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stella-base-zh-v3-1792d

Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
mteb
Eval Results
Inference Endpoints
Model card Files Community
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  ---
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  pipeline_tag: sentence-similarity
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  tags:
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- - sentence-transformers
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- - feature-extraction
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- - sentence-similarity
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- - mteb
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  model-index:
9
- - name: stella-base-zh-v3-1792d
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- results:
11
- - task:
12
- type: STS
13
- dataset:
14
- type: C-MTEB/AFQMC
15
- name: MTEB AFQMC
16
- config: default
17
- split: validation
18
- revision: None
19
- metrics:
20
- - type: cos_sim_pearson
21
- value: 54.5145388936202
22
- - type: cos_sim_spearman
23
- value: 59.223125058197134
24
- - type: euclidean_pearson
25
- value: 57.819377838734695
26
- - type: euclidean_spearman
27
- value: 59.22310494948463
28
- - type: manhattan_pearson
29
- value: 57.44029759610327
30
- - type: manhattan_spearman
31
- value: 58.88336250854381
32
- - task:
33
- type: STS
34
- dataset:
35
- type: C-MTEB/ATEC
36
- name: MTEB ATEC
37
- config: default
38
- split: test
39
- revision: None
40
- metrics:
41
- - type: cos_sim_pearson
42
- value: 54.544243591344866
43
- - type: cos_sim_spearman
44
- value: 58.43052988038229
45
- - type: euclidean_pearson
46
- value: 62.1608405146189
47
- - type: euclidean_spearman
48
- value: 58.43052762862396
49
- - type: manhattan_pearson
50
- value: 61.88443779892169
51
- - type: manhattan_spearman
52
- value: 58.26899143609596
53
- - task:
54
- type: Classification
55
- dataset:
56
- type: mteb/amazon_reviews_multi
57
- name: MTEB AmazonReviewsClassification (zh)
58
- config: zh
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- split: test
60
- revision: 1399c76144fd37290681b995c656ef9b2e06e26d
61
- metrics:
62
- - type: accuracy
63
- value: 46.343999999999994
64
- - type: f1
65
- value: 44.46931958420461
66
- - task:
67
- type: STS
68
- dataset:
69
- type: C-MTEB/BQ
70
- name: MTEB BQ
71
- config: default
72
- split: test
73
- revision: None
74
- metrics:
75
- - type: cos_sim_pearson
76
- value: 68.52081000538426
77
- - type: cos_sim_spearman
78
- value: 70.44089935351529
79
- - type: euclidean_pearson
80
- value: 69.24671010626395
81
- - type: euclidean_spearman
82
- value: 70.44090281761693
83
- - type: manhattan_pearson
84
- value: 69.00737718109357
85
- - type: manhattan_spearman
86
- value: 70.24344902456502
87
- - task:
88
- type: Clustering
89
- dataset:
90
- type: C-MTEB/CLSClusteringP2P
91
- name: MTEB CLSClusteringP2P
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- config: default
93
- split: test
94
- revision: None
95
- metrics:
96
- - type: v_measure
97
- value: 42.86119436460332
98
- - task:
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- type: Clustering
100
- dataset:
101
- type: C-MTEB/CLSClusteringS2S
102
- name: MTEB CLSClusteringS2S
103
- config: default
104
- split: test
105
- revision: None
106
- metrics:
107
- - type: v_measure
108
- value: 39.97521728440642
109
- - task:
110
- type: Reranking
111
- dataset:
112
- type: C-MTEB/CMedQAv1-reranking
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- name: MTEB CMedQAv1
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- config: default
115
- split: test
116
- revision: None
117
- metrics:
118
- - type: map
119
- value: 88.34151862240452
120
- - type: mrr
121
- value: 90.40380952380953
122
- - task:
123
- type: Reranking
124
- dataset:
125
- type: C-MTEB/CMedQAv2-reranking
126
- name: MTEB CMedQAv2
127
- config: default
128
- split: test
129
- revision: None
130
- metrics:
131
- - type: map
132
- value: 89.06288758814637
133
- - type: mrr
134
- value: 90.91285714285713
135
- - task:
136
- type: Retrieval
137
- dataset:
138
- type: C-MTEB/CmedqaRetrieval
139
- name: MTEB CmedqaRetrieval
140
- config: default
141
- split: dev
142
- revision: None
143
- metrics:
144
- - type: map_at_1
145
- value: 25.651000000000003
146
- - type: map_at_10
147
- value: 38.576
148
- - type: map_at_100
149
- value: 40.534
150
- - type: map_at_1000
151
- value: 40.64
152
- - type: map_at_3
153
- value: 34.016000000000005
154
- - type: map_at_5
155
- value: 36.675999999999995
156
- - type: mrr_at_1
157
- value: 39.06
158
- - type: mrr_at_10
159
- value: 47.278
160
- - type: mrr_at_100
161
- value: 48.272999999999996
162
- - type: mrr_at_1000
163
- value: 48.314
