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@@ -14,17 +14,17 @@ model-index:
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  revision: None
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  metrics:
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  - type: cos_sim_pearson
17
- value: 54.0230470968506
18
  - type: cos_sim_spearman
19
- value: 58.79814040939044
20
  - type: euclidean_pearson
21
- value: 57.456654584077896
22
  - type: euclidean_spearman
23
- value: 58.797175403338585
24
  - type: manhattan_pearson
25
- value: 57.44746868687558
26
  - type: manhattan_spearman
27
- value: 58.79066161248843
28
  - task:
29
  type: STS
30
  dataset:
@@ -35,17 +35,17 @@ model-index:
35
  revision: None
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  metrics:
37
  - type: cos_sim_pearson
38
- value: 53.52082226478944
39
  - type: cos_sim_spearman
40
- value: 57.94121517359626
41
  - type: euclidean_pearson
42
- value: 61.160779395243715
43
  - type: euclidean_spearman
44
- value: 57.941233187487526
45
  - type: manhattan_pearson
46
- value: 61.15878861542791
47
  - type: manhattan_spearman
48
- value: 57.940620715184224
49
  - task:
50
  type: Classification
51
  dataset:
@@ -56,9 +56,9 @@ model-index:
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  revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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  metrics:
58
  - type: accuracy
59
- value: 48.536
60
  - type: f1
61
- value: 46.59904838748693
62
  - task:
63
  type: STS
64
  dataset:
@@ -69,17 +69,17 @@ model-index:
69
  revision: None
70
  metrics:
71
  - type: cos_sim_pearson
72
- value: 68.2618929828169
73
  - type: cos_sim_spearman
74
- value: 70.36192476428671
75
  - type: euclidean_pearson
76
- value: 69.41572660105302
77
  - type: euclidean_spearman
78
- value: 70.36195940530303
79
  - type: manhattan_pearson
80
- value: 69.39132691183522
81
  - type: manhattan_spearman
82
- value: 70.34010092400209
83
  - task:
84
  type: Clustering
85
  dataset:
@@ -90,7 +90,7 @@ model-index:
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  revision: None
91
  metrics:
92
  - type: v_measure
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- value: 47.08423639165573
94
  - task:
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  type: Clustering
96
  dataset:
@@ -101,7 +101,7 @@ model-index:
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  revision: None
102
  metrics:
103
  - type: v_measure
104
- value: 44.05176471944069
105
  - task:
106
  type: Reranking
107
  dataset:
@@ -112,9 +112,9 @@ model-index:
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  revision: None
113
  metrics:
114
  - type: map
115
- value: 88.57151543594188
116
  - type: mrr
117
- value: 90.61761904761904
118
  - task:
119
  type: Reranking
120
  dataset:
@@ -125,9 +125,9 @@ model-index:
125
  revision: None
126
  metrics:
127
  - type: map
128
- value: 88.89654770110027
129
  - type: mrr
130
- value: 90.90099206349205
131
  - task:
132
  type: Retrieval
133
  dataset:
@@ -138,65 +138,65 @@ model-index:
138
  revision: None
139
  metrics:
140
  - type: map_at_1
141
- value: 26.924999999999997
142
  - type: map_at_10
143
- value: 40.0
144
  - type: map_at_100
145
- value: 41.911
146
  - type: map_at_1000
147
- value: 42.013
148
  - type: map_at_3
149
- value: 35.408
150
  - type: map_at_5
151
- value: 38.025999999999996
152
  - type: mrr_at_1
153
- value: 40.685
154
  - type: mrr_at_10
155
- value: 48.837
156
  - type: mrr_at_100
157
- value: 49.823
158
  - type: mrr_at_1000
159
- value: 49.863
160
  - type: mrr_at_3
161
- value: 46.141
162
  - type: mrr_at_5
163
- value: 47.707
164
  - type: ndcg_at_1
165
- value: 40.685
166
  - type: ndcg_at_10
167
- value: 46.768
168
  - type: ndcg_at_100
169
- value: 53.996
170
  - type: ndcg_at_1000
171
- value: 55.694
172
  - type: ndcg_at_3
173
- value: 40.993
174
  - type: ndcg_at_5
175
- value: 43.553999999999995
176
  - type: precision_at_1
177
- value: 40.685
178
  - type: precision_at_10
179
- value: 10.42
180
  - type: precision_at_100
181
- value: 1.625
182
  - type: precision_at_1000
183
  value: 0.184
184
  - type: precision_at_3
185
- value: 23.105999999999998
186
  - type: precision_at_5
187
- value: 16.994
188
  - type: recall_at_1
189
- value: 26.924999999999997
190
  - type: recall_at_10
191
- value: 57.818999999999996
192
  - type: recall_at_100
193
- value: 87.4
194
  - type: recall_at_1000
195
- value: 98.721
196
  - type: recall_at_3
197
- value: 40.732
198
  - type: recall_at_5
199
- value: 48.359
200
  - task:
201
  type: PairClassification
202
  dataset:
@@ -207,51 +207,51 @@ model-index:
207
  revision: None
208
  metrics:
209
  - type: cos_sim_accuracy
210
- value: 81.10449377368705
211
  - type: cos_sim_ap
212
- value: 85.15661533065932
213
  - type: cos_sim_f1
214
- value: 82.98479087452472
215
  - type: cos_sim_precision
216
- value: 75.45375972342264
217
  - type: cos_sim_recall
218
- value: 92.18585005279832
219
  - type: dot_accuracy
220
- value: 81.10449377368705
221
  - type: dot_ap
222
- value: 85.1383107430588
223
  - type: dot_f1
224
- value: 82.98479087452472
225
  - type: dot_precision
226
- value: 75.45375972342264
227
  - type: dot_recall
228
- value: 92.18585005279832
229
  - type: euclidean_accuracy
230
- value: 81.10449377368705
231
  - type: euclidean_ap
232
- value: 85.15515844935794
233
  - type: euclidean_f1
234
- value: 82.96860133206471
235
  - type: euclidean_precision
236
- value: 75.4978354978355
237
  - type: euclidean_recall
238
- value: 92.08025343189018
239
  - type: manhattan_accuracy
240
- value: 81.10449377368705
241
  - type: manhattan_ap
242
- value: 85.1260027067541
243
  - type: manhattan_f1
244
- value: 82.93384467881111
245
  - type: manhattan_precision
246
- value: 75.94381035996489
247
  - type: manhattan_recall
248
- value: 91.34107708553326
249
  - type: max_accuracy
250
- value: 81.10449377368705
251
  - type: max_ap
252
- value: 85.15661533065932
253
  - type: max_f1
254
- value: 82.98479087452472
255
  - task:
256
  type: Retrieval
257
  dataset:
@@ -262,65 +262,65 @@ model-index:
262
  revision: None
263
  metrics:
264
  - type: map_at_1
265
