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DMetaSoul
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sbert-chinese-general-v1

Sentence Similarity
sentence-transformers
PyTorch
Transformers
Chinese
bert
feature-extraction
semantic-search
chinese
mteb
Eval Results
text-embeddings-inference
Inference Endpoints
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@@ -7,6 +7,1097 @@ tags:
7
  - transformers
8
  - semantic-search
9
  - chinese
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
10
  ---
11
 
12
  # DMetaSoul/sbert-chinese-general-v1
 
7
  - transformers
8
  - semantic-search
9
  - chinese
10
+ - mteb
11
+ model-index:
12
+ - name: sbert-chinese-general-v1
13
+ results:
14
+ - task:
15
+ type: STS
16
+ dataset:
17
+ type: C-MTEB/AFQMC
18
+ name: MTEB AFQMC
19
+ config: default
20
+ split: validation
21
+ revision: None
22
+ metrics:
23
+ - type: cos_sim_pearson
24
+ value: 22.293919432958074
25
+ - type: cos_sim_spearman
26
+ value: 22.56718923553609
27
+ - type: euclidean_pearson
28
+ value: 22.525656322797026
29
+ - type: euclidean_spearman
30
+ value: 22.56718923553609
31
+ - type: manhattan_pearson
32
+ value: 22.501773028824065
33
+ - type: manhattan_spearman
34
+ value: 22.536992587828397
35
+ - task:
36
+ type: STS
37
+ dataset:
38
+ type: C-MTEB/ATEC
39
+ name: MTEB ATEC
40
+ config: default
41
+ split: test
42
+ revision: None
43
+ metrics:
44
+ - type: cos_sim_pearson
45
+ value: 30.33575274463879
46
+ - type: cos_sim_spearman
47
+ value: 30.298708742167772
48
+ - type: euclidean_pearson
49
+ value: 32.33094743729218
50
+ - type: euclidean_spearman
51
+ value: 30.298710993858734
52
+ - type: manhattan_pearson
53
+ value: 32.31155376195945
54
+ - type: manhattan_spearman
55
+ value: 30.267669681690744
56
+ - task:
57
+ type: Classification
58
+ dataset:
59
+ type: mteb/amazon_reviews_multi
60
+ name: MTEB AmazonReviewsClassification (zh)
61
+ config: zh
62
+ split: test
63
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
64
+ metrics:
65
+ - type: accuracy
66
+ value: 37.507999999999996
67
+ - type: f1
68
+ value: 36.436808400753286
69
+ - task:
70
+ type: STS
71
+ dataset:
72
+ type: C-MTEB/BQ
73
+ name: MTEB BQ
74
+ config: default
75
+ split: test
76
+ revision: None
77
+ metrics:
78
+ - type: cos_sim_pearson
79
+ value: 41.493256724214255
80
+ - type: cos_sim_spearman
81
+ value: 40.98395961967895
82
+ - type: euclidean_pearson
83
+ value: 41.12345737966565
84
+ - type: euclidean_spearman
85
+ value: 40.983959619555996
86
+ - type: manhattan_pearson
87
+ value: 41.02584539471014
88
+ - type: manhattan_spearman
89
+ value: 40.87549513383032
90
+ - task:
91
+ type: BitextMining
92
+ dataset:
93
+ type: mteb/bucc-bitext-mining
94
+ name: MTEB BUCC (zh-en)
95
+ config: zh-en
96
+ split: test
97
+ revision: d51519689f32196a32af33b075a01d0e7c51e252
98
+ metrics:
99
+ - type: accuracy
100
+ value: 9.794628751974724
101
+ - type: f1
102
+ value: 9.350535369492716
103
+ - type: precision
104
+ value: 9.179392662804986
105
+ - type: recall
106
+ value: 9.794628751974724
107
+ - task:
108
+ type: Clustering
109
+ dataset:
110
+ type: C-MTEB/CLSClusteringP2P
111
+ name: MTEB CLSClusteringP2P
112
+ config: default
113
+ split: test
114
+ revision: None
115
+ metrics:
116
+ - type: v_measure
117
+ value: 34.984726547788284
118
+ - task:
119
+ type: Clustering
120
+ dataset:
121
+ type: C-MTEB/CLSClusteringS2S
122
+ name: MTEB CLSClusteringS2S
123
+ config: default
124
+ split: test
125
+ revision: None
126
+ metrics:
127
+ - type: v_measure
128
+ value: 27.81945732281589
129
+ - task:
130
+ type: Reranking
131
+ dataset:
132
+ type: C-MTEB/CMedQAv1-reranking
133
+ name: MTEB CMedQAv1
134
+ config: default
135
+ split: test
136
+ revision: None
137
+ metrics:
138
