2241
  - type: dot_recall
2242
- value: 52
2243
  - type: euclidean_accuracy
2244
- value: 99.56930693069307
2245
  - type: euclidean_ap
2246
- value: 80.28575652582506
2247
  - type: euclidean_f1
2248
- value: 75.52054023635341
2249
  - type: euclidean_precision
2250
- value: 86.35778635778635
2251
  - type: euclidean_recall
2252
- value: 67.10000000000001
2253
  - type: manhattan_accuracy
2254
- value: 99.56039603960396
2255
  - type: manhattan_ap
2256
- value: 79.74630510301085
2257
  - type: manhattan_f1
2258
- value: 74.67569091934575
2259
  - type: manhattan_precision
2260
- value: 85.64036222509702
2261
  - type: manhattan_recall
2262
- value: 66.2
2263
  - type: max_accuracy
2264
- value: 99.68811881188118
2265
  - type: max_ap
2266
- value: 91.08077352794682
2267
  - type: max_f1
2268
- value: 84.38570729319628
2269
  - task:
2270
  type: Clustering
2271
  dataset:
@@ -2276,7 +2276,7 @@ model-index:
2276
  revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2277
  metrics:
2278
  - type: v_measure
2279
- value: 52.0788049295693
2280
  - task:
2281
  type: Clustering
2282
  dataset:
@@ -2287,7 +2287,7 @@ model-index:
2287
  revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2288
  metrics:
2289
  - type: v_measure
2290
- value: 31.606006030205545
2291
  - task:
2292
  type: Reranking
2293
  dataset:
@@ -2298,9 +2298,9 @@ model-index:
2298
  revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2299
  metrics:
2300
  - type: map
2301
- value: 50.87384988372756
2302
  - type: mrr
2303
- value: 51.62476922587217
2304
  - task:
2305
  type: Summarization
2306
  dataset:
@@ -2311,13 +2311,13 @@ model-index:
2311
  revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2312
  metrics:
2313
  - type: cos_sim_pearson
2314
- value: 30.355859978837156
2315
  - type: cos_sim_spearman
2316
- value: 30.0847548337847
2317
  - type: dot_pearson
2318
- value: 19.391736817587557
2319
  - type: dot_spearman
2320
- value: 20.732256259543014
2321
  - task:
2322
  type: Retrieval
2323
  dataset:
@@ -2328,65 +2328,65 @@ model-index:
2328
  revision: None
2329
  metrics:
2330
  - type: map_at_1
2331
- value: 0.19
2332
  - type: map_at_10
2333
- value: 1.2850000000000001
2334
  - type: map_at_100
2335
- value: 6.376999999999999
2336
  - type: map_at_1000
2337
- value: 15.21
2338
  - type: map_at_3
2339
- value: 0.492
2340
  - type: map_at_5
2341
- value: 0.776
2342
  - type: mrr_at_1
2343
- value: 68
2344
  - type: mrr_at_10
2345
- value: 79.783
2346
  - type: mrr_at_100
2347
- value: 79.783
2348
  - type: mrr_at_1000
2349
- value: 79.783
2350
  - type: mrr_at_3
2351
- value: 77.333
2352
  - type: mrr_at_5
2353
- value: 79.533
2354
  - type: ndcg_at_1
2355
- value: 62
2356
  - type: ndcg_at_10
2357
- value: 54.635
2358
  - type: ndcg_at_100
2359
- value: 40.939
2360
  - type: ndcg_at_1000
2361
- value: 37.716
2362
  - type: ndcg_at_3
2363
- value: 58.531
2364
  - type: ndcg_at_5
2365
- value: 58.762
2366
  - type: precision_at_1
2367
- value: 68
2368
  - type: precision_at_10
2369
- value: 58.8
2370
  - type: precision_at_100
2371
- value: 41.74
2372
  - type: precision_at_1000
2373
- value: 16.938
2374
  - type: precision_at_3
2375
- value: 64
2376
  - type: precision_at_5
2377
- value: 64.8
2378
  - type: recall_at_1
2379
- value: 0.19
2380
  - type: recall_at_10
2381
- value: 1.547
2382
  - type: recall_at_100
2383
- value: 9.739
2384
  - type: recall_at_1000
2385
- value: 35.815000000000005
2386
  - type: recall_at_3
2387
- value: 0.528
2388
  - type: recall_at_5
2389
- value: 0.894
2390
  - task:
2391
  type: Retrieval
2392
  dataset:
@@ -2397,65 +2397,65 @@ model-index:
2397
  revision: None
2398
  metrics:
2399
  - type: map_at_1
2400
- value: 1.514
2401
  - type: map_at_10
2402
- value: 7.163
2403
  - type: map_at_100
2404
- value: 11.623999999999999
2405
  - type: map_at_1000
2406
- value: 13.062999999999999
2407
  - type: map_at_3
2408
- value: 3.51
2409
  - type: map_at_5
2410
- value: 4.661
2411
  - type: mrr_at_1
2412
- value: 20.408
2413
  - type: mrr_at_10
2414
- value: 33.993
2415
  - type: mrr_at_100
2416
- value: 35.257
2417
  - type: mrr_at_1000
2418
- value: 35.313
2419
  - type: mrr_at_3
2420
- value: 30.272
2421
  - type: mrr_at_5
2422
- value: 31.701
2423
  - type: ndcg_at_1
2424
- value: 18.367
2425
  - type: ndcg_at_10
2426
- value: 18.062
2427
  - type: ndcg_at_100
2428
- value: 28.441
2429
  - type: ndcg_at_1000
2430
- value: 40.748
2431
  - type: ndcg_at_3
2432
- value: 18.651999999999997
2433
  - type: ndcg_at_5
2434
- value: 17.055
2435
  - type: precision_at_1
2436
- value: 20.408
2437
  - type: precision_at_10
2438
- value: 17.551
2439
  - type: precision_at_100
2440
- value: 6.223999999999999
2441
  - type: precision_at_1000
2442
- value: 1.427
2443
  - type: precision_at_3
2444
  value: 20.408
2445
  - type: precision_at_5
2446
- value: 17.959
2447
  - type: recall_at_1
2448
- value: 1.514
2449
  - type: recall_at_10
2450
- value: 13.447000000000001
2451
  - type: recall_at_100
2452
- value: 39.77
2453
  - type: recall_at_1000
2454
- value: 76.95
2455
  - type: recall_at_3
2456
- value: 4.806
2457
  - type: recall_at_5
2458
- value: 6.873
2459
  - task:
2460
  type: Classification
2461
  dataset:
@@ -2466,11 +2466,11 @@ model-index:
2466
  revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2467
  metrics:
2468
  - type: accuracy
2469
- value: 65.53179999999999
2470
  - type: ap
2471
- value: 11.504743595308318
2472
  - type: f1
2473
- value: 49.74264614001562
2474
  - task:
2475
  type: Classification
2476
  dataset:
@@ -2481,9 +2481,9 @@ model-index:
2481
  revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2482
  metrics:
2483
  - type: accuracy
2484
- value: 56.47425014148275
2485
  - type: f1
2486
- value: 56.555750746223346
2487
  - task:
2488
  type: Clustering
2489
  dataset:
@@ -2494,7 +2494,7 @@ model-index:
2494
  revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2495
  metrics:
2496
  - type: v_measure
2497
- value: 39.27004599453324
2498
  - task:
2499
  type: PairClassification
2500
  dataset:
@@ -2505,51 +2505,51 @@ model-index:
2505
  revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2506
  metrics:
2507
  - type: cos_sim_accuracy
2508
- value: 84.47875067056088
2509
  - type: cos_sim_ap
2510
- value: 68.630858164926
2511
  - type: cos_sim_f1
2512
- value: 64.5112402121748
2513
  - type: cos_sim_precision
2514
- value: 61.87015503875969
2515
  - type: cos_sim_recall
2516
- value: 67.38786279683377
2517
  - type: dot_accuracy
2518
- value: 77.68969422423557
2519
  - type: dot_ap
2520
- value: 37.28838556128439
2521
  - type: dot_f1
2522
- value: 43.27918525376652
2523
  - type: dot_precision
2524
- value: 31.776047460140898
2525
  - type: dot_recall
2526
- value: 67.83641160949868
2527
  - type: euclidean_accuracy
2528
- value: 82.67866722298385
2529
  - type: euclidean_ap
2530
- value: 62.72011158877603
2531
  - type: euclidean_f1
2532
- value: 60.39579770339605
2533
  - type: euclidean_precision
2534
- value: 56.23293903548681
2535
  - type: euclidean_recall
2536
- value: 65.22427440633246
2537
  - type: manhattan_accuracy
2538
- value: 82.67866722298385
2539
  - type: manhattan_ap
2540
- value: 62.80364769571995
2541
  - type: manhattan_f1
2542
- value: 60.413827282864574
2543
  - type: manhattan_precision
2544
- value: 56.94931090866619
2545
  - type: manhattan_recall
2546
- value: 64.32717678100263
2547
  - type: max_accuracy
2548
- value: 84.47875067056088
2549
  - type: max_ap
2550
- value: 68.630858164926
2551
  - type: max_f1
2552
- value: 64.5112402121748
2553
  - task:
2554
  type: PairClassification
2555
  dataset:
@@ -2560,51 +2560,51 @@ model-index:
2560
  revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2561
  metrics:
2562
  - type: cos_sim_accuracy
2563
- value: 88.4192959987581
2564
  - type: cos_sim_ap
2565
- value: 84.81803796578367
2566
  - type: cos_sim_f1
2567
- value: 77.1643709825528
2568
  - type: cos_sim_precision
2569
- value: 73.77958839643183
2570
  - type: cos_sim_recall
2571
- value: 80.874653526332
2572
  - type: dot_accuracy
2573
- value: 81.99441145651414
2574
  - type: dot_ap
2575
- value: 67.908510950511
2576
  - type: dot_f1
2577
- value: 64.4734255193656
2578
  - type: dot_precision
2579
- value: 56.120935539075866
2580
  - type: dot_recall
2581
- value: 75.74684323991376
2582
  - type: euclidean_accuracy
2583
- value: 82.67163426087632
2584
  - type: euclidean_ap
2585
- value: 70.1466353903414
2586
  - type: euclidean_f1
2587
- value: 62.686024087617795
2588
  - type: euclidean_precision
2589
- value: 59.42738875474301
2590
  - type: euclidean_recall
2591
- value: 66.32275947028026
2592
  - type: manhattan_accuracy
2593
- value: 82.6483486630186
2594
  - type: manhattan_ap
2595
- value: 70.12958345267741
2596
  - type: manhattan_f1
2597
- value: 62.5966218150587
2598
  - type: manhattan_precision
2599
- value: 58.47820272800214
2600
  - type: manhattan_recall
2601
- value: 67.33908222975053
2602
  - type: max_accuracy
2603
- value: 88.4192959987581
2604
  - type: max_ap
2605
- value: 84.81803796578367
2606
  - type: max_f1
2607
- value: 77.1643709825528
2608
  ---
2609
  ---
2610
 