- value: 68.44
266
  - type: map_at_10
267
- value: 77.571
268
  - type: map_at_100
269
- value: 77.834
270
  - type: map_at_1000
271
- value: 77.838
272
  - type: map_at_3
273
- value: 75.913
274
  - type: map_at_5
275
- value: 76.942
276
  - type: mrr_at_1
277
- value: 68.809
278
  - type: mrr_at_10
279
- value: 77.656
280
  - type: mrr_at_100
281
- value: 77.912
282
  - type: mrr_at_1000
283
- value: 77.916
284
  - type: mrr_at_3
285
- value: 76.063
286
  - type: mrr_at_5
287
- value: 77.074
288
  - type: ndcg_at_1
289
- value: 68.704
290
  - type: ndcg_at_10
291
- value: 81.524
292
  - type: ndcg_at_100
293
- value: 82.72
294
  - type: ndcg_at_1000
295
- value: 82.826
296
  - type: ndcg_at_3
297
- value: 78.251
298
  - type: ndcg_at_5
299
- value: 80.075
300
  - type: precision_at_1
301
- value: 68.704
302
  - type: precision_at_10
303
- value: 9.463000000000001
304
  - type: precision_at_100
305
- value: 1.001
306
  - type: precision_at_1000
307
  value: 0.101
308
  - type: precision_at_3
309
- value: 28.486
310
  - type: precision_at_5
311
- value: 18.019
312
  - type: recall_at_1
313
- value: 68.44
314
  - type: recall_at_10
315
- value: 93.625
316
  - type: recall_at_100
317
- value: 99.05199999999999
318
  - type: recall_at_1000
319
- value: 99.895
320
  - type: recall_at_3
321
- value: 84.879
322
  - type: recall_at_5
323
- value: 89.252
324
  - task:
325
  type: Retrieval
326
  dataset:
@@ -331,65 +331,65 @@ model-index:
331
  revision: None
332
  metrics:
333
  - type: map_at_1
334
- value: 25.653
335
  - type: map_at_10
336
- value: 79.118
337
  - type: map_at_100
338
- value: 81.917
339
  - type: map_at_1000
340
- value: 81.962
341
  - type: map_at_3
342
- value: 54.56999999999999
343
  - type: map_at_5
344
- value: 69.246
345
  - type: mrr_at_1
346
- value: 89.35
347
  - type: mrr_at_10
348
- value: 92.69
349
  - type: mrr_at_100
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- value: 92.77
351
  - type: mrr_at_1000
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- value: 92.77300000000001
353
  - type: mrr_at_3
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- value: 92.425
355
  - type: mrr_at_5
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- value: 92.575
357
  - type: ndcg_at_1
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- value: 89.35
359
  - type: ndcg_at_10
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- value: 86.558
361
  - type: ndcg_at_100
362
- value: 89.36
363
  - type: ndcg_at_1000
364
- value: 89.789
365
  - type: ndcg_at_3
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- value: 85.419
367
  - type: ndcg_at_5
368
- value: 84.52
369
  - type: precision_at_1
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- value: 89.35
371
  - type: precision_at_10
372
- value: 41.589999999999996
373
  - type: precision_at_100
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- value: 4.781
375
  - type: precision_at_1000
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- value: 0.488
377
  - type: precision_at_3
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- value: 76.717
379
  - type: precision_at_5
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- value: 65.08
381
  - type: recall_at_1
382
- value: 25.653
383
  - type: recall_at_10
384
- value: 87.651
385
  - type: recall_at_100
386
- value: 96.858
387
  - type: recall_at_1000
388
- value: 99.13300000000001
389
  - type: recall_at_3
390
- value: 56.901
391
  - type: recall_at_5
392
- value: 74.054
393
  - task:
394
  type: Retrieval
395
  dataset:
@@ -400,65 +400,65 @@ model-index:
400
  revision: None
401
  metrics:
402
  - type: map_at_1
403
- value: 52.0
404
  - type: map_at_10
405
- value: 62.576
406
  - type: map_at_100
407
- value: 63.073
408
  - type: map_at_1000
409
- value: 63.09
410
  - type: map_at_3
411
- value: 60.25
412
  - type: map_at_5
413
- value: 61.73
414
  - type: mrr_at_1
415
- value: 52.0
416
  - type: mrr_at_10
417
- value: 62.576
418
  - type: mrr_at_100
419
- value: 63.073
420
  - type: mrr_at_1000
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- value: 63.09
422
  - type: mrr_at_3
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- value: 60.25
424
  - type: mrr_at_5
425
- value: 61.73
426
  - type: ndcg_at_1
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- value: 52.0
428
  - type: ndcg_at_10
429
- value: 67.535
430
  - type: ndcg_at_100
431
- value: 69.918
432
  - type: ndcg_at_1000
433
- value: 70.303
434
  - type: ndcg_at_3
435
- value: 62.839
436
  - type: ndcg_at_5
437
- value: 65.511
438
  - type: precision_at_1
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- value: 52.0
440
  - type: precision_at_10
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- value: 8.3
442
  - type: precision_at_100
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- value: 0.941
444
  - type: precision_at_1000
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  value: 0.097
446
  - type: precision_at_3
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- value: 23.433
448
  - type: precision_at_5
449
- value: 15.36
450
  - type: recall_at_1
451
- value: 52.0
452
  - type: recall_at_10
453
- value: 83.0
454
  - type: recall_at_100
455
- value: 94.1
456
  - type: recall_at_1000
457
- value: 97.0
458
  - type: recall_at_3
459
- value: 70.3
460
  - type: recall_at_5
461
- value: 76.8
462
  - task:
463
  type: Classification
464
  dataset:
@@ -469,9 +469,9 @@ model-index:
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  revision: None
470
  metrics:
471
  - type: accuracy
472
- value: 51.76606387071951
473
  - type: f1
474
- value: 40.31516560726137
475
  - task:
476
  type: Classification
477
  dataset:
@@ -482,11 +482,11 @@ model-index:
482
  revision: None
483
  metrics:
484
  - type: accuracy
485
- value: 86.69793621013133
486
  - type: ap
487
- value: 55.46718958939327
488
  - type: f1
489
- value: 81.48228915952436
490
  - task:
491
  type: STS
492
  dataset:
@@ -497,17 +497,17 @@ model-index:
497
  revision: None
498
  metrics:
499
  - type: cos_sim_pearson
500
- value: 71.13474501946303
501
  - type: cos_sim_spearman
502
- value: 78.1761029193962
503
  - type: euclidean_pearson
504
- value: 77.48482435905522