+ - type: map
139
+ value: 53.06586280826805
140
+ - type: mrr
141
+ value: 59.58781746031746
142
+ - task:
143
+ type: Reranking
144
+ dataset:
145
+ type: C-MTEB/CMedQAv2-reranking
146
+ name: MTEB CMedQAv2
147
+ config: default
148
+ split: test
149
+ revision: None
150
+ metrics:
151
+ - type: map
152
+ value: 52.83635946154306
153
+ - type: mrr
154
+ value: 59.315079365079356
155
+ - task:
156
+ type: Retrieval
157
+ dataset:
158
+ type: C-MTEB/CmedqaRetrieval
159
+ name: MTEB CmedqaRetrieval
160
+ config: default
161
+ split: dev
162
+ revision: None
163
+ metrics:
164
+ - type: map_at_1
165
+ value: 5.721
166
+ - type: map_at_10
167
+ value: 8.645
168
+ - type: map_at_100
169
+ value: 9.434
170
+ - type: map_at_1000
171
+ value: 9.586
172
+ - type: map_at_3
173
+ value: 7.413
174
+ - type: map_at_5
175
+ value: 8.05
176
+ - type: mrr_at_1
177
+ value: 9.626999999999999
178
+ - type: mrr_at_10
179
+ value: 13.094
180
+ - type: mrr_at_100
181
+ value: 13.854
182
+ - type: mrr_at_1000
183
+ value: 13.958
184
+ - type: mrr_at_3
185
+ value: 11.724
186
+ - type: mrr_at_5
187
+ value: 12.409
188
+ - type: ndcg_at_1
189
+ value: 9.626999999999999
190
+ - type: ndcg_at_10
191
+ value: 11.35
192
+ - type: ndcg_at_100
193
+ value: 15.593000000000002
194
+ - type: ndcg_at_1000
195
+ value: 19.619
196
+ - type: ndcg_at_3
197
+ value: 9.317
198
+ - type: ndcg_at_5
199
+ value: 10.049
200
+ - type: precision_at_1
201
+ value: 9.626999999999999
202
+ - type: precision_at_10
203
+ value: 2.796
204
+ - type: precision_at_100
205
+ value: 0.629
206
+ - type: precision_at_1000
207
+ value: 0.11800000000000001
208
+ - type: precision_at_3
209
+ value: 5.476
210
+ - type: precision_at_5
211
+ value: 4.1209999999999996
212
+ - type: recall_at_1
213
+ value: 5.721
214
+ - type: recall_at_10
215
+ value: 15.190000000000001
216
+ - type: recall_at_100
217
+ value: 33.633
218
+ - type: recall_at_1000
219
+ value: 62.019999999999996
220
+ - type: recall_at_3
221
+ value: 9.099
222
+ - type: recall_at_5
223
+ value: 11.423
224
+ - task:
225
+ type: PairClassification
226
+ dataset:
227
+ type: C-MTEB/CMNLI
228
+ name: MTEB Cmnli
229
+ config: default
230
+ split: validation
231
+ revision: None
232
+ metrics:
233
+ - type: cos_sim_accuracy
234
+ value: 77.36620565243535
235
+ - type: cos_sim_ap
236
+ value: 85.92291866877001
237
+ - type: cos_sim_f1
238
+ value: 78.19390231037029
239
+ - type: cos_sim_precision
240
+ value: 71.24183006535948
241
+ - type: cos_sim_recall
242
+ value: 86.64952069207388
243
+ - type: dot_accuracy
244
+ value: 77.36620565243535
245
+ - type: dot_ap
246
+ value: 85.94113738490068
247
+ - type: dot_f1
248
+ value: 78.19390231037029
249
+ - type: dot_precision
250
+ value: 71.24183006535948
251
+ - type: dot_recall
252
+ value: 86.64952069207388
253
+ - type: euclidean_accuracy
254
+ value: 77.36620565243535
255
+ - type: euclidean_ap
256
+ value: 85.92291893444687
257
+ - type: euclidean_f1
258
+ value: 78.19390231037029
259
+ - type: euclidean_precision
260
+ value: 71.24183006535948
261
+ - type: euclidean_recall
262
+ value: 86.64952069207388
263
+ - type: manhattan_accuracy
264
+ value: 77.29404690318701
265
+ - type: manhattan_ap
266
+ value: 85.88284362100919
267
+ - type: manhattan_f1
268
+ value: 78.17836812144213
269
+ - type: manhattan_precision
270
+ value: 71.18448838548666
271
+ - type: manhattan_recall
272
+ value: 86.69628244096329
273
+ - type: max_accuracy
274
+ value: 77.36620565243535
275
+ - type: max_ap
276
+ value: 85.94113738490068
277
+ - type: max_f1
278
+ value: 78.19390231037029
279
+ - task:
280
+ type: Retrieval
281
+ dataset:
282
+ type: C-MTEB/CovidRetrieval
283
+ name: MTEB CovidRetrieval
284
+ config: default
285
+ split: dev
286
+ revision: None
287
+ metrics:
288
+ - type: map_at_1
289
+ value: 26.976
290
+ - type: map_at_10
291
+ value: 35.18
292
+ - type: map_at_100
293
+ value: 35.921
294
+ - type: map_at_1000
295
+ value: 35.998999999999995
296
+ - type: map_at_3
297
+ value: 32.763
298
+ - type: map_at_5
299
+ value: 34.165
300
+ - type: mrr_at_1
301
+ value: 26.976
302
+ - type: mrr_at_10
303
+ value: 35.234
304
+ - type: mrr_at_100
305
+ value: 35.939
306
+ - type: mrr_at_1000
307
+ value: 36.016
308
+ - type: mrr_at_3
309
+ value: 32.771
310
+ - type: mrr_at_5
311
+ value: 34.172999999999995
312
+ - type: ndcg_at_1
313
+ value: 26.976
314
+ - type: ndcg_at_10
315
+ value: 39.635
316
+ - type: ndcg_at_100
317
+ value: 43.54
318
+ - type: ndcg_at_1000
319
+ value: 45.723
320
+ - type: ndcg_at_3
321
+ value: 34.652
322
+ - type: ndcg_at_5
323
+ value: 37.186
324
+ - type: precision_at_1
325
+ value: 26.976
326
+ - type: precision_at_10
327
+ value: 5.406
328
+ - type: precision_at_100
329
+ value: 0.736
330
+ - type: precision_at_1000
331
+ value: 0.091
332
+ - type: precision_at_3
333
+ value: 13.418
334
+ - type: precision_at_5
335
+ value: 9.293999999999999
336
+ - type: recall_at_1
337
+ value: 26.976
338
+ - type: recall_at_10
339
+ value: 53.766999999999996
340
+ - type: recall_at_100
341
+ value: 72.761
342
+ - type: recall_at_1000
343
+ value: 90.148
344
+ - type: recall_at_3
345
+ value: 40.095
346
+ - type: recall_at_5
347
+ value: 46.233000000000004
348
+ - task:
349
+ type: Retrieval
350
+ dataset:
351
+ type: C-MTEB/DuRetrieval
352
+ name: MTEB DuRetrieval
353
+ config: default
354
+ split: dev
355
+ revision: None
356
+ metrics:
357
+ - type: map_at_1
358
+ value: 11.285
359
+ - type: map_at_10
360
+ value: 30.259000000000004
361
+ - type: map_at_100
362
+ value: 33.772000000000006
363
+ - type: map_at_1000
364
+ value: 34.037
365
+ - type: map_at_3
366
+ value: 21.038999999999998
367
+ - type: map_at_5
368
+ value: 25.939
369
+ - type: mrr_at_1
370
+ value: 45.1
371
+ - type: mrr_at_10
372
+ value: 55.803999999999995
373
+ - type: mrr_at_100
374
+ value: 56.301
375
+ - type: mrr_at_1000
376
+ value: 56.330999999999996
377
+ - type: mrr_at_3
378
+ value: 53.333
379
+ - type: mrr_at_5
380
+ value: 54.798
381
+ - type: ndcg_at_1
382
+ value: 45.1
383
+ - type: ndcg_at_10
384
+ value: 41.156
385
+ - type: ndcg_at_100
386
+ value: 49.518
387
+ - type: ndcg_at_1000
388
+ value: 52.947
389
+ - type: ndcg_at_3
390
+ value: 39.708
391
+ - type: ndcg_at_5
392
+ value: 38.704
393
+ - type: precision_at_1
394
+ value: 45.1
395
+ - type: precision_at_10
396
+ value: 20.75
397
+ - type: precision_at_100
398
+ value: 3.424
399
+ - type: precision_at_1000
400
+ value: 0.42700000000000005
401
+ - type: precision_at_3
402
+ value: 35.632999999999996
403
+ - type: precision_at_5
404
+ value: 30.080000000000002
405
+ - type: recall_at_1
406
+ value: 11.285
407
+ - type: recall_at_10
408
+ value: 43.242000000000004
409
+ - type: recall_at_100
410
+ value: 68.604
411
+ - type: recall_at_1000
412
+ value: 85.904
413
+ - type: recall_at_3
414
+ value: 24.404
415
+ - type: recall_at_5
416
+ value: 32.757
417
+ - task:
418
+ type: Retrieval
419
+ dataset:
420
+ type: C-MTEB/EcomRetrieval
421
+ name: MTEB EcomRetrieval
422
+ config: default
423
+ split: dev
424
+ revision: None
425
+ metrics:
426
+ - type: map_at_1
427
+ value: 21.0
428
+ - type: map_at_10
429
+ value: 28.364
430
+ - type: map_at_100
431
+ value: 29.199
432
+ - type: map_at_1000
433
+ value: 29.265
434
+ - type: map_at_3
435
+ value: 25.717000000000002
436
+ - type: map_at_5
437
+ value: 27.311999999999998
438
+ - type: mrr_at_1
439
+ value: 21.0
440
+ - type: mrr_at_10
441
+ value: 28.364
442
+ - type: mrr_at_100
443
+ value: 29.199
444
+ - type: mrr_at_1000
445
+ value: 29.265
446