164
- - type: mrr_at_3
165
- value: 44.461
166
- - type: mrr_at_5
167
- value: 46.107
168
- - type: ndcg_at_1
169
- value: 39.06
170
- - type: ndcg_at_10
171
- value: 45.384
172
- - type: ndcg_at_100
173
- value: 52.796
174
- - type: ndcg_at_1000
175
- value: 54.55
176
- - type: ndcg_at_3
177
- value: 39.497
178
- - type: ndcg_at_5
179
- value: 42.189
180
- - type: precision_at_1
181
- value: 39.06
182
- - type: precision_at_10
183
- value: 10.17
184
- - type: precision_at_100
185
- value: 1.6179999999999999
186
- - type: precision_at_1000
187
- value: 0.184
188
- - type: precision_at_3
189
- value: 22.247
190
- - type: precision_at_5
191
- value: 16.529
192
- - type: recall_at_1
193
- value: 25.651000000000003
194
- - type: recall_at_10
195
- value: 56.82899999999999
196
- - type: recall_at_100
197
- value: 87.134
198
- - type: recall_at_1000
199
- value: 98.709
200
- - type: recall_at_3
201
- value: 39.461
202
- - type: recall_at_5
203
- value: 47.329
204
- - task:
205
- type: PairClassification
206
- dataset:
207
- type: C-MTEB/CMNLI
208
- name: MTEB Cmnli
209
- config: default
210
- split: validation
211
- revision: None
212
- metrics:
213
- - type: cos_sim_accuracy
214
- value: 83.1870114251353
215
- - type: cos_sim_ap
216
- value: 90.42393852164342
217
- - type: cos_sim_f1
218
- value: 84.10685985963323
219
- - type: cos_sim_precision
220
- value: 81.5229317533465
221
- - type: cos_sim_recall
222
- value: 86.85994856207621
223
- - type: dot_accuracy
224
- value: 83.1870114251353
225
- - type: dot_ap
226
- value: 90.41339758845682
227
- - type: dot_f1
228
- value: 84.10685985963323
229
- - type: dot_precision
230
- value: 81.5229317533465
231
- - type: dot_recall
232
- value: 86.85994856207621
233
- - type: euclidean_accuracy
234
- value: 83.1870114251353
235
- - type: euclidean_ap
236
- value: 90.42393581056393
237
- - type: euclidean_f1
238
- value: 84.10685985963323
239
- - type: euclidean_precision
240
- value: 81.5229317533465
241
- - type: euclidean_recall
242
- value: 86.85994856207621
243
- - type: manhattan_accuracy
244
- value: 82.77811184606134
245
- - type: manhattan_ap
246
- value: 90.18115714681704
247
- - type: manhattan_f1
248
- value: 83.75083130126357
249
- - type: manhattan_precision
250
- value: 79.62065331928345
251
- - type: manhattan_recall
252
- value: 88.33294365209258
253
- - type: max_accuracy
254
- value: 83.1870114251353
255
- - type: max_ap
256
- value: 90.42393852164342
257
- - type: max_f1
258
- value: 84.10685985963323
259
- - task:
260
- type: Retrieval
261
- dataset:
262
- type: C-MTEB/CovidRetrieval
263
- name: MTEB CovidRetrieval
264
- config: default
265
- split: dev
266
- revision: None
267
- metrics:
268
- - type: map_at_1
269
- value: 68.388
270
- - type: map_at_10
271
- value: 76.819
272
- - type: map_at_100
273
- value: 77.153
274
- - type: map_at_1000
275
- value: 77.16
276
- - type: map_at_3
277
- value: 74.98700000000001
278
- - type: map_at_5
279
- value: 76.101
280
- - type: mrr_at_1
281
- value: 68.599
282
- - type: mrr_at_10
283
- value: 76.844
284
- - type: mrr_at_100
285
- value: 77.168
286
- - type: mrr_at_1000
287
- value: 77.17500000000001
288
- - type: mrr_at_3
289
- value: 75.044
290
- - type: mrr_at_5
291
- value: 76.208
292
- - type: ndcg_at_1
293
- value: 68.599
294
- - type: ndcg_at_10
295
- value: 80.613
296
- - type: ndcg_at_100
297
- value: 82.017
298
- - type: ndcg_at_1000
299
- value: 82.19300000000001
300
- - type: ndcg_at_3
301
- value: 76.956
302
- - type: ndcg_at_5
303
- value: 78.962
304
- - type: precision_at_1
305
- value: 68.599
306
- - type: precision_at_10
307
- value: 9.336
308
- - type: precision_at_100
309
- value: 0.996
310
- - type: precision_at_1000
311
- value: 0.101
312
- - type: precision_at_3
313
- value: 27.678000000000004
314
- - type: precision_at_5
315
- value: 17.619
316
- - type: recall_at_1
317
- value: 68.388
318
- - type: recall_at_10
319
- value: 92.36
320
- - type: recall_at_100
321
- value: 98.52499999999999
322
- - type: recall_at_1000
323
- value: 99.895
324
- - type: recall_at_3
325
- value: 82.53399999999999
326
- - type: recall_at_5
327
- value: 87.355
328
- - task:
329
- type: Retrieval
330
- dataset:
331
- type: C-MTEB/DuRetrieval
332
- name: MTEB DuRetrieval
333
- config: default
334
- split: dev
335
- revision: None
336
- metrics:
337
- - type: map_at_1
338
- value: 25.1
339
- - type: map_at_10
340
- value: 77.71000000000001
341
- - type: map_at_100
342
- value: 80.638
343
- - type: map_at_1000
344
- value: 80.679
345
- - type: map_at_3
346
- value: 53.187
347
- - type: map_at_5
348
- value: 67.735
349
- - type: mrr_at_1
350
- value: 87.8
351
- - type: mrr_at_10
352
- value: 91.8
353
- - type: mrr_at_100
354
- value: 91.893
355
- - type: mrr_at_1000
356
- value: 91.89500000000001
357