505
  - type: euclidean_spearman
506
- value: 78.17607188104564
507
  - type: manhattan_pearson
508
- value: 77.47331798992634
509
  - type: manhattan_spearman
510
- value: 78.1667116013234
511
  - task:
512
  type: Reranking
513
  dataset:
@@ -518,9 +518,9 @@ model-index:
518
  revision: None
519
  metrics:
520
  - type: map
521
- value: 28.23811014679616
522
  - type: mrr
523
- value: 27.34960317460317
524
  - task:
525
  type: Retrieval
526
  dataset:
@@ -531,65 +531,65 @@ model-index:
531
  revision: None
532
  metrics:
533
  - type: map_at_1
534
- value: 65.697
535
  - type: map_at_10
536
- value: 74.778
537
  - type: map_at_100
538
- value: 75.117
539
  - type: map_at_1000
540
- value: 75.129
541
  - type: map_at_3
542
- value: 73.029
543
  - type: map_at_5
544
- value: 74.148
545
  - type: mrr_at_1
546
- value: 67.96600000000001
547
  - type: mrr_at_10
548
- value: 75.405
549
  - type: mrr_at_100
550
- value: 75.703
551
  - type: mrr_at_1000
552
- value: 75.714
553
  - type: mrr_at_3
554
- value: 73.892
555
  - type: mrr_at_5
556
- value: 74.86
557
  - type: ndcg_at_1
558
- value: 67.96600000000001
559
  - type: ndcg_at_10
560
- value: 78.444
561
  - type: ndcg_at_100
562
- value: 79.971
563
  - type: ndcg_at_1000
564
- value: 80.284
565
  - type: ndcg_at_3
566
- value: 75.13300000000001
567
  - type: ndcg_at_5
568
- value: 77.018
569
  - type: precision_at_1
570
- value: 67.96600000000001
571
  - type: precision_at_10
572
- value: 9.474
573
  - type: precision_at_100
574
- value: 1.023
575
  - type: precision_at_1000
576
  value: 0.105
577
  - type: precision_at_3
578
- value: 28.328999999999997
579
  - type: precision_at_5
580
- value: 17.989
581
  - type: recall_at_1
582
- value: 65.697
583
  - type: recall_at_10
584
- value: 89.09899999999999
585
  - type: recall_at_100
586
- value: 96.023
587
  - type: recall_at_1000
588
- value: 98.441
589
  - type: recall_at_3
590
- value: 80.343
591
  - type: recall_at_5
592
- value: 84.824
593
  - task:
594
  type: Classification
595
  dataset:
@@ -600,9 +600,9 @@ model-index:
600
  revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
601
  metrics:
602
  - type: accuracy
603
- value: 75.86751849361129
604
  - type: f1
605
- value: 73.04392353740805
606
  - task:
607
  type: Classification
608
  dataset:
@@ -613,9 +613,9 @@ model-index:
613
  revision: 7d571f92784cd94a019292a1f45445077d0ef634
614
  metrics:
615
  - type: accuracy
616
- value: 78.43308675184936
617
  - type: f1
618
- value: 78.10822258565513
619
  - task:
620
  type: Retrieval
621
  dataset:
@@ -626,65 +626,65 @@ model-index:
626
  revision: None
627
  metrics:
628
  - type: map_at_1
629
- value: 54.900000000000006
630
  - type: map_at_10
631
- value: 60.99
632
  - type: map_at_100
633
- value: 61.556
634
  - type: map_at_1000
635
- value: 61.598
636
  - type: map_at_3
637
- value: 59.516999999999996
638
  - type: map_at_5
639
- value: 60.327
640
  - type: mrr_at_1
641
- value: 55.00000000000001
642
  - type: mrr_at_10
643
- value: 61.040000000000006
644
  - type: mrr_at_100
645
- value: 61.60600000000001
646
  - type: mrr_at_1000
647
- value: 61.648
648
  - type: mrr_at_3
649
- value: 59.567
650
  - type: mrr_at_5
651
- value: 60.377
652
  - type: ndcg_at_1
653
- value: 54.900000000000006
654
  - type: ndcg_at_10
655
- value: 64.021
656
  - type: ndcg_at_100
657
- value: 67.002
658
  - type: ndcg_at_1000
659
- value: 68.21300000000001
660
  - type: ndcg_at_3
661
- value: 60.955999999999996
662
  - type: ndcg_at_5
663
- value: 62.427
664
  - type: precision_at_1
665
- value: 54.900000000000006
666
  - type: precision_at_10
667
- value: 7.359999999999999
668
  - type: precision_at_100
669
- value: 0.881
670
  - type: precision_at_1000
671
  value: 0.098
672
  - type: precision_at_3
673
- value: 21.7
674
  - type: precision_at_5
675
- value: 13.74
676
  - type: recall_at_1
677
- value: 54.900000000000006
678
  - type: recall_at_10
679
- value: 73.6
680
  - type: recall_at_100
681
- value: 88.1
682
  - type: recall_at_1000
683
- value: 97.8
684
  - type: recall_at_3
685
- value: 65.10000000000001
686
  - type: recall_at_5
687
- value: 68.7
688
  - task:
689
  type: Classification
690
  dataset:
@@ -695,9 +695,9 @@ model-index:
695
  revision: None
696
  metrics:
697
  - type: accuracy
698
- value: 77.52333333333333
699
  - type: f1
700
- value: 77.49510023279737
701
  - task:
702
  type: PairClassification
703
  dataset:
@@ -708,51 +708,51 @@ model-index:
708
  revision: None
709
  metrics:
710
  - type: cos_sim_accuracy
711
- value: 81.10449377368705
712
  - type: cos_sim_ap
713
- value: 85.15661533065932
714
  - type: cos_sim_f1
715
- value: 82.98479087452472
716
  - type: cos_sim_precision
717
- value: 75.45375972342264
718
  - type: cos_sim_recall
719
- value: 92.18585005279832
720
  - type: dot_accuracy
721
- value: 81.10449377368705
722
  - type: dot_ap
723
- value: 85.1383107430588
724
  - type: dot_f1
725
- value: 82.98479087452472
726
  - type: dot_precision
727
- value: 75.45375972342264
728
  - type: dot_recall
729
- value: 92.18585005279832
730
  - type: euclidean_accuracy
731
- value: 81.10449377368705
732
  - type: euclidean_ap
733
- value: 85.15515844935794
734
  - type: euclidean_f1
735
- value: 82.96860133206471
736
  - type: euclidean_precision
737
- value: 75.4978354978355
738
  - type: euclidean_recall
739
- value: 92.08025343189018
740
  - type: manhattan_accuracy
741
- value: 81.10449377368705
742
  - type: manhattan_ap
743
- value: 85.1260027067541
744
  - type: manhattan_f1
745
- value: 82.93384467881111
746
  - type: manhattan_precision
747
- value: 75.94381035996489
748
  - type: manhattan_recall
749
- value: 91.34107708553326
750
  - type: max_accuracy
751
- value: 81.10449377368705
752
  - type: max_ap
753
- value: 85.15661533065932
754
  - type: max_f1
755
- value: 82.98479087452472
756
  - task:
757
  type: Classification
758
  dataset:
@@ -763,11 +763,11 @@ model-index:
763
  revision: None
764
  metrics:
765
  - type: accuracy
766
- value: 93.71000000000001
767
  - type: ap
768
- value: 91.83202232349356
769
  - type: f1
770
- value: 93.69900560334331
771
  - task:
772
  type: STS
773
  dataset:
@@ -778,17 +778,17 @@ model-index:
778