@@ -2665,9 +2665,9 @@ We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert
2665
  |all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 |
2666
  |all-mpnet-base-v2|0.726|0.835|**0.78** |0.857|0.8 |**0.906**|0.513 |0.875|0.656 |
2667
  |ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685** |0.876|**0.726** |
2668
- |jina-embedding-t-en-v1|0.714|0.775|0.723|0.825|0.771|0.863|0.479 |0.841|0.542 |
2669
- |jina-embedding-s-en-v1|**0.743**|0.786|0.738|0.837|0.80|0.875|0.523 |0.857|0.524 |
2670
- |jina-embedding-b-en-v1|0.735|0.792|0.752|0.851|0.801|0.89|0.546 |0.871|0.586 |
2671
  |jina-embedding-l-en-v1|0.739|**0.844**|0.778|**0.863**|0.821|0.896|0.566 |**0.882**|0.608 |
2672
 
2673
  ## Usage
@@ -2719,11 +2719,11 @@ If you find Jina Embeddings useful in your research, please cite the following p
2719
 
2720
  ``` latex
2721
  @misc{günther2023jina,
2722
- title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
2723
  author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
2724
  year={2023},
2725
  eprint={2307.11224},
2726
  archivePrefix={arXiv},
2727
  primaryClass={cs.CL}
2728
  }
2729
- ```
 
23
  revision: e8379541af4e31359cca9fbcf4b00f2671dba205
24
  metrics:
25
  - type: accuracy
26
+ value: 66.73134328358208
27
  - type: ap
28
+ value: 28.30575908745204
29
  - type: f1
30
+ value: 60.02420130946191
31
  - task:
32
  type: Classification
33
  dataset:
 
38
  revision: e2d317d38cd51312af73b3d32a06d1a08b442046
39
  metrics:
40
  - type: accuracy
41
+ value: 67.6068
42
  - type: ap
43
+ value: 63.5899352938589
44
  - type: f1
45
+ value: 65.64285334357656
46
  - task:
47
  type: Classification
48
  dataset:
 
