Hugging Face's logo

Erin
/
mist-zh

Feature Extraction
Transformers
PyTorch
bert
mteb
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
1
Erin commited on
Commit
28887cb
•
1 Parent(s): 5b73224

Upload README.md

Browse files
Files changed (1) hide show
  1. README.md +1038 -1147
README.md CHANGED
@@ -1,1152 +1,1043 @@
1
- pipeline_tag: sentence-similarity
2
  tags:
3
- - sentence-transformers
4
- - feature-extraction
5
- - sentence-similarity
6
  - mteb
7
  model-index:
8
  - name: mist-zh
9
  results:
10
- - task:
11
- type:STS
12
- dataset:
13
- type:C-MTEB/AFQMC
14
- name: MTEB AFQMC
15
- config: default
16
- split:validation
17
- revision:None
18
- metrics:
19
- - type: cos_sim_pearson
20
- value: 0.44734816122831544
21
- - type: cos_sim_spearman
22
- value: 0.46970061233318733
23
- - type: euclidean_pearson
24
- value: 0.45380620360050605
25
- - type: euclidean_spearman
26
- value: 0.46970061233318733
27
- - type: manhattan_pearson
28
- value: 0.45251004629975566
29
- - type: manhattan_spearman
30
- value: 0.4685418008817015
31
- - task:
32
- type:Classification
33
- dataset:
34
- type:mteb/amazon_reviews_multi
35
- name: MTEB AmazonReviewsClassification
36
- config: default
37
- split:test
38
- revision:1399c76144fd37290681b995c656ef9b2e06e26d
39
- metrics:
40
- - type: zh_accuracy
41
- value: 0.38855999999999996
42
- - type: zh_accuracy_stderr
43
- value: 0.025344001262626235
44
- - type: zh_f1
45
- value: 0.36961374807419534
46
- - type: zh_f1_stderr
47
- value: 0.021293704875037154
48
- - type: zh_main_score
49
- value: 0.38855999999999996
50
- - task:
51
- type:STS
52
- dataset:
53
- type:C-MTEB/ATEC
54
- name: MTEB ATEC
55
- config: default
56
- split:test
57
- revision:None
58
- metrics:
59
- - type: cos_sim_pearson
60
- value: 0.4923835317471939
61
- - type: cos_sim_spearman
62
- value: 0.5129611473119322
63
- - type: euclidean_pearson
64
- value: 0.5341533188991713
65
- - type: euclidean_spearman
66
- value: 0.5129611360495954
67
- - type: manhattan_pearson
68
- value: 0.5342662771302782
69
- - type: manhattan_spearman
70
- value: 0.5129682402789285
71
- - task:
72
- type:STS
73
- dataset:
74
- type:C-MTEB/BQ
75
- name: MTEB BQ
76
- config: default
77
- split:test
78
- revision:None
79
- metrics:
80
- - type: cos_sim_pearson
81
- value: 0.6179575529204537
82
- - type: cos_sim_spearman
83
- value: 0.6496308773217001
84
- - type: euclidean_pearson
85
- value: 0.6338747223113914
86
- - type: euclidean_spearman
87
- value: 0.6496309119412785
88
- - type: manhattan_pearson
89
- value: 0.6336833986897711
90
- - type: manhattan_spearman
91
- value: 0.6495000035386368
92
- - task:
93
- type:Clustering
94
- dataset:
95
- type:C-MTEB/CLSClusteringP2P
96
- name: MTEB CLSClusteringP2P
97
- config: default
98
- split:test
99
- revision:None
100
- metrics:
101
- - type: v_measure
102
- value: 0.4026570556670306
103
- - type: v_measure_std
104
- value: 0.017061921549952314
105
- - task:
106
- type:Clustering
107
- dataset:
108
- type:C-MTEB/CLSClusteringS2S
109
- name: MTEB CLSClusteringS2S
110
- config: default
111
- split:test
112
- revision:None
113
- metrics:
114
- - type: v_measure
115
- value: 0.3768621168788469
116
- - type: v_measure_std
117
- value: 0.015506559067836777
118
- - task:
119
- type:Retrieval
120
- dataset:
121
- type:C-MTEB/CmedqaRetrieval
122
- name: MTEB CmedqaRetrieval
123
- config: default
124
- split:dev
125
- revision:None
126
- metrics:
127
- - type: map_at_1
128
- value: 0.24044
129
- - type: map_at_10
130
- value: 0.35311
131
- - type: map_at_100
132
- value: 0.37125
133
- - type: map_at_1000
134
- value: 0.3726
135
- - type: map_at_3
136
- value: 0.31342
137
- - type: map_at_5
138
- value: 0.33613
139
- - type: mrr_at_1
140
- value: 0.36909
141
- - type: mrr_at_10
142
- value: 0.44373
143
- - type: mrr_at_100
144
- value: 0.45367
145
- - type: mrr_at_1000
146
- value: 0.45422
147
- - type: mrr_at_3
148
- value: 0.41927
149
- - type: mrr_at_5
150
- value: 0.43292
151
- - type: ndcg_at_1
152
- value: 0.36909
153
- - type: ndcg_at_10
154
- value: 0.41666
155
- - type: ndcg_at_100
156
- value: 0.48915
157
- - type: ndcg_at_1000
158
- value: 0.51348
159
- - type: ndcg_at_3
160
- value: 0.36592
161
- - type: ndcg_at_5
162
- value: 0.38787
163
- - type: precision_at_1
164
- value: 0.36909
165
- - type: precision_at_10
166
- value: 0.09327
167
- - type: precision_at_100
168
- value: 0.01523
169
- - type: precision_at_1000
170
- value: 0.00183
171
- - type: precision_at_3
172
- value: 0.20672
173
- - type: precision_at_5
174
- value: 0.15179
175
- - type: recall_at_1
176
- value: 0.24044
177
- - type: recall_at_10
178
- value: 0.5137
179
- - type: recall_at_100
180
- value: 0.81569
181
- - type: recall_at_1000
182
- value: 0.98053
183
- - type: recall_at_3
184
- value: 0.3612
185
- - type: recall_at_5
186
- value: 0.42829
187
- - task:
188
- type:Reranking
189
- dataset:
190
- type:C-MTEB/CMedQAv1-reranking
191
- name: MTEB CMedQAv1
192
- config: default
193
- split:test
194
- revision:None
195
- metrics:
196
- - type: map
197
- value: 0.8440938491415716
198
- - type: mrr
199
- value: 0.8686722222222222
200
- - task:
201
- type:Reranking
202
- dataset:
203
- type:C-MTEB/CMedQAv2-reranking
204
- name: MTEB CMedQAv2
205
- config: default
206
- split:test
207
- revision:None
208
- metrics:
209
- - type: map
210
- value: 0.852507433210034
211
- - type: mrr
212
- value: 0.8758742063492063
213
- - task:
214
- type:PairClassification
215
- dataset:
216
- type:C-MTEB/CMNLI
217
- name: MTEB Cmnli
218
- config: default
219
- split:validation
220
- revision:None
221
- metrics:
222
- - type: cos_sim_accuracy
223
- value: 0.7592303066746843
224
- - type: cos_sim_accuracy_threshold
225
- value: 0.7603684663772583
226
- - type: cos_sim_ap
227
- value: 0.8439741959629594
228
- - type: cos_sim_f1
229
- value: 0.7728710064333224
230
- - type: cos_sim_f1_threshold
231
- value: 0.7281966805458069
232
- - type: cos_sim_precision
233
- value: 0.7241520228851656
234
- - type: cos_sim_recall
235
- value: 0.828618190320318
236
- - type: dot_accuracy
237
- value: 0.7592303066746843
238
- - type: dot_accuracy_threshold
239
- value: 0.7603684663772583
240
- - type: dot_ap
241
- value: 0.8439592659189602
242
- - type: dot_f1
243
- value: 0.7728710064333224
244
- - type: dot_f1_threshold
245
- value: 0.7281967401504517
246
- - type: dot_precision
247
- value: 0.7241520228851656
248
- - type: dot_recall
249
- value: 0.828618190320318
250
- - type: euclidean_accuracy
251
- value: 0.7592303066746843
252
- - type: euclidean_accuracy_threshold
253
- value: 0.6922882795333862
254
- - type: euclidean_ap
255
- value: 0.8439741904478117
256
- - type: euclidean_f1
257
- value: 0.7728710064333224
258
- - type: euclidean_f1_threshold
259
- value: 0.7372968196868896
260
- - type: euclidean_precision
261
- value: 0.7241520228851656
262
- - type: euclidean_recall