+ - type: mrr_at_3
447
+ value: 25.717000000000002
448
+ - type: mrr_at_5
449
+ value: 27.311999999999998
450
+ - type: ndcg_at_1
451
+ value: 21.0
452
+ - type: ndcg_at_10
453
+ value: 32.708
454
+ - type: ndcg_at_100
455
+ value: 37.184
456
+ - type: ndcg_at_1000
457
+ value: 39.273
458
+ - type: ndcg_at_3
459
+ value: 27.372000000000003
460
+ - type: ndcg_at_5
461
+ value: 30.23
462
+ - type: precision_at_1
463
+ value: 21.0
464
+ - type: precision_at_10
465
+ value: 4.66
466
+ - type: precision_at_100
467
+ value: 0.685
468
+ - type: precision_at_1000
469
+ value: 0.086
470
+ - type: precision_at_3
471
+ value: 10.732999999999999
472
+ - type: precision_at_5
473
+ value: 7.82
474
+ - type: recall_at_1
475
+ value: 21.0
476
+ - type: recall_at_10
477
+ value: 46.6
478
+ - type: recall_at_100
479
+ value: 68.5
480
+ - type: recall_at_1000
481
+ value: 85.6
482
+ - type: recall_at_3
483
+ value: 32.2
484
+ - type: recall_at_5
485
+ value: 39.1
486
+ - task:
487
+ type: Classification
488
+ dataset:
489
+ type: C-MTEB/IFlyTek-classification
490
+ name: MTEB IFlyTek
491
+ config: default
492
+ split: validation
493
+ revision: None
494
+ metrics:
495
+ - type: accuracy
496
+ value: 44.878799538283964
497
+ - type: f1
498
+ value: 33.84678310261366
499
+ - task:
500
+ type: Classification
501
+ dataset:
502
+ type: C-MTEB/JDReview-classification
503
+ name: MTEB JDReview
504
+ config: default
505
+ split: test
506
+ revision: None
507
+ metrics:
508
+ - type: accuracy
509
+ value: 82.1951219512195
510
+ - type: ap
511
+ value: 46.78292030042397
512
+ - type: f1
513
+ value: 76.20482468514128
514
+ - task:
515
+ type: STS
516
+ dataset:
517
+ type: C-MTEB/LCQMC
518
+ name: MTEB LCQMC
519
+ config: default
520
+ split: test
521
+ revision: None
522
+ metrics:
523
+ - type: cos_sim_pearson
524
+ value: 62.84331627244547
525
+ - type: cos_sim_spearman
526
+ value: 68.39990265073726
527
+ - type: euclidean_pearson
528
+ value: 66.87431827169324
529
+ - type: euclidean_spearman
530
+ value: 68.39990264979167
531
+ - type: manhattan_pearson
532
+ value: 66.89702078900328
533
+ - type: manhattan_spearman
534
+ value: 68.42107302159141
535
+ - task:
536
+ type: Reranking
537
+ dataset:
538
+ type: C-MTEB/Mmarco-reranking
539
+ name: MTEB MMarcoReranking
540
+ config: default
541
+ split: dev
542
+ revision: None
543
+ metrics:
544
+ - type: map
545
+ value: 9.28600891904827
546
+ - type: mrr
547
+ value: 8.057936507936509
548
+ - task:
549
+ type: Retrieval
550
+ dataset:
551
+ type: C-MTEB/MMarcoRetrieval
552
+ name: MTEB MMarcoRetrieval
553
+ config: default
554
+ split: dev
555
+ revision: None
556
+ metrics:
557
+ - type: map_at_1
558
+ value: 22.820999999999998
559
+ - type: map_at_10
560
+ value: 30.44
561
+ - type: map_at_100
562
+ value: 31.35
563
+ - type: map_at_1000
564
+ value: 31.419000000000004
565
+ - type: map_at_3
566
+ value: 28.134999999999998
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+ - type: map_at_5
568
+ value: 29.482000000000003
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+ - type: mrr_at_1
570
+ value: 23.782
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+ - type: mrr_at_10
572
+ value: 31.141999999999996
573
+ - type: mrr_at_100
574
+ value: 32.004
575
+ - type: mrr_at_1000
576
+ value: 32.068000000000005
577
+ - type: mrr_at_3
578
+ value: 28.904000000000003
579
+ - type: mrr_at_5
580
+ value: 30.214999999999996
581
+ - type: ndcg_at_1
582
+ value: 23.782
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+ - type: ndcg_at_10
584
+ value: 34.625
585
+ - type: ndcg_at_100
586
+ value: 39.226
587
+ - type: ndcg_at_1000
588
+ value: 41.128
589
+ - type: ndcg_at_3
590
+ value: 29.968
591
+ - type: ndcg_at_5
592
+ value: 32.35
593
+ - type: precision_at_1