  revision: None
779
  metrics:
780
  - type: cos_sim_pearson
781
- value: 39.12276072181213
782
  - type: cos_sim_spearman
783
- value: 45.47002950875883
784
  - type: euclidean_pearson
785
- value: 44.82137309399524
786
  - type: euclidean_spearman
787
- value: 45.46811344101213
788
  - type: manhattan_pearson
789
- value: 44.828762191664694
790
  - type: manhattan_spearman
791
- value: 45.48971275015666
792
  - task:
793
  type: STS
794
  dataset:
@@ -799,17 +799,17 @@ model-index:
799
  revision: None
800
  metrics:
801
  - type: cos_sim_pearson
802
- value: 34.24312789444651
803
  - type: cos_sim_spearman
804
- value: 37.31008735675408
805
  - type: euclidean_pearson
806
- value: 35.12756687070336
807
  - type: euclidean_spearman
808
- value: 37.30862222238361
809
  - type: manhattan_pearson
810
- value: 35.08734520176313
811
  - type: manhattan_spearman
812
- value: 37.25663009244228
813
  - task:
814
  type: STS
815
  dataset:
@@ -820,17 +820,17 @@ model-index:
820
  revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
821
  metrics:
822
  - type: cos_sim_pearson
823
- value: 61.30477884469
824
  - type: cos_sim_spearman
825
- value: 65.86475705722457
826
  - type: euclidean_pearson
827
- value: 64.06391333268994
828
  - type: euclidean_spearman
829
- value: 65.86335860593772
830
  - type: manhattan_pearson
831
- value: 64.01127637711839
832
  - type: manhattan_spearman
833
- value: 65.87720920117906
834
  - task:
835
  type: STS
836
  dataset:
@@ -841,17 +841,17 @@ model-index:
841
  revision: None
842
  metrics:
843
  - type: cos_sim_pearson
844
- value: 81.55425529217757
845
  - type: cos_sim_spearman
846
- value: 82.72308414906433
847
  - type: euclidean_pearson
848
- value: 82.4891240865874
849
  - type: euclidean_spearman
850
- value: 82.72120335429314
851
  - type: manhattan_pearson
852
- value: 82.4768946184714
853
  - type: manhattan_spearman
854
- value: 82.71021690525912
855
  - task:
856
  type: Reranking
857
  dataset:
@@ -862,9 +862,9 @@ model-index:
862
  revision: None
863
  metrics:
864
  - type: map
865
- value: 66.62441330068313
866
  - type: mrr
867
- value: 76.4081189347777
868
  - task:
869
  type: Retrieval
870
  dataset:
@@ -875,65 +875,65 @@ model-index:
875
  revision: None
876
  metrics:
877
  - type: map_at_1
878
- value: 27.883000000000003
879
  - type: map_at_10
880
- value: 77.525
881
  - type: map_at_100
882
- value: 81.144
883
  - type: map_at_1000
884
- value: 81.21000000000001
885
  - type: map_at_3
886
- value: 54.730000000000004
887
  - type: map_at_5
888
- value: 67.136
889
  - type: mrr_at_1
890
- value: 90.527
891
  - type: mrr_at_10
892
- value: 92.971
893
  - type: mrr_at_100
894
- value: 93.052
895
  - type: mrr_at_1000
896
- value: 93.05499999999999
897
  - type: mrr_at_3
898
- value: 92.557
899
  - type: mrr_at_5
900
- value: 92.81700000000001
901
  - type: ndcg_at_1
902
- value: 90.527
903
  - type: ndcg_at_10
904
- value: 85.08200000000001
905
  - type: ndcg_at_100
906
- value: 88.65899999999999
907
  - type: ndcg_at_1000
908
- value: 89.25999999999999
909
  - type: ndcg_at_3
910
- value: 86.586
911
  - type: ndcg_at_5
912
- value: 85.14699999999999
913
  - type: precision_at_1
914
- value: 90.527
915
  - type: precision_at_10
916
- value: 42.054
917
  - type: precision_at_100
918
- value: 5.013999999999999
919
  - type: precision_at_1000
920
- value: 0.516
921
  - type: precision_at_3
922
- value: 75.55199999999999
923
  - type: precision_at_5
924
- value: 63.249
925
  - type: recall_at_1
926
- value: 27.883000000000003
927
  - type: recall_at_10
928
- value: 83.949
929
  - type: recall_at_100
930
- value: 95.568
931
  - type: recall_at_1000
932
- value: 98.547
933
  - type: recall_at_3
934
- value: 56.374
935
  - type: recall_at_5
936
- value: 70.44200000000001
937
  - task:
938
  type: Classification
939
  dataset:
@@ -944,9 +944,9 @@ model-index:
944
  revision: None
945
  metrics:
946
  - type: accuracy
947
- value: 53.689
948
  - type: f1
949
- value: 51.87029846425533
950
  - task:
951
  type: Clustering
952
  dataset:
@@ -957,7 +957,7 @@ model-index:
957
  revision: None
958
  metrics:
959
  - type: v_measure
960
- value: 75.13107992170329
961
  - task:
962
  type: Clustering
963
  dataset:
@@ -968,7 +968,7 @@ model-index:
968
  revision: None
969
  metrics:
970
  - type: v_measure
971
- value: 69.32221171095146
972
  - task:
973
  type: Retrieval
974
  dataset:
@@ -979,65 +979,65 @@ model-index:
979
  revision: None
980
  metrics:
981
  - type: map_at_1
982
- value: 59.4
983
  - type: map_at_10
984
- value: 69.199
985
  - type: map_at_100
986
- value: 69.724
987
  - type: map_at_1000
988
- value: 69.73899999999999
989
  - type: map_at_3
990
- value: 67.717
991
  - type: map_at_5
992
- value: 68.777
993
  - type: mrr_at_1
994
- value: 59.3
995
  - type: mrr_at_10
996
- value: 69.149
997
  - type: mrr_at_100
998
- value: 69.674
999
  - type: mrr_at_1000
1000
- value: 69.68900000000001
1001
  - type: mrr_at_3
1002
- value: 67.667
1003
  - type: mrr_at_5
1004
- value: 68.727
1005
  - type: ndcg_at_1
1006
- value: 59.4
1007
  - type: ndcg_at_10
1008
- value: 73.29
1009
  - type: ndcg_at_100
1010
- value: 75.589
1011
  - type: ndcg_at_1000
1012
- value: 75.985
1013
  - type: ndcg_at_3
1014
- value: 70.339
1015
  - type: ndcg_at_5
1016
- value: 72.241
1017
  - type: precision_at_1
1018
- value: 59.4
1019
  - type: precision_at_10
1020
- value: 8.58
1021
  - type: precision_at_100
1022
- value: 0.96
1023
  - type: precision_at_1000
1024
  value: 0.099
1025
  - type: precision_at_3
1026
- value: 25.967000000000002
1027
  - type: precision_at_5
1028
- value: 16.5
1029
  - type: recall_at_1
1030
- value: 59.4
1031
  - type: recall_at_10
1032
- value: 85.8
1033
  - type: recall_at_100
1034
- value: 96.0
1035
  - type: recall_at_1000
1036
- value: 99.1
1037
  - type: recall_at_3
1038
- value: 77.9
1039
  - type: recall_at_5
1040
- value: 82.5
1041
  - task:
1042
  type: Classification
1043
  dataset:
@@ -1048,11 +1048,11 @@ model-index:
1048
  revision: None
1049
  metrics:
1050
  - type: accuracy
1051
- value: 88.53
1052
  - type: ap
1053
- value: 73.56216166534062
1054
  - type: f1
1055
- value: 87.06093694294485
1056
  ---
1057
 