53
  revision: 1399c76144fd37290681b995c656ef9b2e06e26d
54
  metrics:
55
  - type: accuracy
56
+ value: 31.178
57
  - type: f1
58
+ value: 29.68460843733487
59
  - task:
60
  type: Retrieval
61
  dataset:
62
+ type: arguana
63
+ name: MTEB ArguAna
64
  config: default
65
  split: test
66
  revision: None
67
  metrics:
68
  - type: map_at_1
69
+ value: 24.964
70
  - type: map_at_10
71
+ value: 40.217999999999996
72
  - type: map_at_100
73
+ value: 41.263
74
  - type: map_at_1000
75
+ value: 41.277
76
  - type: map_at_3
77
+ value: 35.183
78
  - type: map_at_5
79
+ value: 38.045
80
  - type: mrr_at_1
81
+ value: 25.107000000000003
82
  - type: mrr_at_10
83
+ value: 40.272999999999996
84
  - type: mrr_at_100
85
+ value: 41.318
86
  - type: mrr_at_1000
87
+ value: 41.333
88
  - type: mrr_at_3
89
+ value: 35.242000000000004
90
  - type: mrr_at_5
91
+ value: 38.101
92
  - type: ndcg_at_1
93
+ value: 24.964
94
  - type: ndcg_at_10
95
+ value: 49.006
96
  - type: ndcg_at_100
97
+ value: 53.446000000000005
98
  - type: ndcg_at_1000
99
+ value: 53.813
100
  - type: ndcg_at_3
101
+ value: 38.598
102
  - type: ndcg_at_5
103
+ value: 43.74
104
  - type: precision_at_1
105
+ value: 24.964
106
  - type: precision_at_10
107
+ value: 7.724
108
  - type: precision_at_100
109
+ value: 0.966
110
  - type: precision_at_1000
111
+ value: 0.099
112
  - type: precision_at_3
113
+ value: 16.169
114
  - type: precision_at_5
115
+ value: 12.191
116
  - type: recall_at_1
117
+ value: 24.964
118
  - type: recall_at_10
119
+ value: 77.24
120
  - type: recall_at_100
121
+ value: 96.586
122
  - type: recall_at_1000
123
+ value: 99.431
124
  - type: recall_at_3
125
+ value: 48.506
126
  - type: recall_at_5
127
+ value: 60.953
128
+ - task:
129
+ type: Clustering
130
+ dataset:
131
+ type: mteb/arxiv-clustering-p2p
132
+ name: MTEB ArxivClusteringP2P
133
+ config: default
134
+ split: test
135
+ revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
136
+ metrics:
137
+ - type: v_measure
138
+ value: 39.25203906042786
139
+ - task:
140
+ type: Clustering
141
+ dataset:
142
+ type: mteb/arxiv-clustering-s2s
143
+ name: MTEB ArxivClusteringS2S
144
+ config: default
145
+ split: test
146
+ revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
147
+ metrics:
148
+ - type: v_measure
149
+ value: 29.07648348376354
150
+ - task:
151
+ type: Reranking
152
+ dataset:
153
+ type: mteb/askubuntudupquestions-reranking
154
+ name: MTEB AskUbuntuDupQuestions
155
+ config: default
156
+ split: test
157
+ revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
158
+ metrics:
159
+ - type: map
160
+ value: 62.4029266143623
161
+ - type: mrr
162
+ value: 75.45750340764191
163
+ - task:
164
+ type: STS
165
+ dataset:
166
+ type: mteb/biosses-sts
167
+ name: MTEB BIOSSES
168
+ config: default
169
+ split: test
170
+ revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
171
+ metrics:
172
+ - type: cos_sim_pearson
173
+ value: 85.92280995704714
174
+ - type: cos_sim_spearman
175
+ value: 83.58082010833608
176
+ - type: euclidean_pearson
177
+ value: 48.64744162695948
178
+ - type: euclidean_spearman
179
+ value: 48.817377397301556
180
+ - type: manhattan_pearson
181
+ value: 48.87684776623195
182
+ - type: manhattan_spearman
183
+ value: 48.94268145725884
184
+ - task:
185
+ type: Classification
186
+ dataset:
187
+ type: mteb/banking77
188
+ name: MTEB Banking77Classification
189
+ config: default
190
+ split: test
191
+ revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
192
+ metrics:
193
+ - type: accuracy
194
+ value: 84.05519480519482
195
+ - type: f1
196
+ value: 83.94978356890618
197
+ - task:
198
+ type: Clustering
199
+ dataset:
200
+ type: mteb/biorxiv-clustering-p2p
201
+ name: MTEB BiorxivClusteringP2P
202
+ config: default
203
+ split: test
204
+ revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
205
+ metrics:
206
+ - type: v_measure
207
+ value: 32.2033276486685
208
+ - task:
209
+ type: Clustering
210
+ dataset:
211
+ type: mteb/biorxiv-clustering-s2s
212
+ name: MTEB BiorxivClusteringS2S
213
+ config: default
214
+ split: test
215
+ revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
216
+ metrics:
217
+ - type: v_measure
218
+ value: 26.631954164406014
219
  - task:
220
  type: Retrieval
221
  dataset:
222
  type: BeIR/cqadupstack
223
+ name: MTEB CQADupstackAndroidRetrieval
224
  config: default
225
  split: test
226
  revision: None
227
  metrics:
228
  - type: map_at_1
229
+ value: 29.625
230
  - type: map_at_10
231
+ value: 40.037
232
  - type: map_at_100
233
+ value: 41.52
234
  - type: map_at_1000
235
+ value: 41.654
236
  - type: map_at_3
237
+ value: 36.818
238
  - type: map_at_5
239
+ value: 38.426
240
  - type: mrr_at_1
241
+ value: 35.336
242
  - type: mrr_at_10
243
+ value: 45.395
244
  - type: mrr_at_100
245
+ value: 46.221000000000004
246
  - type: mrr_at_1000
247
+ value: 46.264
248
  - type: mrr_at_3
249
+ value: 42.823
250
  - type: mrr_at_5
251
+ value: 44.204
252
  - type: ndcg_at_1
253
+ value: 35.336
254
  - type: ndcg_at_10
255
+ value: 46.326
256
  - type: ndcg_at_100
257
+ value: 51.795
258
  - type: ndcg_at_1000
259
+ value: 53.834
260
  - type: ndcg_at_3
261
+ value: 41.299
262
  - type: ndcg_at_5
263
+ value: 43.247
264
  - type: precision_at_1
265
+ value: 35.336
266
  - type: precision_at_10
267
+ value: 8.627
268
  - type: precision_at_100
269
+ value: 1.428
270
  - type: precision_at_1000
271
+ value: 0.197
272
  - type: precision_at_3
273
+ value: 19.647000000000002
274
  - type: precision_at_5
275
+ value: 13.733999999999998
276
  - type: recall_at_1
277
+ value: 29.625
278
  - type: recall_at_10
279
+ value: 59.165
280
  - type: recall_at_100
281
+ value: 81.675
282
  - type: recall_at_1000
283
+ value: 94.17
284
  - type: recall_at_3
285
+ value: 44.485
286
  - type: recall_at_5
287
+ value: 50.198
288
  - task:
289
  type: Retrieval
290
  dataset:
291
  type: BeIR/cqadupstack
292
+ name: MTEB CQADupstackEnglishRetrieval
293
  config: default
294
  split: test
295
  revision: None
296
  metrics:
297
  - type: map_at_1
298
+ value: 26.687
299
  - type: map_at_10
300
+ value: 36.062
301
  - type: map_at_100
302
+ value: 37.263000000000005
303
  - type: map_at_1000
304
+ value: 37.397999999999996
305
  - type: map_at_3
306
+ value: 32.967
307
  - type: map_at_5
308
+ value: 34.75
309
  - type: mrr_at_1
310
+ value: 33.885
311
  - type: mrr_at_10
312
+ value: 42.632999999999996
313
  - type: mrr_at_100
314
+ value: 43.305
315
  - type: mrr_at_1000
316
+ value: 43.354
317
  - type: mrr_at_3
318
+ value: 39.958
319
  - type: mrr_at_5
320
+ value: 41.63
321
  - type: ndcg_at_1
322
+ value: 33.885
323
  - type: ndcg_at_10
324
+ value: 42.001
325
  - type: ndcg_at_100
326
+ value: 46.436
327
  - type: ndcg_at_1000
328
+ value: 48.774
329
  - type: ndcg_at_3
330
+ value: 37.183
331
  - type: ndcg_at_5
332
+ value: 39.605000000000004
333
  - type: precision_at_1
334
+ value: 33.885
335
  - type: precision_at_10
336
+ value: 7.962
337
  - type: precision_at_100
338
+ value: 1.283
339
  - type: precision_at_1000
340
+ value: 0.18
341
  - type: precision_at_3
342
+ value: 17.855999999999998
343
  - type: precision_at_5
344
+ value: 13.083
345
  - type: recall_at_1
346
+ value: 26.687
347
  - type: recall_at_10
348
+ value: 52.75
349
  - type: recall_at_100
350
+ value: 71.324
351
  - type: recall_at_1000
352
+ value: 86.356
353
  - type: recall_at_3
354
+ value: 38.83
355
  - type: recall_at_5
356
+ value: 45.23
357
  - task:
358
  type: Retrieval
359
  dataset:
360
  type: BeIR/cqadupstack
361
+ name: MTEB CQADupstackGamingRetrieval
362
  config: default
363
  split: test
364
  revision: None
365
  metrics:
366
  - type: map_at_1
367
+ value: 34.02
368
  - type: map_at_10
369
+ value: 45.751999999999995
370
  - type: map_at_100
371
+ value: 46.867
372
  - type: map_at_1000
373
+ value: 46.93
374
  - type: map_at_3
375
+ value: 42.409
376
  - type: map_at_5
377
+ value: 44.464999999999996
378
  - type: mrr_at_1
379
+ value: 38.307
380
  - type: mrr_at_10
381
+ value: 48.718
382
  - type: mrr_at_100
383
+ value: 49.509
384
  - type: mrr_at_1000
385
+ value: 49.542
386
  - type: mrr_at_3
387
+ value: 46.007999999999996
388
  - type: mrr_at_5
389
+ value: 47.766999999999996
390
  - type: ndcg_at_1
391
+ value: 38.307
392
  - type: ndcg_at_10
393
+ value: 51.666999999999994
394
  - type: ndcg_at_100
395
+ value: 56.242000000000004
396
  - type: ndcg_at_1000
397
+ value: 57.477999999999994
398
  - type: ndcg_at_3
399
+ value: 45.912
400
  - type: ndcg_at_5
401
+ value: 49.106
402
  - type: precision_at_1
403
+ value: 38.307
404
  - type: precision_at_10
405
+ value: 8.476
406
  - type: precision_at_100
407
+ value: 1.176
408
  - type: precision_at_1000
409
+ value: 0.133
410
  - type: precision_at_3
411
+ value: 20.522000000000002
412
  - type: precision_at_5
413
+ value: 14.557999999999998
414
  - type: recall_at_1
415
+ value: 34.02
416
  - type: recall_at_10
417
+ value: 66.046
418
  - type: recall_at_100
419
+ value: 85.817
420
  - type: recall_at_1000
421
+ value: 94.453
422
  - type: recall_at_3
423
+ value: 51.059
424
  - type: recall_at_5
425
+ value: 58.667
426
  - task:
427
  type: Retrieval
428
  dataset:
429
  type: BeIR/cqadupstack
430
+ name: MTEB CQADupstackGisRetrieval
431
  config: default
432
  split: test
433
  revision: None
434
  metrics:
435
  - type: map_at_1
436
+ value: 23.939
437
  - type: map_at_10
438
+ value: 32.627
439
  - type: map_at_100
440
+ value: 33.617999999999995
441
  - type: map_at_1000
442
+ value: 33.701
443
  - type: map_at_3
444
+ value: 30.11
445
  - type: map_at_5
446
+ value: 31.380000000000003
447
  - type: mrr_at_1
448
+ value: 25.989
449
  - type: mrr_at_10
450
+ value: 34.655
451
  - type: mrr_at_100
452
+ value: 35.502
453
  - type: mrr_at_1000
454
+ value: 35.563
455
  - type: mrr_at_3
456
+ value: 32.109
457
  - type: mrr_at_5
458
+ value: 33.426
459
  - type: ndcg_at_1
460
+ value: 25.989
461
  - type: ndcg_at_10
462
+ value: 37.657000000000004
463
  - type: ndcg_at_100
464
+ value: 42.467
465
  - type: ndcg_at_1000
466
+ value: 44.677
467
  - type: ndcg_at_3
468
+ value: 32.543
469
  - type: ndcg_at_5
470
+ value: 34.74
471
  - type: precision_at_1
472
+ value: 25.989
473
  - type: precision_at_10
474
+ value: 5.876
475
  - type: precision_at_100
476
+ value: 0.8710000000000001
477
  - type: precision_at_1000
478
+ value: 0.11
479
  - type: precision_at_3
480
+ value: 13.861
481
  - type: precision_at_5
482
+ value: 9.626999999999999
483
  - type: recall_at_1
484
+ value: 23.939
485
  - type: recall_at_10
486
+ value: 51.28
487
  - type: recall_at_100
488
+ value: 73.428
489
  - type: recall_at_1000
490
+ value: 90.309
491
  - type: recall_at_3
492
+ value: 37.245
493
  - type: recall_at_5
494
+ value: 42.541000000000004
495
  - task:
496
  type: Retrieval
497
  dataset:
498
  type: BeIR/cqadupstack
499
+ name: MTEB CQADupstackMathematicaRetrieval
500
  config: default
501