263
- value: 0.828618190320318
264
- - type: manhattan_accuracy
265
- value: 0.7583884546001203
266
- - type: manhattan_accuracy_threshold
267
- value: 15.160146713256836
268
- - type: manhattan_ap
269
- value: 0.8439482592167423
270
- - type: manhattan_f1
271
- value: 0.7724197186123941
272
- - type: manhattan_f1_threshold
273
- value: 16.451358795166016
274
- - type: manhattan_precision
275
- value: 0.7143424711958681
276
- - type: manhattan_recall
277
- value: 0.8407762450315642
278
- - type: max_accuracy
279
- value: 0.7592303066746843
280
- - type: max_ap
281
- value: 0.8439741959629594
282
- - type: max_f1
283
- value: 0.7728710064333224
284
- - task:
285
- type:Retrieval
286
- dataset:
287
- type:C-MTEB/CovidRetrieval
288
- name: MTEB CovidRetrieval
289
- config: default
290
- split:dev
291
- revision:None
292
- metrics:
293
- - type: map_at_1
294
- value: 0.6765
295
- - type: map_at_10
296
- value: 0.75672
297
- - type: map_at_100
298
- value: 0.76005
299
- - type: map_at_1000
300
- value: 0.76007
301
- - type: map_at_3
302
- value: 0.73867
303
- - type: map_at_5
304
- value: 0.74949
305
- - type: mrr_at_1
306
- value: 0.67756
307
- - type: mrr_at_10
308
- value: 0.7564
309
- - type: mrr_at_100
310
- value: 0.75973
311
- - type: mrr_at_1000
312
- value: 0.75975
313
- - type: mrr_at_3
314
- value: 0.73867
315
- - type: mrr_at_5
316
- value: 0.74984
317
- - type: ndcg_at_1
318
- value: 0.67861
319
- - type: ndcg_at_10
320
- value: 0.79393
321
- - type: ndcg_at_100
322
- value: 0.81044
323
- - type: ndcg_at_1000
324
- value: 0.81153
325
- - type: ndcg_at_3
326
- value: 0.75767
327
- - type: ndcg_at_5
328
- value: 0.77714
329
- - type: precision_at_1
330
- value: 0.67861
331
- - type: precision_at_10
332
- value: 0.09199
333
- - type: precision_at_100
334
- value: 0.00998
335
- - type: precision_at_1000
336
- value: 0.00101
337
- - type: precision_at_3
338
- value: 0.27222
339
- - type: precision_at_5
340
- value: 0.17302
341
- - type: recall_at_1
342
- value: 0.6765
343
- - type: recall_at_10
344
- value: 0.90938
345
- - type: recall_at_100
346
- value: 0.98736
347
- - type: recall_at_1000
348
- value: 0.99684
349
- - type: recall_at_3
350
- value: 0.81138
351
- - type: recall_at_5
352
- value: 0.85827
353
- - task:
354
- type:Retrieval
355
- dataset:
356
- type: C-MTEB/DuRetrieval
357
- name: MTEB DuRetrieval
358
- config: default
359
- split:dev
360
- revision:None
361
- metrics:
362
- - type: map_at_1
363
- value: 0.25407
364
- - type: map_at_10
365
- value: 0.79001
366
- - type: map_at_100
367
- value: 0.81983
368
- - type: map_at_1000
369
- value: 0.82021
370
- - type: map_at_3
371
- value: 0.54256
372
- - type: map_at_5
373
- value: 0.68918
374
- - type: mrr_at_1
375
- value: 0.8915
376
- - type: mrr_at_10
377
- value: 0.92548
378
- - type: mrr_at_100
379
- value: 0.92614
380
- - type: mrr_at_1000
381
- value: 0.92616
382
- - type: mrr_at_3
383
- value: 0.92175
384
- - type: mrr_at_5
385
- value: 0.92432
386
- - type: ndcg_at_1
387
- value: 0.8915
388
- - type: ndcg_at_10
389
- value: 0.86588
390
- - type: ndcg_at_100
391
- value: 0.89487
392
- - type: ndcg_at_1000
393
- value: 0.89841
394
- - type: ndcg_at_3
395
- value: 0.8501
396
- - type: ndcg_at_5
397
- value: 0.84301
398
- - type: precision_at_1
399
- value: 0.8915
400
- - type: precision_at_10
401
- value: 0.4171
402
- - type: precision_at_100
403
- value: 0.04807
404
- - type: precision_at_1000
405
- value: 0.00489
406
- - type: precision_at_3
407
- value: 0.76417
408
- - type: precision_at_5
409
- value: 0.6495
410
- - type: recall_at_1
411
- value: 0.25407
412
- - type: recall_at_10
413
- value: 0.88221
414
- - type: recall_at_100
415
- value: 0.97527
416
- - type: recall_at_1000
417
- value: 0.99396
418
- - type: recall_at_3
419
- value: 0.56751
420
- - type: recall_at_5
421
- value: 0.74191
422
- - task:
423
- type:Retrieval
424
- dataset:
425
- type:C-MTEB/EcomRetrieval
426
- name: MTEB EcomRetrieval
427
- config: default
428
- split:dev
429
- revision:None
430
- metrics:
431
- - type: map_at_1
432
- value: 0.476
433
- - type: map_at_10
434
- value: 0.5715
435
- - type: map_at_100
436
- value: 0.57789
437
- - type: map_at_1000
438
- value: 0.57808
439
- - type: map_at_3
440
- value: 0.54467
441
- - type: map_at_5
442
- value: 0.56017
443
- - type: mrr_at_1
444
- value: 0.476
445
- - type: mrr_at_10
446
- value: 0.5715
447
- - type: mrr_at_100
448
- value: 0.57789
449
- - type: mrr_at_1000
450
- value: 0.57808
451
- - type: mrr_at_3
452
- value: 0.54467
453
- - type: mrr_at_5
454
- value: 0.56017
455
- - type: ndcg_at_1
456
- value: 0.476
457
- - type: ndcg_at_10
458
- value: 0.62304
459
- - type: ndcg_at_100
460
- value: 0.65329
461
- - type: ndcg_at_1000
462
- value: 0.65837
463
- - type: ndcg_at_3
464
- value: 0.56757
465
- - type: ndcg_at_5
466
- value: 0.59575
467
- - type: precision_at_1
468
- value: 0.476
469
- - type: precision_at_10
470
- value: 0.0787
471
- - type: precision_at_100
472
- value: 0.00926
473
- - type: precision_at_1000
474
- value: 0.00097
475
- - type: precision_at_3
476
- value: 0.21133
477
- - type: precision_at_5
478
- value: 0.1406
479
- - type: recall_at_1
480
- value: 0.476
481
- - type: recall_at_10
482
- value: 0.787
483
- - type: recall_at_100
484
- value: 0.926
485
- - type: recall_at_1000
486
- value: 0.966
487
- - type: recall_at_3
488
- value: 0.634
489
- - type: recall_at_5
490
- value: 0.703
491
- - task:
492
- type:Classification
493
- dataset:
494
- type:C-MTEB/IFlyTek-classification
495
- name: MTEB IFlyTek
496
- config: default
497
- split:validation
498
- revision:None
499
- metrics:
500
- - type: accuracy
501
- value: 0.4828010773374375
502
- - type: accuracy_stderr
503
- value: 0.005244086198507375
504
- - type: f1
505
- value: 0.3553699530214492
506
- - type: f1_stderr
507
- value: 0.0037929213901079944
508
- - type: main_score
509
- value: 0.4828010773374375
510
- - task:
511
- type:Classification
512
- dataset:
513
- type:C-MTEB/JDReview-classification
514
- name: MTEB JDReview
515
- config: default
516
- split:test
517
- revision:None
518
- metrics:
519
- - type: accuracy
520
- value: 0.8484052532833021
521
- - type: accuracy_stderr
522
- value: 0.015848939648737626
523
- - type: ap
524
- value: 0.5235323515091401
525
- - type: ap_stderr
526
- value: 0.02150569650954474
527
- - type: f1
528
- value: 0.7950069160202494
529
- - type: f1_stderr
530
- value: 0.01499969036259696
531
- - type: main_score
532
- value: 0.8484052532833021
533
- - task:
534
- type:STS
535
- dataset:
536
- type:C-MTEB/LCQMC
537
- name: MTEB LCQMC
538
- config: default
539
- split:test
540
- revision:None
541
- metrics:
542
- - type: cos_sim_pearson
543
- value: 0.6968404288794713
544
- - type: cos_sim_spearman
545
- value: 0.7706824442481803
546
- - type: euclidean_pearson
547
- value: 0.7547746745802166
548
- - type: euclidean_spearman
549