1058
  ### 使用方法
 
14
  revision: None
15
  metrics:
16
  - type: cos_sim_pearson
17
+ value: 57.03519449697447
18
  - type: cos_sim_spearman
19
+ value: 61.05687780613
20
  - type: euclidean_pearson
21
+ value: 59.92928475064863
22
  - type: euclidean_spearman
23
+ value: 61.05685769955894
24
  - type: manhattan_pearson
25
+ value: 59.91091069371023
26
  - type: manhattan_spearman
27
+ value: 61.01906162919386
28
  - task:
29
  type: STS
30
  dataset:
 
35
  revision: None
36
  metrics:
37
  - type: cos_sim_pearson
38
+ value: 56.81511631314823
39
  - type: cos_sim_spearman
40
+ value: 59.017410073656826
41
  - type: euclidean_pearson
42
+ value: 63.44414716754522
43
  - type: euclidean_spearman
44
+ value: 59.017407821544175
45
  - type: manhattan_pearson
46
+ value: 63.4171455580894
47
  - type: manhattan_spearman
48
+ value: 59.00005143754492
49
  - task:
50
  type: Classification
51
  dataset:
 
56
  revision: 1399c76144fd37290681b995c656ef9b2e06e26d
57
  metrics:
58
  - type: accuracy
59
+ value: 49.28
60
  - type: f1
61
+ value: 46.84433761170775
62
  - task:
63
  type: STS
64
  dataset:
 
69
  revision: None
70
  metrics:
71
  - type: cos_sim_pearson
72
+ value: 71.06047581825707
73
  - type: cos_sim_spearman
74
+ value: 72.63091479940526
75
  - type: euclidean_pearson
76
+ value: 71.33861457006756
77
  - type: euclidean_spearman
78
+ value: 72.63091479809789
79
  - type: manhattan_pearson
80
+ value: 71.3148241099811
81
  - type: manhattan_spearman
82
+ value: 72.60884847026323
83
  - task:
84
  type: Clustering
85
  dataset:
 
90
  revision: None
91
  metrics:
92
  - type: v_measure
93
+ value: 55.11593452044331
94
  - task:
95
  type: Clustering
96
  dataset:
 
101
  revision: None
102
  metrics:
103
  - type: v_measure
104
+ value: 45.0556727269734
105
  - task:
106
  type: Reranking
107
  dataset:
 
112
  revision: None
113
  metrics:
114
  - type: map
115
+ value: 88.88589952904408
116
  - type: mrr
117
+ value: 90.94142857142857
118
  - task:
119
  type: Reranking
120
  dataset:
 
125
  revision: None
126
  metrics:
127
  - type: map
128
+ value: 89.98162054042666
129
  - type: mrr
130
+ value: 92.06119047619048
131
  - task:
132
  type: Retrieval
133
  dataset:
 
138
  revision: None
139
  metrics:
140
  - type: map_at_1
141
+ value: 26.99
142
  - type: map_at_10
143
+ value: 40.187
144
  - type: map_at_100
145
+ value: 42.057
146
  - type: map_at_1000
147
+ value: 42.156
148
  - type: map_at_3
149
+ value: 35.704
150
  - type: map_at_5
151
+ value: 38.307
152
  - type: mrr_at_1
153
+ value: 40.835
154
  - type: mrr_at_10
155
+ value: 49.207
156
  - type: mrr_at_100
157
+ value: 50.163999999999994
158
  - type: mrr_at_1000
159
+ value: 50.2
160
  - type: mrr_at_3
161
+ value: 46.649
162
  - type: mrr_at_5
163
+ value: 48.082
164
  - type: ndcg_at_1
165
+ value: 40.835
166
  - type: ndcg_at_10
167
+ value: 46.976
168
  - type: ndcg_at_100
169
+ value: 54.162
170
  - type: ndcg_at_1000
171
+ value: 55.84
172
  - type: ndcg_at_3
173
+ value: 41.417
174
  - type: ndcg_at_5
175
+ value: 43.864999999999995
176
  - type: precision_at_1
177
+ value: 40.835
178
  - type: precision_at_10
179
+ value: 10.403
180
  - type: precision_at_100
181
+ value: 1.6219999999999999
182
  - type: precision_at_1000
183
  value: 0.184
184
  - type: precision_at_3
185
+ value: 23.473
186
  - type: precision_at_5
187
+ value: 17.094
188
  - type: recall_at_1
189
+ value: 26.99
190
  - type: recall_at_10
191
+ value: 57.949
192
  - type: recall_at_100
193
+ value: 87.578
194
  - type: recall_at_1000
195
+ value: 98.741
196
  - type: recall_at_3
197
+ value: 41.244
198
  - type: recall_at_5
199
+ value: 48.727
200
  - task:
201
  type: PairClassification
202
  dataset:
 
207
  revision: None
208
  metrics:
209
  - type: cos_sim_accuracy
210
+ value: 85.07516536380037
211
  - type: cos_sim_ap
212
+ value: 92.05034893565924
213
  - type: cos_sim_f1
214
+ value: 85.86387434554975
215
  - type: cos_sim_precision
216
+ value: 82.0
217
  - type: cos_sim_recall
218
+ value: 90.10989010989012
219
  - type: dot_accuracy
220
+ value: 85.07516536380037
221
  - type: dot_ap
222
+ value: 92.05615563994219
223
  - type: dot_f1
224
+ value: 85.86387434554975
225
  - type: dot_precision
226
+ value: 82.0
227
  - type: dot_recall
228
+ value: 90.10989010989012
229
  - type: euclidean_accuracy
230
+ value: 85.07516536380037
231
  - type: euclidean_ap
232
+ value: 92.05034675223959
233
  - type: euclidean_f1
234
+ value: 85.86387434554975
235
  - type: euclidean_precision
236
+ value: 82.0
237
  - type: euclidean_recall
238
+ value: 90.10989010989012
239
  - type: manhattan_accuracy
240
+ value: 85.13529765484064
241
  - type: manhattan_ap
242
+ value: 92.02926780269996
243
  - type: manhattan_f1
244
+ value: 85.87722240858771
245
  - type: manhattan_precision
246
+ value: 82.29747106729532
247
  - type: manhattan_recall
248
+ value: 89.78255786766425
249
  - type: max_accuracy
250
+ value: 85.13529765484064
251
  - type: max_ap
252
+ value: 92.05615563994219
253
  - type: max_f1
254
+ value: 85.87722240858771
255
  - task:
256
  type: Retrieval
257
  dataset:
 