  split: test
502
  revision: None
503
  metrics:
504
  - type: map_at_1
505
+ value: 15.082
506
  - type: map_at_10
507
+ value: 22.486
508
  - type: map_at_100
509
+ value: 23.687
510
  - type: map_at_1000
511
+ value: 23.807000000000002
512
  - type: map_at_3
513
+ value: 20.076
514
  - type: map_at_5
515
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516
  - type: mrr_at_1
517
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518
  - type: mrr_at_10
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520
  - type: mrr_at_100
521
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522
  - type: mrr_at_1000
523
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524
  - type: mrr_at_3
525
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526
  - type: mrr_at_5
527
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528
  - type: ndcg_at_1
529
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530
  - type: ndcg_at_10
531
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532
  - type: ndcg_at_100
533
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534
  - type: ndcg_at_1000
535
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536
  - type: ndcg_at_3
537
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538
  - type: ndcg_at_5
539
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540
  - type: precision_at_1
541
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542
  - type: precision_at_10
543
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544
  - type: precision_at_100
545
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546
  - type: precision_at_1000
547
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548
  - type: precision_at_3
549
+ value: 11.235000000000001
550
  - type: precision_at_5
551
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552
  - type: recall_at_1
553
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554
  - type: recall_at_10
555
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556
  - type: recall_at_100
557
+ value: 64.621
558
  - type: recall_at_1000
559
+ value: 86.162
560
  - type: recall_at_3
561
+ value: 26.055
562
  - type: recall_at_5
563
+ value: 31.208999999999996
564
  - task:
565
  type: Retrieval
566
  dataset:
567
  type: BeIR/cqadupstack
568
+ name: MTEB CQADupstackPhysicsRetrieval
569
  config: default
570
  split: test
571
  revision: None
572
  metrics:
573
  - type: map_at_1
574
+ value: 24.759999999999998
575
  - type: map_at_10
576
+ value: 33.706
577
  - type: map_at_100
578
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579
  - type: map_at_1000
580
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581
  - type: map_at_3
582
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583
  - type: map_at_5
584
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585
  - type: mrr_at_1
586
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587
  - type: mrr_at_10
588
+ value: 38.521
589
  - type: mrr_at_100
590
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591
  - type: mrr_at_1000
592
+ value: 39.494
593
  - type: mrr_at_3
594
+ value: 35.691
595
  - type: mrr_at_5
596
+ value: 37.424
597
  - type: ndcg_at_1
598
+ value: 29.548000000000002
599
  - type: ndcg_at_10
600
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601
  - type: ndcg_at_100
602
+ value: 44.907000000000004
603
  - type: ndcg_at_1000
604
+ value: 47.494
605
  - type: ndcg_at_3
606
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607
  - type: ndcg_at_5
608
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609
  - type: precision_at_1
610
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611
  - type: precision_at_10
612
+ value: 7.084
613
  - type: precision_at_100
614
+ value: 1.169
615
  - type: precision_at_1000
616
+ value: 0.158
617
  - type: precision_at_3
618
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619
  - type: precision_at_5
620
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621
  - type: recall_at_1
622
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623
  - type: recall_at_10
624
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625
  - type: recall_at_100
626
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627
  - type: recall_at_1000
628
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629
  - type: recall_at_3
630
+ value: 36.892
631
  - type: recall_at_5
632
+ value: 43.333
633
  - task:
634
  type: Retrieval
635
  dataset:
636
  type: BeIR/cqadupstack
637
+ name: MTEB CQADupstackProgrammersRetrieval
638
  config: default
639
  split: test
640
  revision: None
641
  metrics:
642
  - type: map_at_1
643
+ value: 23.247999999999998
644
  - type: map_at_10
645
+ value: 31.878
646
  - type: map_at_100
647
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648
  - type: map_at_1000
649
+ value: 33.263999999999996
650
  - type: map_at_3
651
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652
  - type: map_at_5
653
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654
  - type: mrr_at_1
655
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656
  - type: mrr_at_10
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658
  - type: mrr_at_100
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660
  - type: mrr_at_1000
661
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662
  - type: mrr_at_3
663
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664
  - type: mrr_at_5
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666
  - type: ndcg_at_1
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668
  - type: ndcg_at_10
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670
  - type: ndcg_at_100
671
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672
  - type: ndcg_at_1000
673
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674
  - type: ndcg_at_3
675
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676
  - type: ndcg_at_5
677
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678
  - type: precision_at_1
679
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680
  - type: precision_at_10
681
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682
  - type: precision_at_100
683
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684
  - type: precision_at_1000
685
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686
  - type: precision_at_3
687
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688
  - type: precision_at_5
689
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690
  - type: recall_at_1
691
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692
  - type: recall_at_10
693
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694
  - type: recall_at_100
695
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696
  - type: recall_at_1000
697
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698
  - type: recall_at_3
699
+ value: 35.961999999999996
700
  - type: recall_at_5
701
+ value: 40.504
702
  - task:
703
  type: Retrieval
704
  dataset:
705
  type: BeIR/cqadupstack
706
+ name: MTEB CQADupstackRetrieval
707
  config: default
708
  split: test
709
  revision: None
710
  metrics:
711
  - type: map_at_1
712
+ value: 23.825583333333334
713
  - type: map_at_10
714
+ value: 32.2845
715
  - type: map_at_100
716
+ value: 33.48566666666667
717
  - type: map_at_1000
718
+ value: 33.60833333333333
719
  - type: map_at_3
720
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721
  - type: map_at_5
722
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723
  - type: mrr_at_1
724
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725
  - type: mrr_at_10
726
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727
  - type: mrr_at_100
728
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729
  - type: mrr_at_1000
730
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731
  - type: mrr_at_3
732
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733
  - type: mrr_at_5
734
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735
  - type: ndcg_at_1
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737
  - type: ndcg_at_10
738
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739
  - type: ndcg_at_100
740
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741
  - type: ndcg_at_1000
742
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743
  - type: ndcg_at_3
744
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745
  - type: ndcg_at_5
746
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747
  - type: precision_at_1
748
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749
  - type: precision_at_10
750
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751
  - type: precision_at_100
752
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753
  - type: precision_at_1000
754
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755
  - type: precision_at_3
756
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757
  - type: precision_at_5
758
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759
  - type: recall_at_1
760
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761
  - type: recall_at_10
762
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763
  - type: recall_at_100
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765
  - type: recall_at_1000
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767
  - type: recall_at_3
768
+ value: 35.90525
769
  - type: recall_at_5
770
+ value: 41.24583333333334
771
  - task:
772
  type: Retrieval
773
  dataset:
774
  type: BeIR/cqadupstack
775
+ name: MTEB CQADupstackStatsRetrieval
776
  config: default
777
  split: test
778
  revision: None
779
  metrics:
780
  - type: map_at_1
781
+ value: 21.343
782
  - type: map_at_10
783
+ value: 27.313
784
  - type: map_at_100
785
+ value: 28.316999999999997
786
  - type: map_at_1000
787
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788
  - type: map_at_3
789
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790
  - type: map_at_5
791
+ value: 26.409
792
  - type: mrr_at_1
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794
  - type: mrr_at_10
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796
  - type: mrr_at_100
797
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798
  - type: mrr_at_1000
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800
  - type: mrr_at_3
801
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802
  - type: mrr_at_5
803
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804
  - type: ndcg_at_1
805
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806
  - type: ndcg_at_10
807
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808
  - type: ndcg_at_100
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810
  - type: ndcg_at_1000
811
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812
  - type: ndcg_at_3
813
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814
  - type: ndcg_at_5
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816