- - type: mrr_at_3
358
- value: 91.51700000000001
359
- - type: mrr_at_5
360
- value: 91.704
361
- - type: ndcg_at_1
362
- value: 87.8
363
- - type: ndcg_at_10
364
- value: 85.55
365
- - type: ndcg_at_100
366
- value: 88.626
367
- - type: ndcg_at_1000
368
- value: 89.021
369
- - type: ndcg_at_3
370
- value: 83.94
371
- - type: ndcg_at_5
372
- value: 83.259
373
- - type: precision_at_1
374
- value: 87.8
375
- - type: precision_at_10
376
- value: 41.295
377
- - type: precision_at_100
378
- value: 4.781
379
- - type: precision_at_1000
380
- value: 0.488
381
- - type: precision_at_3
382
- value: 75.3
383
- - type: precision_at_5
384
- value: 64.13
385
- - type: recall_at_1
386
- value: 25.1
387
- - type: recall_at_10
388
- value: 87.076
389
- - type: recall_at_100
390
- value: 97.095
391
- - type: recall_at_1000
392
- value: 99.129
393
- - type: recall_at_3
394
- value: 56.013999999999996
395
- - type: recall_at_5
396
- value: 73.2
397
- - task:
398
- type: Retrieval
399
- dataset:
400
- type: C-MTEB/EcomRetrieval
401
- name: MTEB EcomRetrieval
402
- config: default
403
- split: dev
404
- revision: None
405
- metrics:
406
- - type: map_at_1
407
- value: 53.300000000000004
408
- - type: map_at_10
409
- value: 63.01
410
- - type: map_at_100
411
- value: 63.574
412
- - type: map_at_1000
413
- value: 63.587
414
- - type: map_at_3
415
- value: 60.783
416
- - type: map_at_5
417
- value: 62.098
418
- - type: mrr_at_1
419
- value: 53.300000000000004
420
- - type: mrr_at_10
421
- value: 63.01
422
- - type: mrr_at_100
423
- value: 63.574
424
- - type: mrr_at_1000
425
- value: 63.587
426
- - type: mrr_at_3
427
- value: 60.783
428
- - type: mrr_at_5
429
- value: 62.098
430
- - type: ndcg_at_1
431
- value: 53.300000000000004
432
- - type: ndcg_at_10
433
- value: 67.876
434
- - type: ndcg_at_100
435
- value: 70.434
436
- - type: ndcg_at_1000
437
- value: 70.753
438
- - type: ndcg_at_3
439
- value: 63.275000000000006
440
- - type: ndcg_at_5
441
- value: 65.654
442
- - type: precision_at_1
443
- value: 53.300000000000004
444
- - type: precision_at_10
445
- value: 8.32
446
- - type: precision_at_100
447
- value: 0.9480000000000001
448
- - type: precision_at_1000
449
- value: 0.097
450
- - type: precision_at_3
451
- value: 23.5
452
- - type: precision_at_5
453
- value: 15.260000000000002
454
- - type: recall_at_1
455
- value: 53.300000000000004
456
- - type: recall_at_10
457
- value: 83.2
458
- - type: recall_at_100
459
- value: 94.8
460
- - type: recall_at_1000
461
- value: 97.3
462
- - type: recall_at_3
463
- value: 70.5
464
- - type: recall_at_5
465
- value: 76.3
466
- - task:
467
- type: Classification
468
- dataset:
469
- type: C-MTEB/IFlyTek-classification
470
- name: MTEB IFlyTek
471
- config: default
472
- split: validation
473
- revision: None
474
- metrics:
475
- - type: accuracy
476
- value: 49.92689495959984
477
- - type: f1
478
- value: 37.784780470986625
479
- - task:
480
- type: Classification
481
- dataset:
482
- type: C-MTEB/JDReview-classification
483
- name: MTEB JDReview
484
- config: default
485
- split: test
486
- revision: None
487
- metrics:
488
- - type: accuracy
489
- value: 86.26641651031895
490
- - type: ap
491
- value: 54.50750244841821
492
- - type: f1
493
- value: 80.94927946681523
494
- - task:
495
- type: STS
496
- dataset:
497
- type: C-MTEB/LCQMC
498
- name: MTEB LCQMC
499
- config: default
500
- split: test
501
- revision: None
502
- metrics:
503
- - type: cos_sim_pearson
504
- value: 72.3980811478615
505
- - type: cos_sim_spearman
506
- value: 78.26906056425528
507
- - type: euclidean_pearson
508
- value: 77.87705501225068
509
- - type: euclidean_spearman
510
- value: 78.26905834518651
511
- - type: manhattan_pearson
512
- value: 77.77154630197
513
- - type: manhattan_spearman
514
- value: 78.1940918602169
515
- - task:
516
- type: Reranking
517
- dataset:
518
- type: C-MTEB/Mmarco-reranking
519
- name: MTEB MMarcoReranking
520
- config: default
521
- split: dev
522
- revision: None
523
- metrics:
524
- - type: map
525
- value: 27.48003475319453
526
- - type: mrr
527
- value: 26.400793650793652
528
- - task:
529
- type: Retrieval
530
- dataset:
531
- type: C-MTEB/MMarcoRetrieval
532
- name: MTEB MMarcoRetrieval
533
- config: default
534
- split: dev
535
- revision: None
536
- metrics:
537
- - type: map_at_1
538
- value: 64.373
539
- - type: map_at_10
540
- value: 73.604
541
- - type: map_at_100
542
- value: 73.953
543
- - type: map_at_1000
544
- value: 73.965
545
- - type: map_at_3
546
- value: 71.70100000000001
547
- - type: map_at_5
548
- value: 72.859
549
- - type: mrr_at_1
550
- value: 66.676
551
- - type: mrr_at_10
552
- value: 74.248
553
- - type: mrr_at_100
554
- value: 74.56099999999999
555
- - type: mrr_at_1000
556
- value: 74.572
557
- - type: mrr_at_3
558