262
  revision: None
263
  metrics:
264
  - type: map_at_1
265
+ value: 68.072
266
  - type: map_at_10
267
+ value: 76.31700000000001
268
  - type: map_at_100
269
+ value: 76.667
270
  - type: map_at_1000
271
+ value: 76.671
272
  - type: map_at_3
273
+ value: 74.52600000000001
274
  - type: map_at_5
275
+ value: 75.689
276
  - type: mrr_at_1
277
+ value: 68.282
278
  - type: mrr_at_10
279
+ value: 76.363
280
  - type: mrr_at_100
281
+ value: 76.685
282
  - type: mrr_at_1000
283
+ value: 76.688
284
  - type: mrr_at_3
285
+ value: 74.517
286
  - type: mrr_at_5
287
+ value: 75.75
288
  - type: ndcg_at_1
289
+ value: 68.282
290
  - type: ndcg_at_10
291
+ value: 80.123
292
  - type: ndcg_at_100
293
+ value: 81.647
294
  - type: ndcg_at_1000
295
+ value: 81.784
296
  - type: ndcg_at_3
297
+ value: 76.595
298
  - type: ndcg_at_5
299
+ value: 78.689
300
  - type: precision_at_1
301
+ value: 68.282
302
  - type: precision_at_10
303
+ value: 9.252
304
  - type: precision_at_100
305
+ value: 0.997
306
  - type: precision_at_1000
307
  value: 0.101
308
  - type: precision_at_3
309
+ value: 27.643
310
  - type: precision_at_5
311
+ value: 17.64
312
  - type: recall_at_1
313
+ value: 68.072
314
  - type: recall_at_10
315
+ value: 91.807
316
  - type: recall_at_100
317
+ value: 98.63
318
  - type: recall_at_1000
319
+ value: 99.789
320
  - type: recall_at_3
321
+ value: 82.50800000000001
322
  - type: recall_at_5
323
+ value: 87.53999999999999
324
  - task:
325
  type: Retrieval
326
  dataset:
 
331
  revision: None
332
  metrics:
333
  - type: map_at_1
334
+ value: 26.511000000000003
335
  - type: map_at_10
336
+ value: 81.28699999999999
337
  - type: map_at_100
338
+ value: 84.028
339
  - type: map_at_1000
340
+ value: 84.062
341
  - type: map_at_3
342
+ value: 56.821
343
  - type: map_at_5
344
+ value: 71.474
345
  - type: mrr_at_1
346
+ value: 91.55
347
  - type: mrr_at_10
348
+ value: 94.109
349
  - type: mrr_at_100
350
+ value: 94.182
351
  - type: mrr_at_1000
352
+ value: 94.18299999999999
353
  - type: mrr_at_3
354
+ value: 93.833
355
  - type: mrr_at_5
356
+ value: 94.041
357
  - type: ndcg_at_1
358
+ value: 91.55
359
  - type: ndcg_at_10
360
+ value: 88.24300000000001
361
  - type: ndcg_at_100
362
+ value: 90.928
363
  - type: ndcg_at_1000
364
+ value: 91.221
365
  - type: ndcg_at_3
366
+ value: 87.558
367
  - type: ndcg_at_5
368
+ value: 86.39099999999999
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  - type: precision_at_1
370
+ value: 91.55
371
  - type: precision_at_10
372
+ value: 41.959999999999994
373
  - type: precision_at_100
374
+ value: 4.812
375
  - type: precision_at_1000
376
+ value: 0.48900000000000005
377
  - type: precision_at_3
378
+ value: 78.38300000000001
379
  - type: precision_at_5
380
+ value: 66.02
381
  - type: recall_at_1
382
+ value: 26.511000000000003
383
  - type: recall_at_10
384
+ value: 88.98
385
  - type: recall_at_100
386
+ value: 97.941
387
  - type: recall_at_1000
388
+ value: 99.367
389
  - type: recall_at_3
390
+ value: 58.813
391
  - type: recall_at_5
392
+ value: 75.69500000000001
393
  - task:
394
  type: Retrieval
395
  dataset:
 
400
  revision: None
401
  metrics:
402
  - type: map_at_1
403
+ value: 52.7
404
  - type: map_at_10
405
+ value: 62.28399999999999
406
  - type: map_at_100
407
+ value: 62.827
408
  - type: map_at_1000
409
+ value: 62.842
410
  - type: map_at_3
411
+ value: 59.917
412
  - type: map_at_5
413
+ value: 61.327
414
  - type: mrr_at_1
415
+ value: 52.7
416
  - type: mrr_at_10
417
+ value: 62.28399999999999
418
  - type: mrr_at_100
419
+ value: 62.827
420
  - type: mrr_at_1000
421
+ value: 62.842
422
  - type: mrr_at_3
423
+ value: 59.917
424
  - type: mrr_at_5
425
+ value: 61.327
426
  - type: ndcg_at_1
427
+ value: 52.7
428
  - type: ndcg_at_10
429
+ value: 67.128
430
  - type: ndcg_at_100
431
+ value: 69.74900000000001
432
  - type: ndcg_at_1000
433
+ value: 70.108
434
  - type: ndcg_at_3
435
+ value: 62.251
436
  - type: ndcg_at_5
437
+ value: 64.84100000000001
438
  - type: precision_at_1
439
+ value: 52.7
440
  - type: precision_at_10
441
+ value: 8.24
442
  - type: precision_at_100
443
+ value: 0.946
444
  - type: precision_at_1000
445
  value: 0.097
446
  - type: precision_at_3
447
+ value: 23.0
448
  - type: precision_at_5
449
+ value: 15.079999999999998
450
  - type: recall_at_1
451
+ value: 52.7
452
  - type: recall_at_10
453
+ value: 82.39999999999999
454
  - type: recall_at_100
455
+ value: 94.6
456
  - type: recall_at_1000
457
+ value: 97.39999999999999
458
  - type: recall_at_3
459
+ value: 69.0
460
  - type: recall_at_5
461
+ value: 75.4
462
  - task:
463
  type: Classification
464
  dataset:
 
469
  revision: None
470
  metrics:
471
  - type: accuracy
472
+ value: 52.751058099268946
473
  - type: f1
474
+ value: 42.08257079453902
475
  - task:
476
  type: Classification
477
  dataset:
 
482
  revision: None
483
  metrics:
484
  - type: accuracy
485
+ value: 88.29268292682926
486
  - type: ap
487
+ value: 58.92380933786006
488
  - type: f1
489
+ value: 83.38194360730576
490
  - task:
491
  type: STS
492
  dataset:
 
497
  revision: None
498
  metrics:
499
  - type: cos_sim_pearson
500
+ value: 74.20476238217833
501
  - type: cos_sim_spearman
502
+ value: 79.30229178361162
503
  - type: euclidean_pearson
504
+ value: 79.24335190560299
505
  - type: euclidean_spearman
506
+ value: 79.30229178105364
507
  - type: manhattan_pearson
508
+ value: 79.22468300467371
509
  - type: manhattan_spearman
510
+ value: 79.29290711369052
511
  - task:
512
  type: Reranking
513
  dataset:
 
518
  revision: None
519
  metrics:
520
  - type: map
521
+ value: 31.85453315055195
522
  - type: mrr
523
+ value: 30.61468253968254
524
  - task:
525
  type: Retrieval
526
  dataset:
 