  - type: precision_at_1
817
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818
  - type: precision_at_10
819
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820
  - type: precision_at_100
821
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822
  - type: precision_at_1000
823
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824
  - type: precision_at_3
825
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826
  - type: precision_at_5
827
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828
  - type: recall_at_1
829
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830
  - type: recall_at_10
831
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832
  - type: recall_at_100
833
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834
  - type: recall_at_1000
835
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836
  - type: recall_at_3
837
+ value: 29.337000000000003
838
  - type: recall_at_5
839
+ value: 34.756
840
  - task:
841
  type: Retrieval
842
  dataset:
843
  type: BeIR/cqadupstack
844
+ name: MTEB CQADupstackTexRetrieval
845
  config: default
846
  split: test
847
  revision: None
848
  metrics:
849
  - type: map_at_1
850
+ value: 16.595
851
  - type: map_at_10
852
+ value: 23.433
853
  - type: map_at_100
854
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855
  - type: map_at_1000
856
+ value: 24.709999999999997
857
  - type: map_at_3
858
+ value: 21.268
859
  - type: map_at_5
860
+ value: 22.393
861
  - type: mrr_at_1
862
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863
  - type: mrr_at_10
864
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865
  - type: mrr_at_100
866
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867
  - type: mrr_at_1000
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869
  - type: mrr_at_3
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871
  - type: mrr_at_5
872
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873
  - type: ndcg_at_1
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875
  - type: ndcg_at_10
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877
  - type: ndcg_at_100
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879
  - type: ndcg_at_1000
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881
  - type: ndcg_at_3
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883
  - type: ndcg_at_5
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885
  - type: precision_at_1
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887
  - type: precision_at_10
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889
  - type: precision_at_100
890
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891
  - type: precision_at_1000
892
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893
  - type: precision_at_3
894
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895
  - type: precision_at_5
896
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897
  - type: recall_at_1
898
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899
  - type: recall_at_10
900
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901
  - type: recall_at_100
902
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903
  - type: recall_at_1000
904
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905
  - type: recall_at_3
906
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907
  - type: recall_at_5
908
+ value: 31.002000000000002
909
  - task:
910
  type: Retrieval
911
  dataset:
912
  type: BeIR/cqadupstack
913
+ name: MTEB CQADupstackUnixRetrieval
914
  config: default
915
  split: test
916
  revision: None
917
  metrics:
918
  - type: map_at_1
919
+ value: 24.85
920
  - type: map_at_10
921
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922
  - type: map_at_100
923
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924
  - type: map_at_1000
925
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926
  - type: map_at_3
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928
  - type: map_at_5
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930
  - type: mrr_at_1
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932
  - type: mrr_at_10
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934
  - type: mrr_at_100
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936
  - type: mrr_at_1000
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938
  - type: mrr_at_3
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940
  - type: mrr_at_5
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942
  - type: ndcg_at_1
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944
  - type: ndcg_at_10
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946
  - type: ndcg_at_100
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948
  - type: ndcg_at_1000
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950
  - type: ndcg_at_3
951
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952
  - type: ndcg_at_5
953
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954
  - type: precision_at_1
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956
  - type: precision_at_10
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958
  - type: precision_at_100
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960
  - type: precision_at_1000
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962
  - type: precision_at_3
963
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964
  - type: precision_at_5
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966
  - type: recall_at_1
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968
  - type: recall_at_10
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970
  - type: recall_at_100
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972
  - type: recall_at_1000
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974
  - type: recall_at_3
975
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976
  - type: recall_at_5
977
+ value: 41.385
978
  - task:
979
  type: Retrieval
980
  dataset:
981
  type: BeIR/cqadupstack
982
+ name: MTEB CQADupstackWebmastersRetrieval
983
  config: default
984
  split: test
985
  revision: None
986
  metrics:
987
  - type: map_at_1
988
+ value: 23.016000000000002
989
  - type: map_at_10
990
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991
  - type: map_at_100
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993
  - type: map_at_1000
994
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995
  - type: map_at_3
996
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997
  - type: map_at_5
998
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999
  - type: mrr_at_1
1000
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1001
  - type: mrr_at_10
1002
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1003
  - type: mrr_at_100
1004
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1005
  - type: mrr_at_1000
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1007
  - type: mrr_at_3
1008
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1009
  - type: mrr_at_5
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1011
  - type: ndcg_at_1
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1013
  - type: ndcg_at_10
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1015
  - type: ndcg_at_100
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1017
  - type: ndcg_at_1000
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1019
  - type: ndcg_at_3
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1021
  - type: ndcg_at_5
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1023
  - type: precision_at_1
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  - type: precision_at_10
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1027
  - type: precision_at_100
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1029
  - type: precision_at_1000
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1031
  - type: precision_at_3
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1033
  - type: precision_at_5
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1035
  - type: recall_at_1
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1037
  - type: recall_at_10
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1039
  - type: recall_at_100
1040
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1041
  - type: recall_at_1000
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1043
  - type: recall_at_3
1044
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1045
  - type: recall_at_5
1046
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1047
  - task:
1048
  type: Retrieval
1049
  dataset:
1050
+ type: BeIR/cqadupstack
1051
+ name: MTEB CQADupstackWordpressRetrieval
1052
  config: default
1053
  split: test
1054
  revision: None
1055
  metrics:
1056
  - type: map_at_1
1057
+ value: 22.742
1058
  - type: map_at_10
1059
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1060
  - type: map_at_100
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1062
  - type: map_at_1000
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  - type: map_at_3
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  - type: map_at_5
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  - type: mrr_at_1
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  - type: mrr_at_10
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  - type: mrr_at_100
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1074
  - type: mrr_at_1000
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  - type: mrr_at_3
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  - type: mrr_at_5
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  - type: ndcg_at_1
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1082
  - type: ndcg_at_10
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  - type: ndcg_at_100
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  - type: ndcg_at_1000
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  - type: ndcg_at_3
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  - type: ndcg_at_5
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  - type: precision_at_1
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  - type: precision_at_10
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  - type: precision_at_100
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1098
  - type: precision_at_1000
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  - type: precision_at_3
1101
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  - type: precision_at_5
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  - type: recall_at_100
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  - type: recall_at_1000
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  - type: recall_at_3
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  - type: recall_at_5
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1116
  - task:
1117
  type: Retrieval
1118
  dataset:
 