- value: 0.7706825328995878
550
- - type: manhattan_pearson
551
- value: 0.7546220581621667
552
- - type: manhattan_spearman
553
- value: 0.7705100926919136
554
- - task:
555
- type:Classification
556
- dataset:
557
- type:mteb/amazon_massive_intent
558
- name: MTEB MassiveIntentClassification
559
- config: default
560
- split:test
561
- revision:31efe3c427b0bae9c22cbb560b8f15491cc6bed7
562
- metrics:
563
- - type: zh-CN_accuracy
564
- value: 0.6745124411566913
565
- - type: zh-CN_accuracy_stderr
566
- value: 0.015989972974792096
567
- - type: zh-CN_f1
568
- value: 0.6477175074397455
569
- - type: zh-CN_f1_stderr
570
- value: 0.015484061914007355
571
- - type: zh-CN_main_score
572
- value: 0.6745124411566913
573
- - task:
574
- type:Classification
575
- dataset:
576
- type:mteb/amazon_massive_scenario
577
- name: MTEB MassiveScenarioClassification
578
- config: default
579
- split:test
580
- revision:7d571f92784cd94a019292a1f45445077d0ef634
581
- metrics:
582
- - type: zh-CN_accuracy
583
- value: 0.7308002689979824
584
- - type: zh-CN_accuracy_stderr
585
- value: 0.015224400610167006
586
- - type: zh-CN_f1
587
- value: 0.7265358173635958
588
- - type: zh-CN_f1_stderr
589
- value: 0.014453983681998765
590
- - type: zh-CN_main_score
591
- value: 0.7308002689979824
592
- - task:
593
- type:Retrieval
594
- dataset:
595
- type:C-MTEB/MedicalRetrieval
596
- name: MTEB MedicalRetrieval
597
- config: default
598
- split:dev
599
- revision:None
600
- metrics:
601
- - type: map_at_1
602
- value: 0.487
603
- - type: map_at_10
604
- value: 0.54871
605
- - type: map_at_100
606
- value: 0.55381
607
- - type: map_at_1000
608
- value: 0.55436
609
- - type: map_at_3
610
- value: 0.53367
611
- - type: map_at_5
612
- value: 0.54257
613
- - type: mrr_at_1
614
- value: 0.487
615
- - type: mrr_at_10
616
- value: 0.54871
617
- - type: mrr_at_100
618
- value: 0.55381
619
- - type: mrr_at_1000
620
- value: 0.55436
621
- - type: mrr_at_3
622
- value: 0.53367
623
- - type: mrr_at_5
624
- value: 0.54257
625
- - type: ndcg_at_1
626
- value: 0.487
627
- - type: ndcg_at_10
628
- value: 0.57942
629
- - type: ndcg_at_100
630
- value: 0.6075
631
- - type: ndcg_at_1000
632
- value: 0.62401
633
- - type: ndcg_at_3
634
- value: 0.54867
635
- - type: ndcg_at_5
636
- value: 0.56493
637
- - type: precision_at_1
638
- value: 0.487
639
- - type: precision_at_10
640
- value: 0.0676
641
- - type: precision_at_100
642
- value: 0.00815
643
- - type: precision_at_1000
644
- value: 0.00095
645
- - type: precision_at_3
646
- value: 0.19733
647
- - type: precision_at_5
648
- value: 0.1264
649
- - type: recall_at_1
650
- value: 0.487
651
- - type: recall_at_10
652
- value: 0.676
653
- - type: recall_at_100
654
- value: 0.815
655
- - type: recall_at_1000
656
- value: 0.949
657
- - type: recall_at_3
658
- value: 0.592
659
- - type: recall_at_5
660
- value: 0.632
661
- - task:
662
- type:Reranking
663
- dataset:
664
- type:C-MTEB/Mmarco-reranking
665
- name: MTEB MmarcoReranking
666
- config: default
667
- split:dev
668
- revision:None
669
- metrics:
670
- - type: map
671
- value: 0.2809825725416088
672
- - type: mrr
673
- value: 0.2695912698412698
674
- - task:
675
- type:Retrieval
676
- dataset:
677
- type:C-MTEB/MMarcoRetrieval
678
- name: MTEB MMarcoRetrieval
679
- config: default
680
- split:dev
681
- revision:None
682
- metrics:
683
- - type: map_at_1
684
- value: 0.65368
685
- - type: map_at_10
686
- value: 0.74294
687
- - type: map_at_100
688
- value: 0.74653
689
- - type: map_at_1000
690
- value: 0.74664
691
- - type: map_at_3
692
- value: 0.72416
693
- - type: map_at_5
694
- value: 0.73658
695
- - type: mrr_at_1
696
- value: 0.67507
697
- - type: mrr_at_10
698
- value: 0.74853
699
- - type: mrr_at_100
700
- value: 0.75174
701
- - type: mrr_at_1000
702
- value: 0.75184
703
- - type: mrr_at_3
704
- value: 0.73235
705
- - type: mrr_at_5
706
- value: 0.74298
707
- - type: ndcg_at_1
708
- value: 0.67507
709
- - type: ndcg_at_10
710
- value: 0.77948
711
- - type: ndcg_at_100
712
- value: 0.79555
713
- - type: ndcg_at_1000
714
- value: 0.79864
715
- - type: ndcg_at_3
716
- value: 0.74434
717
- - type: ndcg_at_5
718
- value: 0.76504
719
- - type: precision_at_1
720
- value: 0.67507
721
- - type: precision_at_10
722
- value: 0.09423
723
- - type: precision_at_100
724
- value: 0.01022
725
- - type: precision_at_1000
726
- value: 0.00105
727
- - type: precision_at_3
728
- value: 0.27975
729
- - type: precision_at_5
730
- value: 0.17891
731
- - type: recall_at_1
732
- value: 0.65368
733
- - type: recall_at_10
734
- value: 0.88633
735
- - type: recall_at_100
736
- value: 0.95889
737
- - type: recall_at_1000
738
- value: 0.98346
739
- - type: recall_at_3
740
- value: 0.79404
741
- - type: recall_at_5
742
- value: 0.84292
743
- - task:
744
- type:Classification
745
- dataset:
746
- type:C-MTEB/MultilingualSentiment-classification
747
- name: MTEB MultilingualSentiment
748
- config: default
749
- split:validation
750
- revision:None
751
- metrics:
752
- - type: accuracy
753
- value: 0.7127000000000001
754
- - type: accuracy_stderr
755
- value: 0.009532925399197596
756
- - type: f1
757
- value: 0.7046516219039894
758
- - type: f1_stderr
759
- value: 0.01407386657805127
760
- - type: main_score
761
- value: 0.7127000000000001
762
- - task:
763
- type:PairClassification
764
- dataset:
765
- type:C-MTEB/OCNLI
766
- name: MTEB Ocnli
767
- config: default
768
- split:validation
769
- revision:None
770
- metrics:
771
- - type: cos_sim_accuracy
772
- value: 0.6989713048186248
773
- - type: cos_sim_accuracy_threshold
774
- value: 0.7673404216766357
775
- - type: cos_sim_ap
776
- value: 0.7475296949416844
777
- - type: cos_sim_f1
778
- value: 0.730820399113082
779
- - type: cos_sim_f1_threshold
780
- value: 0.7212538719177246
781
- - type: cos_sim_precision
782
- value: 0.6299694189602446
783
- - type: cos_sim_recall
784
- value: 0.870116156282999
785
- - type: dot_accuracy
786
- value: 0.6989713048186248
787
- - type: dot_accuracy_threshold
788
- value: 0.7673404216766357
789
- - type: dot_ap
790
- value: 0.7475289228002875
791
- - type: dot_f1
792
- value: 0.730820399113082
793
- - type: dot_f1_threshold
794
- value: 0.7212539315223694
795
- - type: dot_precision
796
- value: 0.6299694189602446
797
- - type: dot_recall
798
- value: 0.870116156282999
799
- - type: euclidean_accuracy
800
- value: 0.6989713048186248
801
- - type: euclidean_accuracy_threshold
802
- value: 0.6821430325508118
803
- - type: euclidean_ap
804
- value: 0.7475289228002875
805
- - type: euclidean_f1
806
- value: 0.730820399113082
807
- - type: euclidean_f1_threshold
808
- value: 0.7466539740562439
809
- - type: euclidean_precision
810
- value: 0.6299694189602446
811
- - type: euclidean_recall
812
- value: 0.870116156282999
813
- - type: manhattan_accuracy
814
- value: 0.699512723335138
815
- - type: manhattan_accuracy_threshold
816
- value: 15.069363594055176