531
  revision: None
532
  metrics:
533
  - type: map_at_1
534
+ value: 66.671
535
  - type: map_at_10
536
+ value: 75.656
537
  - type: map_at_100
538
+ value: 75.978
539
  - type: map_at_1000
540
+ value: 75.99000000000001
541
  - type: map_at_3
542
+ value: 73.80499999999999
543
  - type: map_at_5
544
+ value: 75.023
545
  - type: mrr_at_1
546
+ value: 68.95400000000001
547
  - type: mrr_at_10
548
+ value: 76.25
549
  - type: mrr_at_100
550
+ value: 76.534
551
  - type: mrr_at_1000
552
+ value: 76.545
553
  - type: mrr_at_3
554
+ value: 74.632
555
  - type: mrr_at_5
556
+ value: 75.69500000000001
557
  - type: ndcg_at_1
558
+ value: 68.95400000000001
559
  - type: ndcg_at_10
560
+ value: 79.293
561
  - type: ndcg_at_100
562
+ value: 80.709
563
  - type: ndcg_at_1000
564
+ value: 81.00500000000001
565
  - type: ndcg_at_3
566
+ value: 75.815
567
  - type: ndcg_at_5
568
+ value: 77.861
569
  - type: precision_at_1
570
+ value: 68.95400000000001
571
  - type: precision_at_10
572
+ value: 9.559
573
  - type: precision_at_100
574
+ value: 1.026
575
  - type: precision_at_1000
576
  value: 0.105
577
  - type: precision_at_3
578
+ value: 28.486
579
  - type: precision_at_5
580
+ value: 18.178
581
  - type: recall_at_1
582
+ value: 66.671
583
  - type: recall_at_10
584
+ value: 89.904
585
  - type: recall_at_100
586
+ value: 96.243
587
  - type: recall_at_1000
588
+ value: 98.55199999999999
589
  - type: recall_at_3
590
+ value: 80.778
591
  - type: recall_at_5
592
+ value: 85.611
593
  - task:
594
  type: Classification
595
  dataset:
 
600
  revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
601
  metrics:
602
  - type: accuracy
603
+ value: 77.64290517821115
604
  - type: f1
605
+ value: 74.45829057694098
606
  - task:
607
  type: Classification
608
  dataset:
 
613
  revision: 7d571f92784cd94a019292a1f45445077d0ef634
614
  metrics:
615
  - type: accuracy
616
+ value: 85.09751176866173
617
  - type: f1
618
+ value: 84.27719445179089
619
  - task:
620
  type: Retrieval
621
  dataset:
 
626
  revision: None
627
  metrics:
628
  - type: map_at_1
629
+ value: 54.7
630
  - type: map_at_10
631
+ value: 61.422
632
  - type: map_at_100
633
+ value: 61.870999999999995
634
  - type: map_at_1000
635
+ value: 61.917
636
  - type: map_at_3
637
+ value: 59.833000000000006
638
  - type: map_at_5
639
+ value: 60.663
640
  - type: mrr_at_1
641
+ value: 54.900000000000006
642
  - type: mrr_at_10
643
+ value: 61.539
644
  - type: mrr_at_100
645
+ value: 61.988
646
  - type: mrr_at_1000
647
+ value: 62.034
648
  - type: mrr_at_3
649
+ value: 59.95
650
  - type: mrr_at_5
651
+ value: 60.78
652
  - type: ndcg_at_1
653
+ value: 54.7
654
  - type: ndcg_at_10
655
+ value: 64.816
656
  - type: ndcg_at_100
657
+ value: 67.27499999999999
658
  - type: ndcg_at_1000
659
+ value: 68.518
660
  - type: ndcg_at_3
661
+ value: 61.446999999999996
662
  - type: ndcg_at_5
663
+ value: 62.937
664
  - type: precision_at_1
665
+ value: 54.7
666
  - type: precision_at_10
667
+ value: 7.5600000000000005
668
  - type: precision_at_100
669
+ value: 0.878
670
  - type: precision_at_1000
671
  value: 0.098
672
  - type: precision_at_3
673
+ value: 22.033
674
  - type: precision_at_5
675
+ value: 13.94
676
  - type: recall_at_1
677
+ value: 54.7
678
  - type: recall_at_10
679
+ value: 75.6
680
  - type: recall_at_100
681
+ value: 87.8
682
  - type: recall_at_1000
683
+ value: 97.6
684
  - type: recall_at_3
685
+ value: 66.10000000000001
686
  - type: recall_at_5
687
+ value: 69.69999999999999
688
  - task:
689
  type: Classification
690
  dataset:
 
695
  revision: None
696
  metrics:
697
  - type: accuracy
698
+ value: 78.61666666666667
699
  - type: f1
700
+ value: 78.46001064447016
701
  - task:
702
  type: PairClassification
703
  dataset:
 
708
  revision: None
709
  metrics:
710
  - type: cos_sim_accuracy
711
+ value: 83.48673524634542
712
  - type: cos_sim_ap
713
+ value: 86.97066512426397
714
  - type: cos_sim_f1
715
+ value: 84.4467108618052
716
  - type: cos_sim_precision
717
+ value: 81.65680473372781
718
  - type: cos_sim_recall
719
+ value: 87.43400211193241
720
  - type: dot_accuracy
721
+ value: 83.48673524634542
722
  - type: dot_ap
723
+ value: 86.97070037115512
724
  - type: dot_f1
725
+ value: 84.4467108618052
726
  - type: dot_precision
727
+ value: 81.65680473372781
728
  - type: dot_recall
729
+ value: 87.43400211193241
730
  - type: euclidean_accuracy
731
+ value: 83.48673524634542
732
  - type: euclidean_ap
733
+ value: 86.97066512426397
734
  - type: euclidean_f1
735
+ value: 84.4467108618052
736
  - type: euclidean_precision
737
+ value: 81.65680473372781
738
  - type: euclidean_recall
739
+ value: 87.43400211193241
740
  - type: manhattan_accuracy
741
+ value: 83.27016783974011
742
  - type: manhattan_ap
743
+ value: 86.97839108799026
744
  - type: manhattan_f1
745
+ value: 84.24273329933708
746
  - type: manhattan_precision
747
+ value: 81.4595660749507
748
  - type: manhattan_recall
749
+ value: 87.22280887011615
750
  - type: max_accuracy
751
+ value: 83.48673524634542
752
  - type: max_ap
753
+ value: 86.97839108799026
754
  - type: max_f1
755
+ value: 84.4467108618052
756
  - task:
757
  type: Classification
758
  dataset:
 
763
  revision: None
764
  metrics:
765
  - type: accuracy
766
+ value: 94.58
767
  - type: ap
768
+ value: 92.67235771989334
769
  - type: f1
770
+ value: 94.56749048144864
771
  - task:
772
  type: STS
773
  dataset:
 
778
  revision: None
779
  metrics:
780
  - type: cos_sim_pearson
781
+ value: 41.13075780508077
782
  - type: cos_sim_spearman
783
+ value: 46.23023927864047
784
  - type: euclidean_pearson
785
+ value: 45.8745816995021
786
  - type: euclidean_spearman
787
+ value: 46.230234996511186
788
  - type: manhattan_pearson
789
+ value: 45.87257756266397
790
  - type: manhattan_spearman
791
+ value: 46.23023501384774
792
  - task:
793
  type: STS
794
  dataset:
 