594
+ value: 23.782
595
+ - type: precision_at_10
596
+ value: 4.994
597
+ - type: precision_at_100
598
+ value: 0.736
599
+ - type: precision_at_1000
600
+ value: 0.09
601
+ - type: precision_at_3
602
+ value: 12.13
603
+ - type: precision_at_5
604
+ value: 8.495999999999999
605
+ - type: recall_at_1
606
+ value: 22.820999999999998
607
+ - type: recall_at_10
608
+ value: 47.141
609
+ - type: recall_at_100
610
+ value: 68.952
611
+ - type: recall_at_1000
612
+ value: 83.985
613
+ - type: recall_at_3
614
+ value: 34.508
615
+ - type: recall_at_5
616
+ value: 40.232
617
+ - task:
618
+ type: Classification
619
+ dataset:
620
+ type: mteb/amazon_massive_intent
621
+ name: MTEB MassiveIntentClassification (zh-CN)
622
+ config: zh-CN
623
+ split: test
624
+ revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
625
+ metrics:
626
+ - type: accuracy
627
+ value: 57.343644922663074
628
+ - type: f1
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+ value: 56.744802953803486
630
+ - task:
631
+ type: Classification
632
+ dataset:
633
+ type: mteb/amazon_massive_scenario
634
+ name: MTEB MassiveScenarioClassification (zh-CN)
635
+ config: zh-CN
636
+ split: test
637
+ revision: 7d571f92784cd94a019292a1f45445077d0ef634
638
+ metrics:
639
+ - type: accuracy
640
+ value: 62.363819771351714
641
+ - type: f1
642
+ value: 62.15920863434656
643
+ - task:
644
+ type: Retrieval
645
+ dataset:
646
+ type: C-MTEB/MedicalRetrieval
647
+ name: MTEB MedicalRetrieval
648
+ config: default
649
+ split: dev
650
+ revision: None
651
+ metrics:
652
+ - type: map_at_1
653
+ value: 14.6
654
+ - type: map_at_10
655
+ value: 18.231
656
+ - type: map_at_100
657
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658
+ - type: map_at_1000
659
+ value: 18.811
660
+ - type: map_at_3
661
+ value: 17.133000000000003
662
+ - type: map_at_5
663
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664
+ - type: mrr_at_1
665
+ value: 14.6
666
+ - type: mrr_at_10
667
+ value: 18.231
668
+ - type: mrr_at_100
669
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670
+ - type: mrr_at_1000
671
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672
+ - type: mrr_at_3
673
+ value: 17.133000000000003
674
+ - type: mrr_at_5
675
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676
+ - type: ndcg_at_1
677
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+ - type: ndcg_at_10
679
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680
+ - type: ndcg_at_100
681
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682
+ - type: ndcg_at_1000
683
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684
+ - type: ndcg_at_3
685
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686
+ - type: ndcg_at_5
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688
+ - type: precision_at_1
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+ - type: precision_at_10
691
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692
+ - type: precision_at_100
693
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694
+ - type: precision_at_1000
695
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696
+ - type: precision_at_3
697
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698
+ - type: precision_at_5
699
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700
+ - type: recall_at_1
701
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702
+ - type: recall_at_10
703
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704
+ - type: recall_at_100
705
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+ - type: recall_at_1000
707
+ value: 60.0
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+ - type: recall_at_3
709
+ value: 20.5
710
+ - type: recall_at_5
711
+ value: 22.8
712
+ - task:
713
+ type: Classification
714
+ dataset:
715
+ type: C-MTEB/MultilingualSentiment-classification
716
+ name: MTEB MultilingualSentiment
717
+ config: default
718
+ split: validation
719
+ revision: None
720
+ metrics:
721
+ - type: accuracy
722
+ value: 66.58333333333333
723
+ - type: f1
724
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725
+ - task:
726
+ type: PairClassification
727
+ dataset:
728
+ type: C-MTEB/OCNLI
729
+ name: MTEB Ocnli
730
+ config: default
731
+ split: validation
732
+ revision: None
733
+ metrics:
734
+ - type: cos_sim_accuracy
735
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736
+ - type: cos_sim_ap
737
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738
+ - type: cos_sim_f1
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+ - type: cos_sim_precision
741
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+ - type: cos_sim_recall
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+ - type: dot_accuracy
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+ - type: dot_ap
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751
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752
+ - type: dot_recall
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+ - type: euclidean_accuracy
755
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756
+ - type: euclidean_ap
757
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758
+ - type: euclidean_f1
759
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760
+ - type: euclidean_precision
761
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762
+ - type: euclidean_recall
763
+ value: 85.74445617740233
764
+ - type: manhattan_accuracy
765
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766
+ - type: manhattan_ap
767
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768
+ - type: manhattan_f1
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770
+ - type: manhattan_precision
771
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772
+ - type: manhattan_recall
773
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774
+ - type: max_accuracy
775
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776
+ - type: max_ap
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778
+ - type: max_f1
779
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780
+ - task:
781
+ type: Classification
782
+ dataset:
783
+ type: C-MTEB/OnlineShopping-classification
784
+ name: MTEB OnlineShopping
785
+ config: default
786
+ split: test
787
+ revision: None
788
+ metrics:
789
+ - type: accuracy
790
+ value: 88.19000000000001
791
+ - type: ap
792
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+ - type: f1
794
+ value: 88.17344156114062
795
+ - task:
796
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797
+ dataset:
798
+ type: C-MTEB/PAWSX
799
+ name: MTEB PAWSX
800
+ config: default
801
+ split: test
802
+ revision: None
803
+ metrics:
804
+ - type: cos_sim_pearson
805
+ value: 13.70522140998517
806
+ - type: cos_sim_spearman
807
+ value: 15.07546667334743
808
+ - type: euclidean_pearson
809
+ value: 17.49511420225285
810
+ - type: euclidean_spearman
811
+ value: 15.093970931789618
812
+ - type: manhattan_pearson
813
+ value: 17.44069961390521
814
+ - type: manhattan_spearman
815
+ value: 15.076029291596962
816
+ - task:
817
+ type: STS
818
+ dataset:
819
+ type: C-MTEB/QBQTC
820
+ name: MTEB QBQTC
821
+ config: default
822
+ split: test
823
+ revision: None
824
+ metrics:
825
+ - type: cos_sim_pearson
826
+ value: 26.835294224547155
827
+ - type: cos_sim_spearman
828
+ value: 27.920204597498856
829
+ - type: euclidean_pearson
830
+ value: 26.153796707702803
831
+ - type: euclidean_spearman
832
+ value: 27.920971379720548
833
+ - type: manhattan_pearson
834
+ value: 26.21954147857523
835
+ - type: manhattan_spearman
836