817
- - type: manhattan_ap
818
- value: 0.7463572749955489
819
- - type: manhattan_f1
820
- value: 0.7280663465735486
821
- - type: manhattan_f1_threshold
822
- value: 16.66558074951172
823
- - type: manhattan_precision
824
- value: 0.6205357142857143
825
- - type: manhattan_recall
826
- value: 0.880675818373812
827
- - type: max_accuracy
828
- value: 0.699512723335138
829
- - type: max_ap
830
- value: 0.7475296949416844
831
- - type: max_f1
832
- value: 0.730820399113082
833
- - task:
834
- type:Classification
835
- dataset:
836
- type:C-MTEB/OnlineShopping-classification
837
- name: MTEB OnlineShopping
838
- config: default
839
- split:test
840
- revision:None
841
- metrics:
842
- - type: accuracy
843
- value: 0.9138
844
- - type: accuracy_stderr
845
- value: 0.012456323695216027
846
- - type: ap
847
- value: 0.8914371766660247
848
- - type: ap_stderr
849
- value: 0.01735000706460752
850
- - type: f1
851
- value: 0.9136682962995261
852
- - type: f1_stderr
853
- value: 0.012499748296615935
854
- - type: main_score
855
- value: 0.9138
856
- - task:
857
- type:STS
858
- dataset:
859
- type:C-MTEB/PAWSX
860
- name: MTEB PAWSX
861
- config: default
862
- split:test
863
- revision:None
864
- metrics:
865
- - type: cos_sim_pearson
866
- value: 0.23621683997579607
867
- - type: cos_sim_spearman
868
- value: 0.2946714129804792
869
- - type: euclidean_pearson
870
- value: 0.29841725912733486
871
- - type: euclidean_spearman
872
- value: 0.2946695199370699
873
- - type: manhattan_pearson
874
- value: 0.29853598937043624
875
- - type: manhattan_spearman
876
- value: 0.2942340511723847
877
- - task:
878
- type:STS
879
- dataset:
880
- type:C-MTEB/QBQTC
881
- name: MTEB QBQTC
882
- config: default
883
- split:test
884
- revision:None
885
- metrics:
886
- - type: cos_sim_pearson
887
- value: 0.3486196986379606
888
- - type: cos_sim_spearman
889
- value: 0.37316873994339983
890
- - type: euclidean_pearson
891
- value: 0.3552672274329054
892
- - type: euclidean_spearman
893
- value: 0.3731679950751101
894
- - type: manhattan_pearson
895
- value: 0.35558794378442254
896
- - type: manhattan_spearman
897
- value: 0.3736943324703548
898
- - task:
899
- type:STS
900
- dataset:
901
- type:mteb/sts22-crosslingual-sts
902
- name: MTEB STS22
903
- config: default
904
- split:test
905
- revision:6d1ba47164174a496b7fa5d3569dae26a6813b80
906
- metrics:
907
- - type: zh_cos_sim
908
- value: {'pearson': 0.687924534800626, 'spearman': 0.6945014686127369}
909
- - type: zh_euclidean
910
- value: {'pearson': 0.6912500964503516, 'spearman': 0.6945014686127369}
911
- - type: zh_manhattan
912
- value: {'pearson': 0.7053825064823807, 'spearman': 0.7067595198226869}
913
- - task:
914
- type:STS
915
- dataset:
916
- type:C-MTEB/STSB
917
- name: MTEB STSB
918
- config: default
919
- split:test
920
- revision:None
921
- metrics:
922
- - type: cos_sim_pearson
923
- value: 0.7902281275805849
924
- - type: cos_sim_spearman
925
- value: 0.7969275718339353
926
- - type: euclidean_pearson
927
- value: 0.7939660648560956
928
- - type: euclidean_spearman
929
- value: 0.7969291851788453
930
- - type: manhattan_pearson
931
- value: 0.793382690172365
932
- - type: manhattan_spearman
933
- value: 0.7963605584076028
934
- - task:
935
- type:Reranking
936
- dataset:
937
- type:C-MTEB/T2Reranking
938
- name: MTEB T2Reranking
939
- config: default
940
- split:dev
941
- revision:None
942
- metrics:
943
- - type: map
944
- value: 0.6619942712343411
945
- - type: mrr
946
- value: 0.7576681067371656
947
- - task:
948
- type:Retrieval
949
- dataset:
950
- type:C-MTEB/T2Retrieval
951
- name: MTEB T2Retrieval
952
- config: default
953
- split:dev
954
- revision:None
955
- metrics:
956
- - type: map_at_1
957
- value: 0.26594
958
- - type: map_at_10
959
- value: 0.75272
960
- - type: map_at_100
961
- value: 0.7896
962
- - type: map_at_1000
963
- value: 0.79032
964
- - type: map_at_3
965
- value: 0.5276
966
- - type: map_at_5
967
- value: 0.64967
968
- - type: mrr_at_1
969
- value: 0.88721
970
- - type: mrr_at_10
971
- value: 0.9138
972
- - type: mrr_at_100
973
- value: 0.91484
974
- - type: mrr_at_1000
975
- value: 0.91489
976
- - type: mrr_at_3
977
- value: 0.90901
978
- - type: mrr_at_5
979
- value: 0.9121
980
- - type: ndcg_at_1
981
- value: 0.88721
982
- - type: ndcg_at_10
983
- value: 0.83099
984
- - type: ndcg_at_100
985
- value: 0.86938
986
- - type: ndcg_at_1000
987
- value: 0.87644
988
- - type: ndcg_at_3
989
- value: 0.84573
990
- - type: ndcg_at_5
991
- value: 0.83131
992
- - type: precision_at_1
993
- value: 0.88721
994
- - type: precision_at_10
995
- value: 0.41506
996
- - type: precision_at_100
997
- value: 0.0499
998
- - type: precision_at_1000
999
- value: 0.00515
1000
- - type: precision_at_3
1001
- value: 0.74214
1002
- - type: precision_at_5
1003
- value: 0.62244
1004
- - type: recall_at_1
1005
- value: 0.26594
1006
- - type: recall_at_10
1007
- value: 0.82121
1008
- - type: recall_at_100
1009
- value: 0.94643
1010
- - type: recall_at_1000
1011
- value: 0.98261
1012
- - type: recall_at_3
1013
- value: 0.54539
1014
- - type: recall_at_5
1015
- value: 0.68573
1016
- - task:
1017
- type:Clustering
1018
- dataset:
1019
- type:C-MTEB/ThuNewsClusteringP2P
1020
- name: MTEB ThuNewsClusteringP2P
1021
- config: default
1022
- split:test
1023
- revision:None
1024
- metrics:
1025
- - type: v_measure
1026
- value: 0.6234936773593232
1027
- - type: v_measure_std
1028
- value: 0.014872291909155068
1029
- - task:
1030
- type:Clustering
1031
- dataset:
1032
- type:C-MTEB/ThuNewsClusteringS2S
1033
- name: MTEB ThuNewsClusteringS2S
1034
- config: default
1035
- split:test
1036
- revision:None
1037
- metrics:
1038
- - type: v_measure
1039
- value: 0.5865057354232379
1040
- - type: v_measure_std
1041
- value: 0.014281574028380747
1042
- - task:
1043
- type:Classification
1044
- dataset:
1045
- type:C-MTEB/TNews-classification
1046
- name: MTEB TNews
1047
- config: default
1048
- split:validation
1049
- revision:None
1050
- metrics:
1051
- - type: accuracy
1052
- value: 0.51845
1053
- - type: accuracy_stderr
1054
- value: 0.006959058844412791
1055
- - type: f1
1056
- value: 0.4997529772676145
1057
- - type: f1_stderr
1058
- value: 0.007865498715360303
1059
- - type: main_score
1060
- value: 0.51845
1061
- - task:
1062
- type:Retrieval
1063
- dataset:
1064
- type:C-MTEB/VideoRetrieval
1065
- name: MTEB VideoRetrieval
1066
- config: default
1067
- split:dev
1068
- revision:None
1069
- metrics:
1070
- - type: map_at_1
1071
- value: 0.522
1072
- - type: map_at_10
1073
- value: 0.62669
1074
- - type: map_at_100
1075
- value: 0.63239
1076
- - type: map_at_1000
1077
- value: 0.63253
1078
- - type: map_at_3
1079
- value: 0.60267
1080
- - type: map_at_5
1081
- value: 0.61772
1082
- - type: mrr_at_1
1083
- value: 0.522
1084
- - type: mrr_at_10
1085
- value: 0.62669
1086
- - type: mrr_at_100
1087
- value: 0.63239