1123
  revision: None
1124
  metrics:
1125
  - type: map_at_1
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  - type: map_at_10
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  - type: recall_at_5
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1186
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1187
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1192
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1193
  metrics:
1194
  - type: map_at_1
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  - type: map_at_10
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  - type: recall_at_1000
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  - type: recall_at_3
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  - type: recall_at_5
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1255
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1256
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1261
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  - type: accuracy
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  dataset:
 
1274
  revision: None
1275
  metrics:
1276
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  - type: map_at_10
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1337
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1338
  dataset:
 
1343
  revision: None
1344
  metrics:
1345
  - type: map_at_1
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  - type: map_at_10
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1406
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1407
  dataset:
 
1412
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1413
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1414
  - type: map_at_1
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1475
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1476
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1481
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  dataset:
 
1496
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1497
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1560
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1565
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1578
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1591
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1604
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1617
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1628
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1639
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  dataset:
 
1652
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1721
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  - type: ndcg_at_1000
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  - type: ndcg_at_3
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  - type: ndcg_at_5
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  - type: precision_at_100
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  - type: precision_at_1000
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  - type: precision_at_3
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  - type: precision_at_5
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  - type: recall_at_1
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  - type: recall_at_10
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  - type: recall_at_100
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  - type: recall_at_1000
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  - type: recall_at_3
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1781
  - type: recall_at_5
1782
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1783
  - task:
1784
  type: Retrieval
1785
  dataset:
 
1790
  revision: None
1791
  metrics:
1792
  - type: map_at_1
1793
+ value: 69.926
1794
  - type: map_at_10
1795
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1796
  - type: map_at_100
1797
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1798
  - type: map_at_1000
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1800
  - type: map_at_3
1801
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  - type: map_at_5
1803
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1804
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1805
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1807
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1824
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1830
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1832
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1834
  - type: precision_at_1000
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1836
  - type: precision_at_3
1837
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1838
  - type: precision_at_5
1839
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  - type: recall_at_1
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  - type: recall_at_10
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  - type: recall_at_1000
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1848
  - type: recall_at_3
1849
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  - type: recall_at_5
1851
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1852
  - task:
1853
  type: Clustering
1854
  dataset:
 
1859
  revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
1860
  metrics:
1861
  - type: v_measure
1862
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  - task:
1864
  type: Clustering
1865
  dataset:
 
1870
  revision: 282350215ef01743dc01b456c7f5241fa8937f16
1871
  metrics:
1872
  - type: v_measure
1873
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1874
  - task:
1875
  type: Retrieval
1876
  dataset:
 