+ value: 27.996860049937478
837
+ - task:
838
+ type: STS
839
+ dataset:
840
+ type: mteb/sts22-crosslingual-sts
841
+ name: MTEB STS22 (zh)
842
+ config: zh
843
+ split: test
844
+ revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
845
+ metrics:
846
+ - type: cos_sim_pearson
847
+ value: 55.15901259718581
848
+ - type: cos_sim_spearman
849
+ value: 61.57967880874167
850
+ - type: euclidean_pearson
851
+ value: 53.83523291596683
852
+ - type: euclidean_spearman
853
+ value: 61.57967880874167
854
+ - type: manhattan_pearson
855
+ value: 54.99971428907956
856
+ - type: manhattan_spearman
857
+ value: 61.61229543613867
858
+ - task:
859
+ type: STS
860
+ dataset:
861
+ type: mteb/sts22-crosslingual-sts
862
+ name: MTEB STS22 (zh-en)
863
+ config: zh-en
864
+ split: test
865
+ revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
866
+ metrics:
867
+ - type: cos_sim_pearson
868
+ value: 34.20930208460845
869
+ - type: cos_sim_spearman
870
+ value: 33.879011104224524
871
+ - type: euclidean_pearson
872
+ value: 35.08526425284862
873
+ - type: euclidean_spearman
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+ value: 33.879011104224524
875
+ - type: manhattan_pearson
876
+ value: 35.509419089701275
877
+ - type: manhattan_spearman
878
+ value: 33.30035487147621
879
+ - task:
880
+ type: STS
881
+ dataset:
882
+ type: C-MTEB/STSB
883
+ name: MTEB STSB
884
+ config: default
885
+ split: test
886
+ revision: None
887
+ metrics:
888
+ - type: cos_sim_pearson
889
+ value: 82.30068282185835
890
+ - type: cos_sim_spearman
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+ value: 82.16763221361724
892
+ - type: euclidean_pearson
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+ value: 80.52772752433374
894
+ - type: euclidean_spearman
895
+ value: 82.16797037220333
896
+ - type: manhattan_pearson
897
+ value: 80.51093859500105
898
+ - type: manhattan_spearman
899
+ value: 82.17643310049654
900
+ - task:
901
+ type: Reranking
902
+ dataset:
903
+ type: C-MTEB/T2Reranking
904
+ name: MTEB T2Reranking
905
+ config: default
906
+ split: dev
907
+ revision: None
908
+ metrics:
909
+ - type: map
910
+ value: 65.14113035189213
911
+ - type: mrr
912
+ value: 74.9589270937443
913
+ - task:
914
+ type: Retrieval
915
+ dataset:
916
+ type: C-MTEB/T2Retrieval
917
+ name: MTEB T2Retrieval
918
+ config: default
919
+ split: dev
920
+ revision: None
921
+ metrics:
922
+ - type: map_at_1
923
+ value: 12.013
924
+ - type: map_at_10
925
+ value: 30.885
926
+ - type: map_at_100
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+ value: 34.643
928
+ - type: map_at_1000
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+ value: 34.927
930
+ - type: map_at_3
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+ value: 21.901
932
+ - type: map_at_5
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+ value: 26.467000000000002
934
+ - type: mrr_at_1
935
+ value: 49.623
936
+ - type: mrr_at_10
937
+ value: 58.05200000000001
938
+ - type: mrr_at_100
939
+ value: 58.61300000000001
940
+ - type: mrr_at_1000
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+ value: 58.643
942
+ - type: mrr_at_3
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+ value: 55.947
944
+ - type: mrr_at_5
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+ value: 57.229
946
+ - type: ndcg_at_1
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948
+ - type: ndcg_at_10
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950
+ - type: ndcg_at_100
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952
+ - type: ndcg_at_1000
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954
+ - type: ndcg_at_3
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+ - type: ndcg_at_5
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+ - type: precision_at_1