- value: 72.59100000000001
559
- - type: mrr_at_5
560
- value: 73.592
561
- - type: ndcg_at_1
562
- value: 66.676
563
- - type: ndcg_at_10
564
- value: 77.417
565
- - type: ndcg_at_100
566
- value: 79.006
567
- - type: ndcg_at_1000
568
- value: 79.334
569
- - type: ndcg_at_3
570
- value: 73.787
571
- - type: ndcg_at_5
572
- value: 75.74
573
- - type: precision_at_1
574
- value: 66.676
575
- - type: precision_at_10
576
- value: 9.418
577
- - type: precision_at_100
578
- value: 1.0210000000000001
579
- - type: precision_at_1000
580
- value: 0.105
581
- - type: precision_at_3
582
- value: 27.832
583
- - type: precision_at_5
584
- value: 17.736
585
- - type: recall_at_1
586
- value: 64.373
587
- - type: recall_at_10
588
- value: 88.565
589
- - type: recall_at_100
590
- value: 95.789
591
- - type: recall_at_1000
592
- value: 98.355
593
- - type: recall_at_3
594
- value: 78.914
595
- - type: recall_at_5
596
- value: 83.56
597
- - task:
598
- type: Classification
599
- dataset:
600
- type: mteb/amazon_massive_intent
601
- name: MTEB MassiveIntentClassification (zh-CN)
602
- config: zh-CN
603
- split: test
604
- revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
605
- metrics:
606
- - type: accuracy
607
- value: 72.0544720914593
608
- - type: f1
609
- value: 69.61749470345791
610
- - task:
611
- type: Classification
612
- dataset:
613
- type: mteb/amazon_massive_scenario
614
- name: MTEB MassiveScenarioClassification (zh-CN)
615
- config: zh-CN
616
- split: test
617
- revision: 7d571f92784cd94a019292a1f45445077d0ef634
618
- metrics:
619
- - type: accuracy
620
- value: 75.30262273032953
621
- - type: f1
622
- value: 75.05097671215634
623
- - task:
624
- type: Retrieval
625
- dataset:
626
- type: C-MTEB/MedicalRetrieval
627
- name: MTEB MedicalRetrieval
628
- config: default
629
- split: dev
630
- revision: None
631
- metrics:
632
- - type: map_at_1
633
- value: 55.1
634
- - type: map_at_10
635
- value: 61.284000000000006
636
- - type: map_at_100
637
- value: 61.794000000000004
638
- - type: map_at_1000
639
- value: 61.838
640
- - type: map_at_3
641
- value: 59.75
642
- - type: map_at_5
643
- value: 60.64000000000001
644
- - type: mrr_at_1
645
- value: 55.300000000000004
646
- - type: mrr_at_10
647
- value: 61.38400000000001
648
- - type: mrr_at_100
649
- value: 61.894000000000005
650
- - type: mrr_at_1000
651
- value: 61.938
652
- - type: mrr_at_3
653
- value: 59.85
654
- - type: mrr_at_5
655
- value: 60.74
656
- - type: ndcg_at_1
657
- value: 55.1
658
- - type: ndcg_at_10
659
- value: 64.345
660
- - type: ndcg_at_100
661
- value: 67.148
662
- - type: ndcg_at_1000
663
- value: 68.36
664
- - type: ndcg_at_3
665
- value: 61.182
666
- - type: ndcg_at_5
667
- value: 62.808
668
- - type: precision_at_1
669
- value: 55.1
670
- - type: precision_at_10
671
- value: 7.3999999999999995
672
- - type: precision_at_100
673
- value: 0.8789999999999999
674
- - type: precision_at_1000
675
- value: 0.098
676
- - type: precision_at_3
677
- value: 21.767
678
- - type: precision_at_5
679
- value: 13.86
680
- - type: recall_at_1
681
- value: 55.1
682
- - type: recall_at_10
683
- value: 74.0
684
- - type: recall_at_100
685
- value: 87.9
686
- - type: recall_at_1000
687
- value: 97.5
688
- - type: recall_at_3
689
- value: 65.3
690
- - type: recall_at_5
691
- value: 69.3
692
- - task:
693
- type: Classification
694
- dataset:
695
- type: C-MTEB/MultilingualSentiment-classification
696
- name: MTEB MultilingualSentiment
697
- config: default
698
- split: validation
699
- revision: None
700
- metrics:
701
- - type: accuracy
702
- value: 76.21666666666667
703
- - type: f1
704
- value: 76.03732395559548
705
- - task:
706
- type: PairClassification
707
- dataset:
708
- type: C-MTEB/OCNLI
709
- name: MTEB Ocnli
710
- config: default
711
- split: validation
712
- revision: None
713
- metrics:
714
- - type: cos_sim_accuracy
715
- value: 81.8083378451543
716
- - type: cos_sim_ap
717
- value: 85.43050139514027
718
- - type: cos_sim_f1
719
- value: 83.25969563082965
720
- - type: cos_sim_precision
721
- value: 77.79816513761469
722
- - type: cos_sim_recall
723
- value: 89.54593453009504
724
- - type: dot_accuracy
725
- value: 81.8083378451543
726
- - type: dot_ap
727
- value: 85.43050139514027
728
- - type: dot_f1
729
- value: 83.25969563082965
730
- - type: dot_precision
731
- value: 77.79816513761469
732
- - type: dot_recall
733
- value: 89.54593453009504
734
- - type: euclidean_accuracy
735
- value: 81.8083378451543
736
- - type: euclidean_ap
737
- value: 85.43050139514027
738
- - type: euclidean_f1
739
- value: 83.25969563082965
740
- - type: euclidean_precision
741
- value: 77.79816513761469
742
- - type: euclidean_recall
743
- value: 89.54593453009504
744
- - type: manhattan_accuracy
745
- value: 81.53762858689767
746
- - type: manhattan_ap