1088
- - type: mrr_at_1000
1089
- value: 0.63253
1090
- - type: mrr_at_3
1091
- value: 0.60267
1092
- - type: mrr_at_5
1093
- value: 0.61772
1094
- - type: ndcg_at_1
1095
- value: 0.522
1096
- - type: ndcg_at_10
1097
- value: 0.67583
1098
- - type: ndcg_at_100
1099
- value: 0.70305
1100
- - type: ndcg_at_1000
1101
- value: 0.70652
1102
- - type: ndcg_at_3
1103
- value: 0.62776
1104
- - type: ndcg_at_5
1105
- value: 0.6547
1106
- - type: precision_at_1
1107
- value: 0.522
1108
- - type: precision_at_10
1109
- value: 0.0829
1110
- - type: precision_at_100
1111
- value: 0.00955
1112
- - type: precision_at_1000
1113
- value: 0.00098
1114
- - type: precision_at_3
1115
- value: 0.23333
1116
- - type: precision_at_5
1117
- value: 0.153
1118
- - type: recall_at_1
1119
- value: 0.522
1120
- - type: recall_at_10
1121
- value: 0.829
1122
- - type: recall_at_100
1123
- value: 0.955
1124
- - type: recall_at_1000
1125
- value: 0.982
1126
- - type: recall_at_3
1127
- value: 0.7
1128
- - type: recall_at_5
1129
- value: 0.765
1130
- - task:
1131
- type:Classification
1132
- dataset:
1133
- type: C-MTEB/waimai-classification
1134
- name: MTEB Waimai
1135
- config: default
1136
- split:test
1137
- revision:None
1138
- metrics:
1139
- - type: accuracy
1140
- value: 0.8664999999999999
1141
- - type: accuracy_stderr
1142
- value: 0.007697402159170332
1143
- - type: ap
1144
- value: 0.6990209999390807
1145
- - type: ap_stderr
1146
- value: 0.014543148063974986
1147
- - type: f1
1148
- value: 0.849231810656075
1149
- - type: f1_stderr
1150
- value: 0.0073258070989864026
1151
- - type: main_score
1152
- value: 0.8664999999999999
 
1
+ ---
2
  tags:
 
 
 
3
  - mteb
4
  model-index:
5
  - name: mist-zh
6
  results:
7
+ - task:
8
+ type: STS
9
+ dataset:
10
+ type: C-MTEB/AFQMC
11
+ name: MTEB AFQMC
12
+ config: default
13
+ split: validation
14
+ revision: None
15
+ metrics:
16
+ - type: cos_sim_pearson
17
+ value: 44.734816122831546
18
+ - type: cos_sim_spearman
19
+ value: 46.97006123331873
20
+ - type: euclidean_pearson
21
+ value: 45.38062036005061
22
+ - type: euclidean_spearman
23
+ value: 46.97006123331873
24
+ - type: manhattan_pearson
25
+ value: 45.25100462997557
26
+ - type: manhattan_spearman
27
+ value: 46.85418008817015
28
+ - task:
29
+ type: STS
30
+ dataset:
31
+ type: C-MTEB/ATEC
32
+ name: MTEB ATEC
33
+ config: default
34
+ split: test
35
+ revision: None
36
+ metrics:
37
+ - type: cos_sim_pearson
38
+ value: 49.23835317471939
39
+ - type: cos_sim_spearman
40
+ value: 51.29611473119322
41
+ - type: euclidean_pearson
42
+ value: 53.41533188991713
43
+ - type: euclidean_spearman
44
+ value: 51.29611360495954
45
+ - type: manhattan_pearson
46
+ value: 53.42662771302782
47
+ - type: manhattan_spearman
48
+ value: 51.29682402789285
49
+ - task:
50
+ type: Classification
51
+ dataset:
52
+ type: mteb/amazon_reviews_multi
53
+ name: MTEB AmazonReviewsClassification (zh)
54
+ config: zh
55
+ split: test
56
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
57
+ metrics:
58
+ - type: accuracy
59
+ value: 38.855999999999995
60
+ - type: f1
61
+ value: 36.96137480741953
62
+ - task:
63
+ type: STS
64
+ dataset:
65
+ type: C-MTEB/BQ
66
+ name: MTEB BQ
67
+ config: default
68
+ split: test
69
+ revision: None
70
+ metrics:
71
+ - type: cos_sim_pearson
72
+ value: 61.79575529204537
73
+ - type: cos_sim_spearman
74
+ value: 64.96308773217001
75
+ - type: euclidean_pearson
76
+ value: 63.38747223113914
77
+ - type: euclidean_spearman
78
+ value: 64.96309119412786
79
+ - type: manhattan_pearson
80
+ value: 63.36833986897711
81
+ - type: manhattan_spearman
82
+ value: 64.95000035386369
83
+ - task:
84
+ type: Clustering
85
+ dataset:
86
+ type: C-MTEB/CLSClusteringP2P
87
+ name: MTEB CLSClusteringP2P
88
+ config: default
89
+ split: test
90
+ revision: None
91
+ metrics:
92
+ - type: v_measure
93
+ value: 40.26570556670306
94
+ - task:
95
+ type: Clustering
96
+ dataset:
97
+ type: C-MTEB/CLSClusteringS2S
98
+ name: MTEB CLSClusteringS2S
99
+ config: default
100
+ split: test
101
+ revision: None
102
+ metrics:
103
+ - type: v_measure
104
+ value: 37.68621168788469
105
+ - task:
106
+ type: Reranking
107
+ dataset:
108
+ type: C-MTEB/CMedQAv1-reranking
109
+ name: MTEB CMedQAv1
110
+ config: default
111
+ split: test
112
+ revision: None
113
+ metrics:
114
+ - type: map
115
+ value: 84.40938491415716
116
+ - type: mrr
117
+ value: 86.86722222222222
118
+ - task:
119
+ type: Reranking
120
+ dataset:
121
+ type: C-MTEB/CMedQAv2-reranking
122
+ name: MTEB CMedQAv2
123
+ config: default
124
+ split: test
125
+ revision: None
126
+ metrics:
127
+ - type: map
128
+ value: 85.2507433210034
129
+ - type: mrr
130
+ value: 87.58742063492063
131
+ - task:
132
+ type: Retrieval
133
+ dataset:
134
+ type: C-MTEB/CmedqaRetrieval
135
+ name: MTEB CmedqaRetrieval
136
+ config: default
137
+ split: dev
138
+ revision: None
139
+ metrics:
140
+ - type: map_at_1
141
+ value: 24.043999999999997
142
+ - type: map_at_10
143
+ value: 35.311
144
+ - type: map_at_100
145
+ value: 37.125
146
+ - type: map_at_1000
147
+ value: 37.26
148
+ - type: map_at_3
149
+ value: 31.342
150
+ - type: map_at_5
151
+ value: 33.613
152
+ - type: mrr_at_1
153
+ value: 36.909
154
+ - type: mrr_at_10
155
+ value: 44.373000000000005
156
+ - type: mrr_at_100
157
+ value: 45.367000000000004
158
+ - type: mrr_at_1000
159
+ value: 45.422000000000004
160
+ - type: mrr_at_3
161
+ value: 41.927
162
+ - type: mrr_at_5
163
+ value: 43.292
164
+ - type: ndcg_at_1
165
+ value: 36.909
166
+ - type: ndcg_at_10
167
+ value: 41.666
168
+ - type: ndcg_at_100
169
+ value: 48.915
170
+ - type: ndcg_at_1000
171
+ value: 51.348000000000006
172
+ - type: ndcg_at_3
173
+ value: 36.592
174
+ - type: ndcg_at_5
175
+ value: 38.787
176
+ - type: precision_at_1
177
+ value: 36.909
178
+ - type: precision_at_10
179
+ value: 9.327
180
+ - type: precision_at_100
181
+ value: 1.5230000000000001
182
+ - type: precision_at_1000
183
+ value: 0.183
184
+ - type: precision_at_3
185
+ value: 20.671999999999997
186
+ - type: precision_at_5
187
+ value: 15.179
188
+ - type: recall_at_1
189
+ value: 24.043999999999997
190
+ - type: recall_at_10
191
+ value: 51.370000000000005
192
+ - type: recall_at_100
193
+ value: 81.569
194
+ - type: recall_at_1000
195
+ value: 98.053
196
+ - type: recall_at_3
197
+ value: 36.120000000000005
198
+ - type: recall_at_5
199
+ value: 42.829
200
+ - task:
201
+ type: PairClassification
202
+ dataset:
203
+ type: C-MTEB/CMNLI
204
+ name: MTEB Cmnli
205
+ config: default
206
+ split: validation
207
+ revision: None
208
+ metrics:
209
+ - type: cos_sim_accuracy
210
+ value: 75.92303066746842
211
+ - type: cos_sim_ap
212
+ value: 84.39741959629595
213
+ - type: cos_sim_f1
214
+ value: 77.28710064333224
215
+ - type: cos_sim_precision
216
+ value: 72.41520228851655
217
+ - type: cos_sim_recall
218
+ value: 82.8618190320318