799
  revision: None
800
  metrics:
801
  - type: cos_sim_pearson
802
+ value: 44.584801951997676
803
  - type: cos_sim_spearman
804
+ value: 45.80390449641642
805
  - type: euclidean_pearson
806
+ value: 41.235476712471055
807
  - type: euclidean_spearman
808
+ value: 45.80391504205642
809
  - type: manhattan_pearson
810
+ value: 41.282727075778766
811
  - type: manhattan_spearman
812
+ value: 45.80885691191199
813
  - task:
814
  type: STS
815
  dataset:
 
820
  revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
821
  metrics:
822
  - type: cos_sim_pearson
823
+ value: 60.07699182332446
824
  - type: cos_sim_spearman
825
+ value: 61.30742120893451
826
  - type: euclidean_pearson
827
+ value: 57.975507370373805
828
  - type: euclidean_spearman
829
+ value: 61.30742120893451
830
  - type: manhattan_pearson
831
+ value: 57.981532129657566
832
  - type: manhattan_spearman
833
+ value: 61.35516394120813
834
  - task:
835
  type: STS
836
  dataset:
 
841
  revision: None
842
  metrics:
843
  - type: cos_sim_pearson
844
+ value: 77.33873897664922
845
  - type: cos_sim_spearman
846
+ value: 78.48046279063745
847
  - type: euclidean_pearson
848
+ value: 78.22561405005021
849
  - type: euclidean_spearman
850
+ value: 78.48054253550603
851
  - type: manhattan_pearson
852
+ value: 78.15799842348594
853
  - type: manhattan_spearman
854
+ value: 78.4163953888659
855
  - task:
856
  type: Reranking
857
  dataset:
 
862
  revision: None
863
  metrics:
864
  - type: map
865
+ value: 66.75988051767987
866
  - type: mrr
867
+ value: 77.18975346852801
868
  - task:
869
  type: Retrieval
870
  dataset:
 
875
  revision: None
876
  metrics:
877
  - type: map_at_1
878
+ value: 26.873
879
  - type: map_at_10
880
+ value: 75.21900000000001
881
  - type: map_at_100
882
+ value: 78.94200000000001
883
  - type: map_at_1000
884
+ value: 79.01599999999999
885
  - type: map_at_3
886
+ value: 52.885000000000005
887
  - type: map_at_5
888
+ value: 65.062
889
  - type: mrr_at_1
890
+ value: 88.646
891
  - type: mrr_at_10
892
+ value: 91.604
893
  - type: mrr_at_100
894
+ value: 91.69500000000001
895
  - type: mrr_at_1000
896
+ value: 91.69800000000001
897
  - type: mrr_at_3
898
+ value: 91.115
899
  - type: mrr_at_5
900
+ value: 91.444
901
  - type: ndcg_at_1
902
+ value: 88.646
903
  - type: ndcg_at_10
904
+ value: 83.19800000000001
905
  - type: ndcg_at_100
906
+ value: 87.04899999999999
907
  - type: ndcg_at_1000
908
+ value: 87.754
909
  - type: ndcg_at_3
910
+ value: 84.63199999999999
911
  - type: ndcg_at_5
912
+ value: 83.295
913
  - type: precision_at_1
914
+ value: 88.646
915
  - type: precision_at_10
916
+ value: 41.339
917
  - type: precision_at_100
918
+ value: 4.977
919
  - type: precision_at_1000
920
+ value: 0.515
921
  - type: precision_at_3
922
+ value: 74.009
923
  - type: precision_at_5
924
+ value: 62.104000000000006
925
  - type: recall_at_1
926
+ value: 26.873
927
  - type: recall_at_10
928
+ value: 82.268
929
  - type: recall_at_100
930
+ value: 94.675
931
  - type: recall_at_1000
932
+ value: 98.226
933
  - type: recall_at_3
934
+ value: 54.761
935
  - type: recall_at_5
936
+ value: 68.905
937
  - task:
938
  type: Classification
939
  dataset:
 
944
  revision: None
945
  metrics:
946
  - type: accuracy
947
+ value: 54.498000000000005
948
  - type: f1
949
+ value: 52.67480963825165
950
  - task:
951
  type: Clustering
952
  dataset:
 
957
  revision: None
958
  metrics:
959
  - type: v_measure
960
+ value: 71.20219333478684
961
  - task:
962
  type: Clustering
963
  dataset:
 
968
  revision: None
969
  metrics:
970
  - type: v_measure
971
+ value: 68.2649587922088
972
  - task:
973
  type: Retrieval
974
  dataset:
 
979
  revision: None
980
  metrics:
981
  - type: map_at_1
982
+ value: 56.39999999999999
983
  - type: map_at_10
984
+ value: 66.245
985
  - type: map_at_100
986
+ value: 66.838
987
  - type: map_at_1000
988
+ value: 66.849
989
  - type: map_at_3
990
+ value: 64.533
991
  - type: map_at_5
992
+ value: 65.593
993
  - type: mrr_at_1
994
+ value: 56.39999999999999
995
  - type: mrr_at_10
996
+ value: 66.245
997
  - type: mrr_at_100
998
+ value: 66.838
999
  - type: mrr_at_1000
1000
+ value: 66.849
1001
  - type: mrr_at_3
1002
+ value: 64.533
1003
  - type: mrr_at_5
1004
+ value: 65.593
1005
  - type: ndcg_at_1
1006
+ value: 56.39999999999999
1007
  - type: ndcg_at_10
1008
+ value: 70.575
1009
  - type: ndcg_at_100
1010
+ value: 73.324
1011
  - type: ndcg_at_1000
1012
+ value: 73.617
1013
  - type: ndcg_at_3
1014
+ value: 67.147
1015
  - type: ndcg_at_5
1016
+ value: 69.05
1017
  - type: precision_at_1
1018
+ value: 56.39999999999999
1019
  - type: precision_at_10
1020
+ value: 8.39
1021
  - type: precision_at_100
1022
+ value: 0.964
1023
  - type: precision_at_1000
1024
  value: 0.099
1025
  - type: precision_at_3
1026
+ value: 24.9
1027
  - type: precision_at_5
1028
+ value: 15.86
1029
  - type: recall_at_1
1030
+ value: 56.39999999999999
1031
  - type: recall_at_10
1032
+ value: 83.89999999999999
1033
  - type: recall_at_100
1034
+ value: 96.39999999999999
1035
  - type: recall_at_1000
1036
+ value: 98.7
1037
  - type: recall_at_3
1038
+ value: 74.7
1039
  - type: recall_at_5
1040
+ value: 79.3
1041
  - task:
1042
  type: Classification
1043
  dataset:
 
1048
  revision: None
1049
  metrics:
1050
  - type: accuracy
1051
+ value: 89.63000000000001
1052
  - type: ap
1053
+ value: 75.78836247276601
1054
  - type: f1
1055
+ value: 88.24687781823513
1056
  ---
1057
 
1058
  ### 使用方法