747
- value: 84.90556637024838
748
- - type: manhattan_f1
749
- value: 82.90258449304174
750
- - type: manhattan_precision
751
- value: 78.30985915492957
752
- - type: manhattan_recall
753
- value: 88.0675818373812
754
- - type: max_accuracy
755
- value: 81.8083378451543
756
- - type: max_ap
757
- value: 85.43050139514027
758
- - type: max_f1
759
- value: 83.25969563082965
760
- - task:
761
- type: Classification
762
- dataset:
763
- type: C-MTEB/OnlineShopping-classification
764
- name: MTEB OnlineShopping
765
- config: default
766
- split: test
767
- revision: None
768
- metrics:
769
- - type: accuracy
770
- value: 93.53
771
- - type: ap
772
- value: 91.62070655043128
773
- - type: f1
774
- value: 93.51908163199477
775
- - task:
776
- type: STS
777
- dataset:
778
- type: C-MTEB/PAWSX
779
- name: MTEB PAWSX
780
- config: default
781
- split: test
782
- revision: None
783
- metrics:
784
- - type: cos_sim_pearson
785
- value: 38.451787103814375
786
- - type: cos_sim_spearman
787
- value: 43.97299462643919
788
- - type: euclidean_pearson
789
- value: 43.63298716626501
790
- - type: euclidean_spearman
791
- value: 43.973080252178576
792
- - type: manhattan_pearson
793
- value: 43.37465277323481
794
- - type: manhattan_spearman
795
- value: 43.71981281220414
796
- - task:
797
- type: STS
798
- dataset:
799
- type: C-MTEB/QBQTC
800
- name: MTEB QBQTC
801
- config: default
802
- split: test
803
- revision: None
804
- metrics:
805
- - type: cos_sim_pearson
806
- value: 37.75882451277358
807
- - type: cos_sim_spearman
808
- value: 40.0244327844802
809
- - type: euclidean_pearson
810
- value: 38.11050875514246
811
- - type: euclidean_spearman
812
- value: 40.02440987254504
813
- - type: manhattan_pearson
814
- value: 38.03186803221696
815
- - type: manhattan_spearman
816
- value: 39.757452890246775
817
- - task:
818
- type: STS
819
- dataset:
820
- type: mteb/sts22-crosslingual-sts
821
- name: MTEB STS22 (zh)
822
- config: zh
823
- split: test
824
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
825
- metrics:
826
- - type: cos_sim_pearson
827
- value: 65.9133992390713
828
- - type: cos_sim_spearman
829
- value: 66.4894937647578
830
- - type: euclidean_pearson
831
- value: 66.19047142189935
832
- - type: euclidean_spearman
833
- value: 66.4894937647578
834
- - type: manhattan_pearson
835
- value: 66.6960935896136
836
- - type: manhattan_spearman
837
- value: 66.88179996508133
838
- - task:
839
- type: STS
840
- dataset:
841
- type: C-MTEB/STSB
842
- name: MTEB STSB
843
- config: default
844
- split: test
845
- revision: None
846
- metrics:
847
- - type: cos_sim_pearson
848
- value: 80.55099417946924
849
- - type: cos_sim_spearman
850
- value: 83.05000687568048
851
- - type: euclidean_pearson
852
- value: 82.62744668792926
853
- - type: euclidean_spearman
854
- value: 83.05000687568048
855
- - type: manhattan_pearson
856
- value: 82.6543207325763
857
- - type: manhattan_spearman
858
- value: 83.06852715971705
859
- - task:
860
- type: Reranking
861
- dataset:
862
- type: C-MTEB/T2Reranking
863
- name: MTEB T2Reranking
864
- config: default
865
- split: dev
866
- revision: None
867
- metrics:
868
- - type: map
869
- value: 66.48634798223672
870
- - type: mrr
871
- value: 76.30158461488861
872
- - task:
873
- type: Retrieval
874
- dataset:
875
- type: C-MTEB/T2Retrieval
876
- name: MTEB T2Retrieval
877
- config: default
878
- split: dev
879
- revision: None
880
- metrics:
881
- - type: map_at_1
882
- value: 27.483999999999998
883
- - type: map_at_10
884
- value: 76.848
885
- - type: map_at_100
886
- value: 80.541
887
- - type: map_at_1000
888
- value: 80.607
889
- - type: map_at_3
890
- value: 54.111
891
- - type: map_at_5
892
- value: 66.46300000000001
893
- - type: mrr_at_1
894
- value: 90.045
895
- - type: mrr_at_10
896
- value: 92.552
897
- - type: mrr_at_100
898
- value: 92.642
899
- - type: mrr_at_1000
900
- value: 92.645
901
- - type: mrr_at_3
902
- value: 92.134
903
- - type: mrr_at_5
904
- value: 92.391
905
- - type: ndcg_at_1
906
- value: 90.045
907
- - type: ndcg_at_10
908
- value: 84.504
909
- - type: ndcg_at_100
910
- value: 88.23100000000001
911
- - type: ndcg_at_1000
912
- value: 88.85300000000001
913
- - type: ndcg_at_3
914
- value: 85.992
915
- - type: ndcg_at_5
916
- value: 84.548
917
- - type: precision_at_1
918
- value: 90.045
919
- - type: precision_at_10
920
- value: 41.91
921
- - type: precision_at_100
922
- value: 5.017
923
- - type: precision_at_1000
924
- value: 0.516
925
- - type: precision_at_3
926
- value: 75.15899999999999
927
- - type: precision_at_5
928
- value: 62.958000000000006
929
- - type: recall_at_1
930
- value: 27.483999999999998
931
- - type: recall_at_10
932
- value: 83.408
933
- - type: recall_at_100
934
- value: 95.514
935
- - type: recall_at_1000
936