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960
+ - type: precision_at_10
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962
+ - type: precision_at_100
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+ value: 3.6450000000000005
964
+ - type: precision_at_1000
965
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966
+ - type: precision_at_3
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+ - type: precision_at_5
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970
+ - type: recall_at_1
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972
+ - type: recall_at_10
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974
+ - type: recall_at_100
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976
+ - type: recall_at_1000
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978
+ - type: recall_at_3
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980
+ - type: recall_at_5
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+ - task:
983
+ type: Classification
984
+ dataset:
985
+ type: C-MTEB/TNews-classification
986
+ name: MTEB TNews
987
+ config: default
988
+ split: validation
989
+ revision: None
990
+ metrics:
991
+ - type: accuracy
992
+ value: 39.800999999999995
993
+ - type: f1
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+ value: 38.5345899934575
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+ - task:
996
+ type: Clustering
997
+ dataset:
998
+ type: C-MTEB/ThuNewsClusteringP2P
999
+ name: MTEB ThuNewsClusteringP2P
1000
+ config: default
1001
+ split: test
1002
+ revision: None
1003
+ metrics:
1004
+ - type: v_measure
1005
+ value: 40.16574242797479
1006
+ - task:
1007
+ type: Clustering
1008
+ dataset:
1009
+ type: C-MTEB/ThuNewsClusteringS2S
1010
+ name: MTEB ThuNewsClusteringS2S
1011
+ config: default
1012
+ split: test
1013
+ revision: None
1014
+ metrics:
1015
+ - type: v_measure
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+ value: 24.232617974671754
1017
+ - task:
1018
+ type: Retrieval
1019
+ dataset:
1020
+ type: C-MTEB/VideoRetrieval
1021
+ name: MTEB VideoRetrieval
1022
+ config: default
1023
+ split: dev
1024
+ revision: None
1025
+ metrics:
1026
+ - type: map_at_1
1027
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1028
+ - type: map_at_10
1029
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1030
+ - type: map_at_100
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+ - type: map_at_1000
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1040
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1041
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+ - type: precision_at_100
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+ - type: precision_at_3
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+ - type: precision_at_5
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1074
+ - type: recall_at_1
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1076
+ - type: recall_at_10
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+ value: 47.4
1078
+ - type: recall_at_100
1079
+ value: 66.0
1080
+ - type: recall_at_1000
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+ value: 83.0
1082
+ - type: recall_at_3
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+ value: 35.0
1084
+ - type: recall_at_5
1085
+ value: 40.300000000000004
1086
+ - task:
1087
+ type: Classification
1088
+ dataset:
1089
+ type: C-MTEB/waimai-classification
1090
+ name: MTEB Waimai
1091
+ config: default
1092
+ split: test
1093
+ revision: None
1094
+ metrics:
1095
+ - type: accuracy
1096
+ value: 83.96000000000001
1097
+ - type: ap
1098
+ value: 65.11027167433211
1099
+ - type: f1
1100
+ value: 82.03549710974653
1101
  ---
1102
 
1103
  # DMetaSoul/sbert-chinese-general-v1