219
+ - type: dot_accuracy
220
+ value: 75.92303066746842
221
+ - type: dot_ap
222
+ value: 84.39592659189601
223
+ - type: dot_f1
224
+ value: 77.28710064333224
225
+ - type: dot_precision
226
+ value: 72.41520228851655
227
+ - type: dot_recall
228
+ value: 82.8618190320318
229
+ - type: euclidean_accuracy
230
+ value: 75.92303066746842
231
+ - type: euclidean_ap
232
+ value: 84.39741904478117
233
+ - type: euclidean_f1
234
+ value: 77.28710064333224
235
+ - type: euclidean_precision
236
+ value: 72.41520228851655
237
+ - type: euclidean_recall
238
+ value: 82.8618190320318
239
+ - type: manhattan_accuracy
240
+ value: 75.83884546001202
241
+ - type: manhattan_ap
242
+ value: 84.39482592167423
243
+ - type: manhattan_f1
244
+ value: 77.2419718612394
245
+ - type: manhattan_precision
246
+ value: 71.43424711958681
247
+ - type: manhattan_recall
248
+ value: 84.07762450315643
249
+ - type: max_accuracy
250
+ value: 75.92303066746842
251
+ - type: max_ap
252
+ value: 84.39741959629595
253
+ - type: max_f1
254
+ value: 77.28710064333224
255
+ - task:
256
+ type: Retrieval
257
+ dataset:
258
+ type: C-MTEB/CovidRetrieval
259
+ name: MTEB CovidRetrieval
260
+ config: default
261
+ split: dev
262
+ revision: None
263
+ metrics:
264
+ - type: map_at_1
265
+ value: 67.65
266
+ - type: map_at_10
267
+ value: 75.672
268
+ - type: map_at_100
269
+ value: 76.005
270
+ - type: map_at_1000
271
+ value: 76.007
272
+ - type: map_at_3
273
+ value: 73.867
274
+ - type: map_at_5
275
+ value: 74.949
276
+ - type: mrr_at_1
277
+ value: 67.756
278
+ - type: mrr_at_10
279
+ value: 75.64
280
+ - type: mrr_at_100
281
+ value: 75.973
282
+ - type: mrr_at_1000
283
+ value: 75.97500000000001
284
+ - type: mrr_at_3
285
+ value: 73.867
286
+ - type: mrr_at_5
287
+ value: 74.984
288
+ - type: ndcg_at_1
289
+ value: 67.861
290
+ - type: ndcg_at_10
291
+ value: 79.393
292
+ - type: ndcg_at_100
293
+ value: 81.04400000000001
294
+ - type: ndcg_at_1000
295
+ value: 81.15299999999999
296
+ - type: ndcg_at_3
297
+ value: 75.767
298
+ - type: ndcg_at_5
299
+ value: 77.714
300
+ - type: precision_at_1
301
+ value: 67.861
302
+ - type: precision_at_10
303
+ value: 9.199
304
+ - type: precision_at_100
305
+ value: 0.9979999999999999
306
+ - type: precision_at_1000
307
+ value: 0.101
308
+ - type: precision_at_3
309
+ value: 27.222
310
+ - type: precision_at_5
311
+ value: 17.302
312
+ - type: recall_at_1
313
+ value: 67.65
314
+ - type: recall_at_10
315
+ value: 90.938
316
+ - type: recall_at_100
317
+ value: 98.736
318
+ - type: recall_at_1000
319
+ value: 99.684
320
+ - type: recall_at_3
321
+ value: 81.138
322
+ - type: recall_at_5
323
+ value: 85.827
324
+ - task:
325
+ type: Retrieval
326
+ dataset:
327
+ type: C-MTEB/DuRetrieval
328
+ name: MTEB DuRetrieval
329
+ config: default
330
+ split: dev
331
+ revision: None
332
+ metrics:
333
+ - type: map_at_1
334
+ value: 25.407000000000004
335
+ - type: map_at_10
336
+ value: 79.001
337
+ - type: map_at_100
338
+ value: 81.98299999999999
339
+ - type: map_at_1000
340
+ value: 82.021
341
+ - type: map_at_3
342
+ value: 54.25600000000001
343
+ - type: map_at_5
344
+ value: 68.918
345
+ - type: mrr_at_1
346
+ value: 89.14999999999999
347
+ - type: mrr_at_10
348
+ value: 92.548
349
+ - type: mrr_at_100
350
+ value: 92.61399999999999
351
+ - type: mrr_at_1000
352
+ value: 92.616
353
+ - type: mrr_at_3
354
+ value: 92.175
355
+ - type: mrr_at_5
356
+ value: 92.432
357
+ - type: ndcg_at_1
358
+ value: 89.14999999999999
359
+ - type: ndcg_at_10
360
+ value: 86.588
361
+ - type: ndcg_at_100
362
+ value: 89.48700000000001
363
+ - type: ndcg_at_1000
364
+ value: 89.84100000000001
365
+ - type: ndcg_at_3
366
+ value: 85.00999999999999
367
+ - type: ndcg_at_5
368
+ value: 84.301
369
+ - type: precision_at_1
370
+ value: 89.14999999999999
371
+ - type: precision_at_10
372
+ value: 41.71
373
+ - type: precision_at_100
374
+ value: 4.807
375
+ - type: precision_at_1000
376
+ value: 0.48900000000000005
377
+ - type: precision_at_3
378
+ value: 76.417
379
+ - type: precision_at_5
380
+ value: 64.95
381
+ - type: recall_at_1
382
+ value: 25.407000000000004
383
+ - type: recall_at_10
384
+ value: 88.221
385
+ - type: recall_at_100
386
+ value: 97.527
387
+ - type: recall_at_1000
388
+ value: 99.396
389
+ - type: recall_at_3
390
+ value: 56.751
391
+ - type: recall_at_5
392
+ value: 74.191
393
+ - task:
394
+ type: Retrieval
395
+ dataset:
396
+ type: C-MTEB/EcomRetrieval
397
+ name: MTEB EcomRetrieval
398
+ config: default
399
+ split: dev
400
+ revision: None
401
+ metrics:
402
+ - type: map_at_1
403
+ value: 47.599999999999994
404
+ - type: map_at_10
405
+ value: 57.15
406
+ - type: map_at_100
407
+ value: 57.789
408
+ - type: map_at_1000
409
+ value: 57.80800000000001
410
+ - type: map_at_3
411
+ value: 54.467
412
+ - type: map_at_5
413
+ value: 56.016999999999996
414
+ - type: mrr_at_1
415
+ value: 47.599999999999994
416
+ - type: mrr_at_10
417
+ value: 57.15
418
+ - type: mrr_at_100
419
+ value: 57.789
420
+ - type: mrr_at_1000
421
+ value: 57.80800000000001
422
+ - type: mrr_at_3
423
+ value: 54.467
424
+ - type: mrr_at_5
425
+ value: 56.016999999999996
426
+ - type: ndcg_at_1
427
+ value: 47.599999999999994
428
+ - type: ndcg_at_10
429
+ value: 62.304
430
+ - type: ndcg_at_100
431
+ value: 65.32900000000001
432
+ - type: ndcg_at_1000
433
+ value: 65.837
434
+ - type: ndcg_at_3
435
+ value: 56.757000000000005
436
+ - type: ndcg_at_5
437
+ value: 59.575
438
+ - type: precision_at_1
439
+ value: 47.599999999999994
440
+ - type: precision_at_10
441
+ value: 7.870000000000001
442
+ - type: precision_at_100
443
+ value: 0.9259999999999999
444
+ - type: precision_at_1000
445
+ value: 0.097
446
+ - type: precision_at_3
447
+ value: 21.133
448
+ - type: precision_at_5
449
+ value: 14.06
450
+ - type: recall_at_1
451
+ value: 47.599999999999994
452
+ - type: recall_at_10
453
+ value: 78.7
454
+ - type: recall_at_100
455
+ value: 92.60000000000001
456
+ - type: recall_at_1000
457
+ value: 96.6
458
+ - type: recall_at_3
459
+ value: 63.4
460
+ - type: recall_at_5
461
+ value: 70.3
462
+ - task:
463
+ type: Classification
464
+ dataset:
465
+ type: C-MTEB/IFlyTek-classification
466
+ name: MTEB IFlyTek
467
+ config: default
468
+ split: validation
469
+ revision: None
470
+ metrics:
471
+ - type: accuracy
472
+ value: 48.28010773374375
473
+ - type: f1
474
+ value: 35.536995302144916
475
+ - task:
476
+ type: Classification
477
+ dataset:
478
+ type: C-MTEB/JDReview-classification
479
+ name: MTEB JDReview
480
+ config: default
481
+ split: test
482
+ revision: None
483
+ metrics:
484
+ - type: accuracy
485
+ value: 84.8405253283302
486
+ - type: ap
487
+ value: 52.35323515091401
488
+ - type: f1
489
+ value: 79.50069160202494
490
+ - task:
491
+ type: STS
492
+ dataset:
493
+ type: C-MTEB/LCQMC
494
+ name: MTEB LCQMC
495
+ config: default
496
+ split: test