- value: 98.65
937
- - type: recall_at_3
938
- value: 55.822
939
- - type: recall_at_5
940
- value: 69.868
941
- - task:
942
- type: Classification
943
- dataset:
944
- type: C-MTEB/TNews-classification
945
- name: MTEB TNews
946
- config: default
947
- split: validation
948
- revision: None
949
- metrics:
950
- - type: accuracy
951
- value: 53.196
952
- - type: f1
953
- value: 51.51679244513836
954
- - task:
955
- type: Clustering
956
- dataset:
957
- type: C-MTEB/ThuNewsClusteringP2P
958
- name: MTEB ThuNewsClusteringP2P
959
- config: default
960
- split: test
961
- revision: None
962
- metrics:
963
- - type: v_measure
964
- value: 67.87592101539063
965
- - task:
966
- type: Clustering
967
- dataset:
968
- type: C-MTEB/ThuNewsClusteringS2S
969
- name: MTEB ThuNewsClusteringS2S
970
- config: default
971
- split: test
972
- revision: None
973
- metrics:
974
- - type: v_measure
975
- value: 62.4675464095125
976
- - task:
977
- type: Retrieval
978
- dataset:
979
- type: C-MTEB/VideoRetrieval
980
- name: MTEB VideoRetrieval
981
- config: default
982
- split: dev
983
- revision: None
984
- metrics:
985
- - type: map_at_1
986
- value: 57.9
987
- - type: map_at_10
988
- value: 68.099
989
- - type: map_at_100
990
- value: 68.55499999999999
991
- - type: map_at_1000
992
- value: 68.566
993
- - type: map_at_3
994
- value: 66.4
995
- - type: map_at_5
996
- value: 67.46
997
- - type: mrr_at_1
998
- value: 57.9
999
- - type: mrr_at_10
1000
- value: 68.099
1001
- - type: mrr_at_100
1002
- value: 68.55499999999999
1003
- - type: mrr_at_1000
1004
- value: 68.566
1005
- - type: mrr_at_3
1006
- value: 66.4
1007
- - type: mrr_at_5
1008
- value: 67.46
1009
- - type: ndcg_at_1
1010
- value: 57.9
1011
- - type: ndcg_at_10
1012
- value: 72.555
1013
- - type: ndcg_at_100
1014
- value: 74.715
1015
- - type: ndcg_at_1000
1016
- value: 75.034
1017
- - type: ndcg_at_3
1018
- value: 69.102
1019
- - type: ndcg_at_5
1020
- value: 71.004
1021
- - type: precision_at_1
1022
- value: 57.9
1023
- - type: precision_at_10
1024
- value: 8.63
1025
- - type: precision_at_100
1026
- value: 0.963
1027
- - type: precision_at_1000
1028
- value: 0.099
1029
- - type: precision_at_3
1030
- value: 25.633
1031
- - type: precision_at_5
1032
- value: 16.3
1033
- - type: recall_at_1
1034
- value: 57.9
1035
- - type: recall_at_10
1036
- value: 86.3
1037
- - type: recall_at_100
1038
- value: 96.3
1039
- - type: recall_at_1000
1040
- value: 98.9
1041
- - type: recall_at_3
1042
- value: 76.9
1043
- - type: recall_at_5
1044
- value: 81.5
1045
- - task:
1046
- type: Classification
1047
- dataset:
1048
- type: C-MTEB/waimai-classification
1049
- name: MTEB Waimai
1050
- config: default
1051
- split: test
1052
- revision: None
1053
- metrics:
1054
- - type: accuracy
1055
- value: 87.27000000000001
1056
- - type: ap
1057
- value: 71.10883470119464
1058
- - type: f1
1059
- value: 85.76618863591946
 
1060
  ---
1061
 
1062
  **新闻 | News**
@@ -1224,4 +1225,4 @@ Q: 长度为什么只有512，能否更长？\
1224
  A: 可以但没必要，长了效果普遍不好，这是当前训练方法和数据导致的，几乎无解，建议长文本还是走分块。
1225
 
1226
  Q: 训练资源和算力？\
1227
- A: 亿级别的数据，单卡A100要一个月起步
 
1
  ---
2
  pipeline_tag: sentence-similarity
3
  tags:
4
+ - sentence-transformers
5
+ - feature-extraction
6
+ - sentence-similarity
7
+ - mteb
8
  model-index:
9
+ - name: stella-base-zh-v3-1792d
10
+ results:
11
+ - task:
12
+ type: STS
13
+ dataset:
14
+ type: C-MTEB/AFQMC
15
+ name: MTEB AFQMC
16
+ config: default
17
+ split: validation
18
+ revision: None
19
+ metrics:
20
+ - type: cos_sim_pearson
21
+ value: 54.5145388936202
22
+ - type: cos_sim_spearman
23
+ value: 59.223125058197134
24
+ - type: euclidean_pearson
25
+ value: 57.819377838734695
26
+ - type: euclidean_spearman
27
+ value: 59.22310494948463
28
+ - type: manhattan_pearson
29
+ value: 57.44029759610327
30
+ - type: manhattan_spearman
31
+ value: 58.88336250854381
32
+ - task:
33
+ type: STS
34
+ dataset:
35
+ type: C-MTEB/ATEC
36
+ name: MTEB ATEC
37
+ config: default
38
+ split: test
39
+ revision: None
40
+ metrics:
41
+ - type: cos_sim_pearson
42
+ value: 54.544243591344866
43
+ - type: cos_sim_spearman
44
+ value: 58.43052988038229
45
+ - type: euclidean_pearson
46
+ value: 62.1608405146189
47
+ - type: euclidean_spearman
48
+ value: 58.43052762862396
49
+ - type: manhattan_pearson
50
+ value: 61.88443779892169
51
+ - type: manhattan_spearman
52
+ value: 58.26899143609596
53
+ - task:
54
+ type: Classification
55
+ dataset:
56
+ type: mteb/amazon_reviews_multi
57
+ name: MTEB AmazonReviewsClassification (zh)
58
+ config: zh
59
+ split: test
60
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
61
+ metrics:
62
+ - type: accuracy
63
+ value: 46.343999999999994
64
+ - type: f1
65
+ value: 44.46931958420461
66
+ - task:
67
+ type: STS
68
+ dataset:
69
+ type: C-MTEB/BQ
70
+ name: MTEB BQ
71