1881
  revision: None
1882
  metrics:
1883
  - type: map_at_1
1884
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1885
  - type: map_at_10
1886
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1887
  - type: map_at_100
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1889
  - type: map_at_1000
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  - type: map_at_3
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  - type: map_at_5
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  - type: mrr_at_1
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1897
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1898
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1899
  - type: mrr_at_100
1900
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1901
  - type: mrr_at_1000
1902
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1903
  - type: mrr_at_3
1904
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1905
  - type: mrr_at_5
1906
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1907
  - type: ndcg_at_1
1908
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1909
  - type: ndcg_at_10
1910
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1911
  - type: ndcg_at_100
1912
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1913
  - type: ndcg_at_1000
1914
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1915
  - type: ndcg_at_3
1916
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1917
  - type: ndcg_at_5
1918
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1919
  - type: precision_at_1
1920
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1921
  - type: precision_at_10
1922
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1923
  - type: precision_at_100
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1925
  - type: precision_at_1000
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1927
  - type: precision_at_3
1928
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1929
  - type: precision_at_5
1930
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1931
  - type: recall_at_1
1932
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1933
  - type: recall_at_10
1934
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1935
  - type: recall_at_100
1936
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1937
  - type: recall_at_1000
1938
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1939
  - type: recall_at_3
1940
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1941
  - type: recall_at_5
1942
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1943
  - task:
1944
  type: STS
1945
  dataset:
 
1950
  revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
1951
  metrics:
1952
  - type: cos_sim_pearson
1953
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1954
  - type: cos_sim_spearman
1955
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1956
  - type: euclidean_pearson
1957
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1958
  - type: euclidean_spearman
1959
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1960
  - type: manhattan_pearson
1961
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1962
  - type: manhattan_spearman
1963
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1964
  - task:
1965
  type: STS
1966
  dataset:
 
1971
  revision: a0d554a64d88156834ff5ae9920b964011b16384
1972
  metrics:
1973
  - type: cos_sim_pearson
1974
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1975
  - type: cos_sim_spearman
1976
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1977
  - type: euclidean_pearson
1978
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1979
  - type: euclidean_spearman
1980
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1981
  - type: manhattan_pearson
1982
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1983
  - type: manhattan_spearman
1984
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1985
  - task:
1986
  type: STS
1987
  dataset:
 
1992
  revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
1993
  metrics:
1994
  - type: cos_sim_pearson
1995
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1996
  - type: cos_sim_spearman
1997
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1998
  - type: euclidean_pearson
1999
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2000
  - type: euclidean_spearman
2001
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2002
  - type: manhattan_pearson
2003
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2004
  - type: manhattan_spearman
2005
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2006
  - task:
2007
  type: STS
2008
  dataset:
 
2013
  revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
2014
  metrics:
2015
  - type: cos_sim_pearson
2016
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2017
  - type: cos_sim_spearman
2018
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2019
  - type: euclidean_pearson
2020
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2021
  - type: euclidean_spearman
2022
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2023
  - type: manhattan_pearson
2024
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2025
  - type: manhattan_spearman
2026
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2027
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2028
  type: STS
2029
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2034
  revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
2035
  metrics:
2036
  - type: cos_sim_pearson
2037
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2038
  - type: cos_sim_spearman
2039
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2040
  - type: euclidean_pearson
2041
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2042
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2043
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2044
  - type: manhattan_pearson
2045
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2046
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2047
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2048
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2049
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2050
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2055
  revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
2056
  metrics:
2057
  - type: cos_sim_pearson
2058
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2059
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2060
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2061
  - type: euclidean_pearson
2062
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2063
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2065
  - type: manhattan_pearson
2066
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2067
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2068
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2069
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2070
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2071
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2076
  revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
2077
  metrics:
2078
  - type: cos_sim_pearson
2079
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2080
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2081
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2082
  - type: euclidean_pearson
2083
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2084
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2085
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2086
  - type: manhattan_pearson
2087
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2088
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2089
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2090
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2091
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2092
  dataset:
 
2097
  revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
2098
  metrics:
2099
  - type: cos_sim_pearson
2100
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2101
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2102
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2103
  - type: euclidean_pearson
2104
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2105
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2106
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2107
  - type: manhattan_pearson
2108
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2109
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2110
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2111
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2112
  type: STS
2113
  dataset:
 
2118
  revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
2119
  metrics:
2120
  - type: cos_sim_pearson
2121
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2122
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2123
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2124
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2125
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2126
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2127
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2128
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2129
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2130
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2131
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2132
  - task:
2133
  type: Reranking
2134
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2139
  revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
2140
  metrics:
2141
  - type: map
2142
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2143
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2144
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2145
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2146
  type: Retrieval
2147
  dataset:
 
2152
  revision: None
2153
  metrics:
2154
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2155
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2156
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2157
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2158
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2161
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2162
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2163
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2164
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2165
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2166
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2167
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2168
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2172
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2174
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2175
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2176
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2177
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2178
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2179
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2180
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2182
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2184
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2186
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2187
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2188
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2189
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2190
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2191
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2192
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2193
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2194
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2195
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2196
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2197
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2198
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2199
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2200
  - type: precision_at_5
2201
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2202
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2203
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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
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2211
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2212
  - type: recall_at_5
2213
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2214
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2215
  type: PairClassification
2216
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2221
  revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2222
  metrics:
2223
  - type: cos_sim_accuracy
2224
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2225
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2231
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2233
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2235
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2236
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2237
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2239
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2240
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2241
  - type: dot_recall
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2243
  - type: euclidean_accuracy
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2245
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2246
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2247
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2248
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2249
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2251
  - type: euclidean_recall
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2253
  - type: manhattan_accuracy
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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
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2260
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2261
  - type: manhattan_recall
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2263
  - type: max_accuracy
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2267
  - type: max_f1
2268
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2269
  - task:
2270
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2271
  dataset:
 
2276
  revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2277
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2278
  - type: v_measure
2279
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2280
  - task:
2281
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2282
  dataset:
 
2287
  revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2288
  metrics:
2289
  - type: v_measure
2290
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2291
  - task:
2292
  type: Reranking
2293
  dataset:
 
2298
  revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2299
  metrics:
2300
  - type: map
2301
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2302
  - type: mrr
2303
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2304
  - task:
2305
  type: Summarization
2306
  dataset:
 
2311
  revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2312
  metrics:
2313
  - type: cos_sim_pearson
2314
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  - type: cos_sim_spearman
2316
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  - type: dot_pearson
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2319
  - type: dot_spearman
2320
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2321
  - task:
2322
  type: Retrieval
2323
  dataset:
 
2328
  revision: None
2329
  metrics:
2330
  - type: map_at_1
2331
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2332
  - type: map_at_10
2333
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2334
  - type: map_at_100
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2336
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2337
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2339
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2341
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2345
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  - type: mrr_at_1000
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2350
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2354
  - type: ndcg_at_1
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2358
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2362
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2364
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2366
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2370
  - type: precision_at_100
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  - type: precision_at_1000
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2374
  - type: precision_at_3
2375
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  - type: precision_at_5
2377
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2378
  - type: recall_at_1
2379
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2380
  - type: recall_at_10
2381
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2382
  - type: recall_at_100
2383
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2384
  - type: recall_at_1000
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2386
  - type: recall_at_3
2387
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  - type: recall_at_5
2389
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2390
  - task:
2391
  type: Retrieval
2392
  dataset:
 