497
+ revision: None
498
+ metrics:
499
+ - type: cos_sim_pearson
500
+ value: 69.68404288794713
501
+ - type: cos_sim_spearman
502
+ value: 77.06824442481803
503
+ - type: euclidean_pearson
504
+ value: 75.47746745802166
505
+ - type: euclidean_spearman
506
+ value: 77.06825328995878
507
+ - type: manhattan_pearson
508
+ value: 75.46220581621667
509
+ - type: manhattan_spearman
510
+ value: 77.05100926919137
511
+ - task:
512
+ type: Retrieval
513
+ dataset:
514
+ type: C-MTEB/MMarcoRetrieval
515
+ name: MTEB MMarcoRetrieval
516
+ config: default
517
+ split: dev
518
+ revision: None
519
+ metrics:
520
+ - type: map_at_1
521
+ value: 65.36800000000001
522
+ - type: map_at_10
523
+ value: 74.29400000000001
524
+ - type: map_at_100
525
+ value: 74.653
526
+ - type: map_at_1000
527
+ value: 74.664
528
+ - type: map_at_3
529
+ value: 72.416
530
+ - type: map_at_5
531
+ value: 73.658
532
+ - type: mrr_at_1
533
+ value: 67.50699999999999
534
+ - type: mrr_at_10
535
+ value: 74.85300000000001
536
+ - type: mrr_at_100
537
+ value: 75.17399999999999
538
+ - type: mrr_at_1000
539
+ value: 75.184
540
+ - type: mrr_at_3
541
+ value: 73.235
542
+ - type: mrr_at_5
543
+ value: 74.298
544
+ - type: ndcg_at_1
545
+ value: 67.50699999999999
546
+ - type: ndcg_at_10
547
+ value: 77.948
548
+ - type: ndcg_at_100
549
+ value: 79.55499999999999
550
+ - type: ndcg_at_1000
551
+ value: 79.864
552
+ - type: ndcg_at_3
553
+ value: 74.434
554
+ - type: ndcg_at_5
555
+ value: 76.504
556
+ - type: precision_at_1
557
+ value: 67.50699999999999
558
+ - type: precision_at_10
559
+ value: 9.423
560
+ - type: precision_at_100
561
+ value: 1.022
562
+ - type: precision_at_1000
563
+ value: 0.105
564
+ - type: precision_at_3
565
+ value: 27.975
566
+ - type: precision_at_5
567
+ value: 17.891000000000002
568
+ - type: recall_at_1
569
+ value: 65.36800000000001
570
+ - type: recall_at_10
571
+ value: 88.633
572
+ - type: recall_at_100
573
+ value: 95.889
574
+ - type: recall_at_1000
575
+ value: 98.346
576
+ - type: recall_at_3
577
+ value: 79.404
578
+ - type: recall_at_5
579
+ value: 84.292
580
+ - task:
581
+ type: Classification
582
+ dataset:
583
+ type: mteb/amazon_massive_intent
584
+ name: MTEB MassiveIntentClassification (zh-CN)
585
+ config: zh-CN
586
+ split: test
587
+ revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
588
+ metrics:
589
+ - type: accuracy
590
+ value: 67.45124411566913
591
+ - type: f1
592
+ value: 64.77175074397455
593
+ - task:
594
+ type: Classification
595
+ dataset:
596
+ type: mteb/amazon_massive_scenario
597
+ name: MTEB MassiveScenarioClassification (zh-CN)
598
+ config: zh-CN
599
+ split: test
600
+ revision: 7d571f92784cd94a019292a1f45445077d0ef634
601
+ metrics:
602
+ - type: accuracy
603
+ value: 73.08002689979824
604
+ - type: f1
605
+ value: 72.65358173635958
606
+ - task:
607
+ type: Retrieval
608
+ dataset:
609
+ type: C-MTEB/MedicalRetrieval
610
+ name: MTEB MedicalRetrieval
611
+ config: default
612
+ split: dev
613
+ revision: None
614
+ metrics:
615
+ - type: map_at_1
616
+ value: 48.699999999999996
617
+ - type: map_at_10
618
+ value: 54.871
619
+ - type: map_at_100
620
+ value: 55.381
621
+ - type: map_at_1000
622
+ value: 55.43599999999999
623
+ - type: map_at_3
624
+ value: 53.367
625
+ - type: map_at_5
626
+ value: 54.257
627
+ - type: mrr_at_1
628
+ value: 48.699999999999996
629
+ - type: mrr_at_10
630
+ value: 54.871
631
+ - type: mrr_at_100
632
+ value: 55.381
633
+ - type: mrr_at_1000
634
+ value: 55.43599999999999
635
+ - type: mrr_at_3
636
+ value: 53.367
637
+ - type: mrr_at_5
638
+ value: 54.257
639
+ - type: ndcg_at_1
640
+ value: 48.699999999999996
641
+ - type: ndcg_at_10
642
+ value: 57.94200000000001
643
+ - type: ndcg_at_100
644
+ value: 60.75000000000001
645
+ - type: ndcg_at_1000
646
+ value: 62.400999999999996
647
+ - type: ndcg_at_3
648
+ value: 54.867
649
+ - type: ndcg_at_5
650
+ value: 56.493
651
+ - type: precision_at_1
652
+ value: 48.699999999999996
653
+ - type: precision_at_10
654
+ value: 6.76
655
+ - type: precision_at_100
656
+ value: 0.815
657
+ - type: precision_at_1000
658
+ value: 0.095
659
+ - type: precision_at_3
660
+ value: 19.733
661
+ - type: precision_at_5
662
+ value: 12.64
663
+ - type: recall_at_1
664
+ value: 48.699999999999996
665
+ - type: recall_at_10
666
+ value: 67.60000000000001
667
+ - type: recall_at_100
668
+ value: 81.5
669
+ - type: recall_at_1000
670
+ value: 94.89999999999999
671
+ - type: recall_at_3
672
+ value: 59.199999999999996
673
+ - type: recall_at_5
674
+ value: 63.2
675
+ - task:
676
+ type: Classification
677
+ dataset:
678
+ type: C-MTEB/MultilingualSentiment-classification
679
+ name: MTEB MultilingualSentiment
680
+ config: default
681
+ split: validation
682
+ revision: None
683
+ metrics:
684
+ - type: accuracy
685
+ value: 71.27000000000001
686
+ - type: f1
687
+ value: 70.46516219039894
688
+ - task:
689
+ type: PairClassification
690
+ dataset:
691
+ type: C-MTEB/OCNLI
692
+ name: MTEB Ocnli
693
+ config: default
694
+ split: validation
695
+ revision: None
696
+ metrics:
697
+ - type: cos_sim_accuracy
698
+ value: 69.89713048186248
699
+ - type: cos_sim_ap
700
+ value: 74.75296949416844
701
+ - type: cos_sim_f1
702
+ value: 73.0820399113082
703
+ - type: cos_sim_precision
704
+ value: 62.99694189602446
705
+ - type: cos_sim_recall
706
+ value: 87.0116156282999
707
+ - type: dot_accuracy
708
+ value: 69.89713048186248
709
+ - type: dot_ap
710
+ value: 74.75289228002875
711
+ - type: dot_f1
712
+ value: 73.0820399113082
713
+ - type: dot_precision
714
+ value: 62.99694189602446
715
+ - type: dot_recall
716
+ value: 87.0116156282999
717
+ - type: euclidean_accuracy
718
+ value: 69.89713048186248
719
+ - type: euclidean_ap
720
+ value: 74.75289228002875
721
+ - type: euclidean_f1
722
+ value: 73.0820399113082
723
+ - type: euclidean_precision
724
+ value: 62.99694189602446
725
+ - type: euclidean_recall
726
+ value: 87.0116156282999
727
+ - type: manhattan_accuracy
728
+ value: 69.9512723335138
729
+ - type: manhattan_ap
730
+ value: 74.63572749955489
731
+ - type: manhattan_f1
732
+ value: 72.80663465735486
733
+ - type: manhattan_precision
734
+ value: 62.05357142857143
735
+ - type: manhattan_recall
736
+ value: 88.0675818373812
737
+ - type: max_accuracy
738
+ value: 69.9512723335138
739
+ - type: max_ap
740
+ value: 74.75296949416844
741
+ - type: max_f1
742
+ value: 73.0820399113082
743
+ - task:
744
+ type: Classification
745
+ dataset:
746
+ type: C-MTEB/OnlineShopping-classification
747
+ name: MTEB OnlineShopping
748
+ config: default
749
+ split: test
750
+ revision: None
751
+ metrics:
752
+ - type: accuracy
753
+ value: 91.38
754
+ - type: ap
755
+ value: 89.14371766660247
756
+ - type: f1
757
+ value: 91.3668296299526
758
+ - task:
759
+ type: STS
760
+ dataset:
761
+ type: C-MTEB/PAWSX
762
+ name: MTEB PAWSX
763
+ config: default