+ config: default
72
+ split: test
73
+ revision: None
74
+ metrics:
75
+ - type: cos_sim_pearson
76
+ value: 68.52081000538426
77
+ - type: cos_sim_spearman
78
+ value: 70.44089935351529
79
+ - type: euclidean_pearson
80
+ value: 69.24671010626395
81
+ - type: euclidean_spearman
82
+ value: 70.44090281761693
83
+ - type: manhattan_pearson
84
+ value: 69.00737718109357
85
+ - type: manhattan_spearman
86
+ value: 70.24344902456502
87
+ - task:
88
+ type: Clustering
89
+ dataset:
90
+ type: C-MTEB/CLSClusteringP2P
91
+ name: MTEB CLSClusteringP2P
92
+ config: default
93
+ split: test
94
+ revision: None
95
+ metrics:
96
+ - type: v_measure
97
+ value: 42.86119436460332
98
+ - task:
99
+ type: Clustering
100
+ dataset:
101
+ type: C-MTEB/CLSClusteringS2S
102
+ name: MTEB CLSClusteringS2S
103
+ config: default
104
+ split: test
105
+ revision: None
106
+ metrics:
107
+ - type: v_measure
108
+ value: 39.97521728440642
109
+ - task:
110
+ type: Reranking
111
+ dataset:
112
+ type: C-MTEB/CMedQAv1-reranking
113
+ name: MTEB CMedQAv1
114
+ config: default
115
+ split: test
116
+ revision: None
117
+ metrics:
118
+ - type: map
119
+ value: 88.34151862240452
120
+ - type: mrr
121
+ value: 90.40380952380953
122
+ - task:
123
+ type: Reranking
124
+ dataset:
125
+ type: C-MTEB/CMedQAv2-reranking
126
+ name: MTEB CMedQAv2
127
+ config: default
128
+ split: test
129
+ revision: None
130
+ metrics:
131
+ - type: map
132
+ value: 89.06288758814637
133
+ - type: mrr
134
+ value: 90.91285714285713
135
+ - task:
136
+ type: Retrieval
137
+ dataset:
138
+ type: C-MTEB/CmedqaRetrieval
139
+ name: MTEB CmedqaRetrieval
140
+ config: default
141
+ split: dev
142
+ revision: None
143
+ metrics:
144
+ - type: map_at_1
145
+ value: 25.651000000000003
146
+ - type: map_at_10
147
+ value: 38.576
148
+ - type: map_at_100
149
+ value: 40.534
150
+ - type: map_at_1000
151
+ value: 40.64
152
+ - type: map_at_3
153
+ value: 34.016000000000005
154
+ - type: map_at_5
155
+ value: 36.675999999999995
156
+ - type: mrr_at_1
157
+ value: 39.06
158
+ - type: mrr_at_10
159
+ value: 47.278
160
+ - type: mrr_at_100
161
+ value: 48.272999999999996
162
+ - type: mrr_at_1000
163
+ value: 48.314
164
+ - type: mrr_at_3
165
+ value: 44.461
166
+ - type: mrr_at_5
167
+ value: 46.107
168
+ - type: ndcg_at_1
169
+ value: 39.06
170
+ - type: ndcg_at_10
171
+ value: 45.384
172
+ - type: ndcg_at_100
173
+ value: 52.796
174
+ - type: ndcg_at_1000
175
+ value: 54.55
176
+ - type: ndcg_at_3
177
+ value: 39.497
178
+ - type: ndcg_at_5
179
+ value: 42.189
180
+ - type: precision_at_1
181
+ value: 39.06
182
+ - type: precision_at_10
183
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207
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212
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213
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+ - type: manhattan_pearson
814
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815
+ - type: manhattan_spearman
816
+ value: 39.757452890246775
817
+ - task:
818
+ type: STS
819
+ dataset:
820
+ type: mteb/sts22-crosslingual-sts
821
+ name: MTEB STS22 (zh)
822
+ config: zh
823
+ split: test
824
+ revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
825
+ metrics:
826
+ - type: cos_sim_pearson
827
+ value: 65.9133992390713
828
+ - type: cos_sim_spearman
829
+ value: 66.4894937647578
830
+ - type: euclidean_pearson
831
+ value: 66.19047142189935
832
+ - type: euclidean_spearman
833
+ value: 66.4894937647578
834
+ - type: manhattan_pearson
835
+ value: 66.6960935896136
836
+ - type: manhattan_spearman
837
+ value: 66.88179996508133
838
+ - task:
839
+ type: STS
840
+ dataset:
841
+ type: C-MTEB/STSB
842
+ name: MTEB STSB
843
+ config: default
844
+ split: test
845
+ revision: None
846
+ metrics:
847
+ - type: cos_sim_pearson
848
+ value: 80.55099417946924
849
+ - type: cos_sim_spearman
850
+ value: 83.05000687568048
851
+ - type: euclidean_pearson
852
+ value: 82.62744668792926
853
+ - type: euclidean_spearman
854
+ value: 83.05000687568048
855
+ - type: manhattan_pearson
856
+ value: 82.6543207325763
857
+ - type: manhattan_spearman
858
+ value: 83.06852715971705
859
+ - task:
860
+ type: Reranking
861
+ dataset:
862
+ type: C-MTEB/T2Reranking
863
+ name: MTEB T2Reranking
864
+ config: default
865
+ split: dev
866
+ revision: None
867
+ metrics:
868
+ - type: map
869
+ value: 66.48634798223672
870
+ - type: mrr
871
+ value: 76.30158461488861
872
+ - task:
873
+ type: Retrieval
874
+ dataset:
875
+ type: C-MTEB/T2Retrieval
876
+ name: MTEB T2Retrieval
877
+ config: default
878
+ split: dev
879
+ revision: None
880
+ metrics:
881
+ - type: map_at_1
882
+ value: 27.483999999999998
883
+ - type: map_at_10
884
+ value: 76.848
885
+ - type: map_at_100
886
+ value: 80.541
887
+ - type: map_at_1000
888