2397
  revision: None
2398
  metrics:
2399
  - type: map_at_1
2400
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2401
  - type: map_at_10
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  - type: map_at_1000
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  - type: map_at_3
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+ value: 3.6450000000000005
2409
  - type: map_at_5
2410
+ value: 4.93
2411
  - type: mrr_at_1
2412
+ value: 18.367
2413
  - type: mrr_at_10
2414
+ value: 32.576
2415
  - type: mrr_at_100
2416
+ value: 34.163
2417
  - type: mrr_at_1000
2418
+ value: 34.18
2419
  - type: mrr_at_3
2420
+ value: 28.571
2421
  - type: mrr_at_5
2422
+ value: 30.918
2423
  - type: ndcg_at_1
2424
+ value: 15.306000000000001
2425
  - type: ndcg_at_10
2426
+ value: 18.59
2427
  - type: ndcg_at_100
2428
+ value: 30.394
2429
  - type: ndcg_at_1000
2430
+ value: 42.198
2431
  - type: ndcg_at_3
2432
+ value: 18.099
2433
  - type: ndcg_at_5
2434
+ value: 16.955000000000002
2435
  - type: precision_at_1
2436
+ value: 16.326999999999998
2437
  - type: precision_at_10
2438
+ value: 17.959
2439
  - type: precision_at_100
2440
+ value: 6.755
2441
  - type: precision_at_1000
2442
+ value: 1.4529999999999998
2443
  - type: precision_at_3
2444
  value: 20.408
2445
  - type: precision_at_5
2446
+ value: 18.367
2447
  - type: recall_at_1
2448
+ value: 1.4040000000000001
2449
  - type: recall_at_10
2450
+ value: 14.048
2451
  - type: recall_at_100
2452
+ value: 42.150999999999996
2453
  - type: recall_at_1000
2454
+ value: 77.85600000000001
2455
  - type: recall_at_3
2456
+ value: 4.819
2457
  - type: recall_at_5
2458
+ value: 7.13
2459
  - task:
2460
  type: Classification
2461
  dataset:
 
2466
  revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2467
  metrics:
2468
  - type: accuracy
2469
+ value: 66.1456
2470
  - type: ap
2471
+ value: 11.631023858569064
2472
  - type: f1
2473
+ value: 50.128196455722254
2474
  - task:
2475
  type: Classification
2476
  dataset:
 
2481
  revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2482
  metrics:
2483
  - type: accuracy
2484
+ value: 56.850594227504246
2485
  - type: f1
2486
+ value: 56.82313689360827
2487
  - task:
2488
  type: Clustering
2489
  dataset:
 
2494
  revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2495
  metrics:
2496
  - type: v_measure
2497
+ value: 38.060423744064764
2498
  - task:
2499
  type: PairClassification
2500
  dataset:
 
2505
  revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2506
  metrics:
2507
  - type: cos_sim_accuracy
2508
+ value: 84.43702688204088
2509
  - type: cos_sim_ap
2510
+ value: 68.30176948820142
2511
  - type: cos_sim_f1
2512
+ value: 64.25430330443524
2513
  - type: cos_sim_precision
2514
+ value: 61.33365315423362
2515
  - type: cos_sim_recall
2516
+ value: 67.46701846965699
2517
  - type: dot_accuracy
2518
+ value: 77.76718126005842
2519
  - type: dot_ap
2520
+ value: 37.510516716176305
2521
  - type: dot_f1
2522
+ value: 43.53859496964441
2523
  - type: dot_precision
2524
+ value: 32.428940568475454
2525
  - type: dot_recall
2526
+ value: 66.2269129287599
2527
  - type: euclidean_accuracy
2528
+ value: 82.10049472492102
2529
  - type: euclidean_ap
2530
+ value: 61.64354520687271
2531
  - type: euclidean_f1
2532
+ value: 59.804144841721694
2533
  - type: euclidean_precision
2534
+ value: 52.604166666666664
2535
  - type: euclidean_recall
2536
+ value: 69.28759894459104
2537
  - type: manhattan_accuracy
2538
+ value: 82.22566609048101
2539
  - type: manhattan_ap
2540
+ value: 61.753431124879974
2541
  - type: manhattan_f1
2542
+ value: 59.77735297424941
2543
  - type: manhattan_precision
2544
+ value: 52.0870076425632
2545
  - type: manhattan_recall
2546
+ value: 70.13192612137203
2547
  - type: max_accuracy
2548
+ value: 84.43702688204088
2549
  - type: max_ap
2550
+ value: 68.30176948820142
2551
  - type: max_f1
2552
+ value: 64.25430330443524
2553
  - task:
2554
  type: PairClassification
2555
  dataset:
 
2560
  revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2561
  metrics:
2562
  - type: cos_sim_accuracy
2563
+ value: 88.81515116233942
2564
  - type: cos_sim_ap
2565
+ value: 85.33305785100573
2566
  - type: cos_sim_f1
2567
+ value: 78.11202938475667
2568
  - type: cos_sim_precision
2569
+ value: 74.68567816253424
2570
  - type: cos_sim_recall
2571
+ value: 81.86787804126887
2572
  - type: dot_accuracy
2573
+ value: 82.50475414289595
2574
  - type: dot_ap
2575
+ value: 69.87015340174045
2576
  - type: dot_f1
2577
+ value: 65.94174480373633
2578
  - type: dot_precision
2579
+ value: 61.40362525728703
2580
  - type: dot_recall
2581
+ value: 71.20418848167539
2582
  - type: euclidean_accuracy
2583
+ value: 83.05778709201692
2584
  - type: euclidean_ap
2585
+ value: 70.54206653977498
2586
  - type: euclidean_f1
2587
+ value: 62.98969847356943
2588
  - type: euclidean_precision
2589
+ value: 61.55033063923585
2590
  - type: euclidean_recall
2591
+ value: 64.49799815214044
2592
  - type: manhattan_accuracy
2593
+ value: 83.0034540303489
2594
  - type: manhattan_ap
2595
+ value: 70.53997987198404
2596
  - type: manhattan_f1
2597
+ value: 62.95875898600075
2598
  - type: manhattan_precision
2599
+ value: 61.89555125725339
2600
  - type: manhattan_recall
2601
+ value: 64.05913150600554
2602
  - type: max_accuracy
2603
+ value: 88.81515116233942
2604
  - type: max_ap
2605
+ value: 85.33305785100573
2606
  - type: max_f1
2607
+ value: 78.11202938475667
2608
  ---
2609
  ---
2610
 
 
2665
  |all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 |
2666
  |all-mpnet-base-v2|0.726|0.835|**0.78** |0.857|0.8 |**0.906**|0.513 |0.875|0.656 |
2667
  |ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685** |0.876|**0.726** |
2668
+ |jina-embedding-t-en-v1|0.717|0.773|0.731|0.829|0.777|0.860|0.482 |0.840|0.522 |
2669
+ |jina-embedding-s-en-v1|0.743|0.786|0.738|0.837|0.80|0.875|0.523 |0.857|0.524 |
2670
+ |jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.890|0.606 |0.876|0.594 |
2671
  |jina-embedding-l-en-v1|0.739|**0.844**|0.778|**0.863**|0.821|0.896|0.566 |**0.882**|0.608 |
2672
 
2673
  ## Usage
 
2719
 
2720
  ``` latex
2721
  @misc{günther2023jina,
2722
+ title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
2723
  author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
2724
  year={2023},
2725
  eprint={2307.11224},
2726
  archivePrefix={arXiv},
2727
  primaryClass={cs.CL}
2728
  }
2729
+ ```