764
+ split: test
765
+ revision: None
766
+ metrics:
767
+ - type: cos_sim_pearson
768
+ value: 23.621683997579606
769
+ - type: cos_sim_spearman
770
+ value: 29.46714129804792
771
+ - type: euclidean_pearson
772
+ value: 29.841725912733487
773
+ - type: euclidean_spearman
774
+ value: 29.466951993706992
775
+ - type: manhattan_pearson
776
+ value: 29.853598937043625
777
+ - type: manhattan_spearman
778
+ value: 29.42340511723847
779
+ - task:
780
+ type: STS
781
+ dataset:
782
+ type: C-MTEB/QBQTC
783
+ name: MTEB QBQTC
784
+ config: default
785
+ split: test
786
+ revision: None
787
+ metrics:
788
+ - type: cos_sim_pearson
789
+ value: 34.86196986379606
790
+ - type: cos_sim_spearman
791
+ value: 37.316873994339986
792
+ - type: euclidean_pearson
793
+ value: 35.52672274329054
794
+ - type: euclidean_spearman
795
+ value: 37.316799507511014
796
+ - type: manhattan_pearson
797
+ value: 35.55879437844226
798
+ - type: manhattan_spearman
799
+ value: 37.369433247035474
800
+ - task:
801
+ type: STS
802
+ dataset:
803
+ type: mteb/sts22-crosslingual-sts
804
+ name: MTEB STS22 (zh)
805
+ config: zh
806
+ split: test
807
+ revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
808
+ metrics:
809
+ - type: cos_sim_pearson
810
+ value: 68.7924534800626
811
+ - type: cos_sim_spearman
812
+ value: 69.45014686127368
813
+ - type: euclidean_pearson
814
+ value: 69.12500964503516
815
+ - type: euclidean_spearman
816
+ value: 69.45014686127368
817
+ - type: manhattan_pearson
818
+ value: 70.53825064823806
819
+ - type: manhattan_spearman
820
+ value: 70.67595198226869
821
+ - task:
822
+ type: STS
823
+ dataset:
824
+ type: C-MTEB/STSB
825
+ name: MTEB STSB
826
+ config: default
827
+ split: test
828
+ revision: None
829
+ metrics:
830
+ - type: cos_sim_pearson
831
+ value: 79.02281275805849
832
+ - type: cos_sim_spearman
833
+ value: 79.69275718339352
834
+ - type: euclidean_pearson
835
+ value: 79.39660648560955
836
+ - type: euclidean_spearman
837
+ value: 79.69291851788452
838
+ - type: manhattan_pearson
839
+ value: 79.3382690172365
840
+ - type: manhattan_spearman
841
+ value: 79.63605584076028
842
+ - task:
843
+ type: Reranking
844
+ dataset:
845
+ type: C-MTEB/T2Reranking
846
+ name: MTEB T2Reranking
847
+ config: default
848
+ split: dev
849
+ revision: None
850
+ metrics:
851
+ - type: map
852
+ value: 66.1994271234341
853
+ - type: mrr
854
+ value: 75.76681067371655
855
+ - task:
856
+ type: Retrieval
857
+ dataset:
858
+ type: C-MTEB/T2Retrieval
859
+ name: MTEB T2Retrieval
860
+ config: default
861
+ split: dev
862
+ revision: None
863
+ metrics:
864
+ - type: map_at_1
865
+ value: 26.594
866
+ - type: map_at_10
867
+ value: 75.27199999999999
868
+ - type: map_at_100
869
+ value: 78.96
870
+ - type: map_at_1000
871
+ value: 79.032
872
+ - type: map_at_3
873
+ value: 52.76
874
+ - type: map_at_5
875
+ value: 64.967
876
+ - type: mrr_at_1
877
+ value: 88.721
878
+ - type: mrr_at_10
879
+ value: 91.38
880
+ - type: mrr_at_100
881
+ value: 91.484
882
+ - type: mrr_at_1000
883
+ value: 91.489
884
+ - type: mrr_at_3
885
+ value: 90.901
886
+ - type: mrr_at_5
887
+ value: 91.21000000000001
888
+ - type: ndcg_at_1
889
+ value: 88.721
890
+ - type: ndcg_at_10
891
+ value: 83.099
892
+ - type: ndcg_at_100
893
+ value: 86.938
894
+ - type: ndcg_at_1000
895
+ value: 87.644
896
+ - type: ndcg_at_3
897
+ value: 84.573
898
+ - type: ndcg_at_5
899
+ value: 83.131
900
+ - type: precision_at_1
901
+ value: 88.721
902
+ - type: precision_at_10
903
+ value: 41.506
904
+ - type: precision_at_100
905
+ value: 4.99
906
+ - type: precision_at_1000
907
+ value: 0.515
908
+ - type: precision_at_3
909
+ value: 74.214
910
+ - type: precision_at_5
911
+ value: 62.244
912
+ - type: recall_at_1
913
+ value: 26.594
914
+ - type: recall_at_10
915
+ value: 82.121
916
+ - type: recall_at_100
917
+ value: 94.643
918
+ - type: recall_at_1000
919
+ value: 98.261
920
+ - type: recall_at_3
921
+ value: 54.539
922
+ - type: recall_at_5
923
+ value: 68.573
924
+ - task:
925
+ type: Classification
926
+ dataset:
927
+ type: C-MTEB/TNews-classification
928
+ name: MTEB TNews
929
+ config: default
930
+ split: validation
931
+ revision: None
932
+ metrics:
933
+ - type: accuracy
934
+ value: 51.845
935
+ - type: f1
936
+ value: 49.97529772676145
937
+ - task:
938
+ type: Clustering
939
+ dataset:
940
+ type: C-MTEB/ThuNewsClusteringP2P
941
+ name: MTEB ThuNewsClusteringP2P
942
+ config: default
943
+ split: test
944
+ revision: None
945
+ metrics:
946
+ - type: v_measure
947
+ value: 62.34936773593232
948
+ - task:
949
+ type: Clustering
950
+ dataset:
951
+ type: C-MTEB/ThuNewsClusteringS2S
952
+ name: MTEB ThuNewsClusteringS2S
953
+ config: default
954
+ split: test
955
+ revision: None
956
+ metrics:
957
+ - type: v_measure
958
+ value: 58.65057354232379
959
+ - task:
960
+ type: Retrieval
961
+ dataset:
962
+ type: C-MTEB/VideoRetrieval
963
+ name: MTEB VideoRetrieval
964
+ config: default
965
+ split: dev
966
+ revision: None
967
+ metrics:
968
+ - type: map_at_1
969
+ value: 52.2
970
+ - type: map_at_10
971
+ value: 62.669
972
+ - type: map_at_100
973
+ value: 63.239000000000004
974
+ - type: map_at_1000
975
+ value: 63.253
976
+ - type: map_at_3
977
+ value: 60.267
978
+ - type: map_at_5
979
+ value: 61.772000000000006
980
+ - type: mrr_at_1
981
+ value: 52.2
982
+ - type: mrr_at_10
983
+ value: 62.669
984
+ - type: mrr_at_100
985
+ value: 63.239000000000004
986
+ - type: mrr_at_1000
987
+ value: 63.253
988
+ - type: mrr_at_3
989
+ value: 60.267
990
+ - type: mrr_at_5
991
+ value: 61.772000000000006
992
+ - type: ndcg_at_1
993
+ value: 52.2
994
+ - type: ndcg_at_10
995
+ value: 67.583
996
+ - type: ndcg_at_100
997
+ value: 70.30499999999999
998
+ - type: ndcg_at_1000
999
+ value: 70.652
1000
+ - type: ndcg_at_3
1001
+ value: 62.775999999999996
1002
+ - type: ndcg_at_5
1003
+ value: 65.47
1004
+ - type: precision_at_1
1005
+ value: 52.2
1006
+ - type: precision_at_10
1007
+ value: 8.290000000000001
1008
+ - type: precision_at_100
1009
+ value: 0.955
1010
+ - type: precision_at_1000
1011
+ value: 0.098
1012
+ - type: precision_at_3
1013
+ value: 23.333000000000002
1014
+ - type: precision_at_5
1015
+ value: 15.299999999999999
1016
+ - type: recall_at_1
1017
+ value: 52.2
1018
+ - type: recall_at_10
1019
+ value: 82.89999999999999
1020
+ - type: recall_at_100
1021
+ value: 95.5
1022
+ - type: recall_at_1000
1023
+ value: 98.2
1024
+ - type: recall_at_3
1025
+ value: 70.0
1026
+ - type: recall_at_5
1027
+ value: 76.5
1028
+ - task:
1029
+ type: Classification
1030
+ dataset:
1031
+ type: C-MTEB/waimai-classification
1032
+ name: MTEB Waimai
1033
+ config: default
1034
+ split: test
1035
+ revision: None
1036
+ metrics:
1037
+ - type: accuracy
1038
+ value: 86.64999999999999
1039
+ - type: ap
1040
+ value: 69.90209999390807
1041
+ - type: f1
1042
+ value: 84.9231810656075
1043
+ ---