+ value: 80.607
889
+ - type: map_at_3
890
+ value: 54.111
891
+ - type: map_at_5
892
+ value: 66.46300000000001
893
+ - type: mrr_at_1
894
+ value: 90.045
895
+ - type: mrr_at_10
896
+ value: 92.552
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+ - type: mrr_at_100
898
+ value: 92.642
899
+ - type: mrr_at_1000
900
+ value: 92.645
901
+ - type: mrr_at_3
902
+ value: 92.134
903
+ - type: mrr_at_5
904
+ value: 92.391
905
+ - type: ndcg_at_1
906
+ value: 90.045
907
+ - type: ndcg_at_10
908
+ value: 84.504
909
+ - type: ndcg_at_100
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+ value: 88.23100000000001
911
+ - type: ndcg_at_1000
912
+ value: 88.85300000000001
913
+ - type: ndcg_at_3
914
+ value: 85.992
915
+ - type: ndcg_at_5
916
+ value: 84.548
917
+ - type: precision_at_1
918
+ value: 90.045
919
+ - type: precision_at_10
920
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921
+ - type: precision_at_100
922
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923
+ - type: precision_at_1000
924
+ value: 0.516
925
+ - type: precision_at_3
926
+ value: 75.15899999999999
927
+ - type: precision_at_5
928
+ value: 62.958000000000006
929
+ - type: recall_at_1
930
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931
+ - type: recall_at_10
932
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933
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934
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935
+ - type: recall_at_1000
936
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937
+ - type: recall_at_3
938
+ value: 55.822
939
+ - type: recall_at_5
940
+ value: 69.868
941
+ - task:
942
+ type: Classification
943
+ dataset:
944
+ type: C-MTEB/TNews-classification
945
+ name: MTEB TNews
946
+ config: default
947
+ split: validation
948
+ revision: None
949
+ metrics:
950
+ - type: accuracy
951
+ value: 53.196
952
+ - type: f1
953
+ value: 51.51679244513836
954
+ - task:
955
+ type: Clustering
956
+ dataset:
957
+ type: C-MTEB/ThuNewsClusteringP2P
958
+ name: MTEB ThuNewsClusteringP2P
959
+ config: default
960
+ split: test
961
+ revision: None
962
+ metrics:
963
+ - type: v_measure
964
+ value: 67.87592101539063
965
+ - task:
966
+ type: Clustering
967
+ dataset:
968
+ type: C-MTEB/ThuNewsClusteringS2S
969
+ name: MTEB ThuNewsClusteringS2S
970
+ config: default
971
+ split: test
972
+ revision: None
973
+ metrics:
974
+ - type: v_measure
975
+ value: 62.4675464095125
976
+ - task:
977
+ type: Retrieval
978
+ dataset:
979
+ type: C-MTEB/VideoRetrieval
980
+ name: MTEB VideoRetrieval
981
+ config: default
982
+ split: dev
983
+ revision: None
984
+ metrics:
985
+ - type: map_at_1
986
+ value: 57.9
987
+ - type: map_at_10
988
+ value: 68.099
989
+ - type: map_at_100
990
+ value: 68.55499999999999
991
+ - type: map_at_1000
992
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993
+ - type: map_at_3
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+ - type: map_at_5
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+ value: 67.46
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+ - type: mrr_at_1
998
+ value: 57.9
999
+ - type: mrr_at_10
1000
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1001
+ - type: mrr_at_100
1002
+ value: 68.55499999999999
1003
+ - type: mrr_at_1000
1004
+ value: 68.566
1005
+ - type: mrr_at_3
1006
+ value: 66.4
1007
+ - type: mrr_at_5
1008
+ value: 67.46
1009
+ - type: ndcg_at_1
1010
+ value: 57.9
1011
+ - type: ndcg_at_10
1012
+ value: 72.555
1013
+ - type: ndcg_at_100
1014
+ value: 74.715
1015
+ - type: ndcg_at_1000
1016
+ value: 75.034
1017
+ - type: ndcg_at_3
1018
+ value: 69.102
1019
+ - type: ndcg_at_5
1020
+ value: 71.004
1021
+ - type: precision_at_1
1022
+ value: 57.9
1023
+ - type: precision_at_10
1024
+ value: 8.63
1025
+ - type: precision_at_100
1026
+ value: 0.963
1027
+ - type: precision_at_1000
1028
+ value: 0.099
1029
+ - type: precision_at_3
1030
+ value: 25.633
1031
+ - type: precision_at_5
1032
+ value: 16.3
1033
+ - type: recall_at_1
1034
+ value: 57.9
1035
+ - type: recall_at_10
1036
+ value: 86.3
1037
+ - type: recall_at_100
1038
+ value: 96.3
1039
+ - type: recall_at_1000
1040
+ value: 98.9
1041
+ - type: recall_at_3
1042
+ value: 76.9
1043
+ - type: recall_at_5
1044
+ value: 81.5
1045
+ - task:
1046
+ type: Classification
1047
+ dataset:
1048
+ type: C-MTEB/waimai-classification
1049
+ name: MTEB Waimai
1050
+ config: default
1051
+ split: test
1052
+ revision: None
1053
+ metrics:
1054
+ - type: accuracy
1055
+ value: 87.27000000000001
1056
+ - type: ap
1057
+ value: 71.10883470119464
1058
+ - type: f1
1059
+ value: 85.76618863591946
1060
+ license: mit
1061
  ---
1062
 
1063
  **新闻 | News**
 
1225
  A: 可以但没必要，长了效果普遍不好，这是当前训练方法和数据导致的，几乎无解，建议长文本还是走分块。
1226
 
1227
  Q: 训练资源和算力？\
1228
+ A: 亿级别的数据，单卡A100要一个月起步