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Dmeta-embedding-zh

Feature Extraction
sentence-transformers
PyTorch
Chinese
English
bert
sentence-similarity
mteb
RAG
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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@@ -4,12 +4,1065 @@ tags:
4
  - sentence-transformers
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  - feature-extraction
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  - sentence-similarity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7
 
8
  ---
9
 
10
- # {MODEL_NAME}
11
 
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- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
13
 
14
  <!--- Describe your model here -->
15
 
@@ -33,24 +1086,6 @@ print(embeddings)
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  ```
34
 
35
 
36
-
37
- ## Evaluation Results
38
-
39
- <!--- Describe how your model was evaluated -->
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-
41
- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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-
43
-
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-
45
- ## Full Model Architecture
46
- ```
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- SentenceTransformer(
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- (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel
49
- (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
50
- (2): Normalize()
51
- )
52
- ```
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-
54
  ## Citing & Authors
55
 
56
  <!--- Describe where people can find more information -->
 
4
  - sentence-transformers
5
  - feature-extraction
6
  - sentence-similarity
7
+ - mteb
8
+ model-index:
9
+ - name: Dmeta-embedding
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: 65.60825224706932
22
+ - type: cos_sim_spearman
23
+ value: 71.12862586297193
24
+ - type: euclidean_pearson
25
+ value: 70.18130275750404
26
+ - type: euclidean_spearman
27
+ value: 71.12862586297193
28
+ - type: manhattan_pearson
29
+ value: 70.14470398075396
30
+ - type: manhattan_spearman
31
+ value: 71.05226975911737
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: 65.52386345655479
43
+ - type: cos_sim_spearman
44
+ value: 64.64245253181382
45
+ - type: euclidean_pearson
46
+ value: 73.20157662981914
47
+ - type: euclidean_spearman
48
+ value: 64.64245253178956
49
+ - type: manhattan_pearson
50
+ value: 73.22837571756348
51
+ - type: manhattan_spearman
52
+ value: 64.62632334391418
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: 44.925999999999995
64
+ - type: f1
65
+ value: 42.82555191308971
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: 71.35236446393156
77
+ - type: cos_sim_spearman
78
+ value: 72.29629643702184
79
+ - type: euclidean_pearson
80
+ value: 70.94570179874498
81
+ - type: euclidean_spearman
82
+ value: 72.29629297226953
83
+ - type: manhattan_pearson
84
+ value: 70.84463025501125
85
+ - type: manhattan_spearman
86
+ value: 72.24527021975821
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: 40.24232916894152
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: 39.167806226929706
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: 88.48837920106357
120
+ - type: mrr
121
+ value: 90.36861111111111
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: 89.17878171657071
133
+ - type: mrr
134
+ value: 91.35805555555555
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: 25.751
146
+ - type: map_at_10
147
+ value: 38.946
148
+ - type: map_at_100
149
+ value: 40.855000000000004
150
+ - type: map_at_1000
151
+ value: 40.953
152
+ - type: map_at_3
153
+ value: 34.533
154
+ - type: map_at_5
155
+ value: 36.905
156
+ - type: mrr_at_1
157
+ value: 39.235
158
+ - type: mrr_at_10
159
+ value: 47.713
160
+ - type: mrr_at_100
161
+ value: 48.71
162
+ - type: mrr_at_1000
163
+ value: 48.747
164
+ - type: mrr_at_3
165
+ value: 45.086
166
+ - type: mrr_at_5
167
+ value: 46.498
168
+ - type: ndcg_at_1
169
+ value: 39.235
170
+ - type: ndcg_at_10
171
+ value: 45.831
172
+ - type: ndcg_at_100
173
+ value: 53.162
174
+ - type: ndcg_at_1000
175
+ value: 54.800000000000004
176
+ - type: ndcg_at_3
177
+ value: 40.188
178
+ - type: ndcg_at_5
179
+ value: 42.387
180
+ - type: precision_at_1
181
+ value: 39.235
182
+ - type: precision_at_10
183
+ value: 10.273
184
+ - type: precision_at_100
185
+ value: 1.627
186
+ - type: precision_at_1000
187
+ value: 0.183
188
+ - type: precision_at_3
189
+ value: 22.772000000000002
190
+ - type: precision_at_5
191
+ value: 16.524
192
+ - type: recall_at_1
193
+ value: 25.751
194
+ - type: recall_at_10
195
+ value: 57.411
196
+ - type: recall_at_100
197
+ value: 87.44
198
+ - type: recall_at_1000
199
+ value: 98.386
200
+ - type: recall_at_3
201
+ value: 40.416000000000004
202
+ - type: recall_at_5
203
+ value: 47.238
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: 83.59591100420926
215
+ - type: cos_sim_ap
216
+ value: 90.65538153970263
217
+ - type: cos_sim_f1
218
+ value: 84.76466651795673
219
+ - type: cos_sim_precision
220
+ value: 81.04073363190446
221
+ - type: cos_sim_recall
222
+ value: 88.84732288987608
223
+ - type: dot_accuracy
224
+ value: 83.59591100420926
225
+ - type: dot_ap
226
+ value: 90.64355541781003
227
+ - type: dot_f1
228
+ value: 84.76466651795673
229
+ - type: dot_precision
230
+ value: 81.04073363190446
231
+ - type: dot_recall
232
+ value: 88.84732288987608
233
+ - type: euclidean_accuracy
234
+ value: 83.59591100420926
235
+ - type: euclidean_ap
236
+ value: 90.6547878194287
237
+ - type: euclidean_f1
238
+ value: 84.76466651795673
239
+ - type: euclidean_precision
240
+ value: 81.04073363190446
241
+ - type: euclidean_recall
242
+ value: 88.84732288987608
243
+ - type: manhattan_accuracy
244
+ value: 83.51172579675286
245
+ - type: manhattan_ap
246
+ value: 90.59941589844144
247
+ - type: manhattan_f1
248
+ value: 84.51827242524917
249
+ - type: manhattan_precision
250
+ value: 80.28613507258574
251
+ - type: manhattan_recall
252
+ value: 89.22141688099134
253
+ - type: max_accuracy
254
+ value: 83.59591100420926
255
+ - type: max_ap
256
+ value: 90.65538153970263
257
+ - type: max_f1
258
+ value: 84.76466651795673
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: 63.251000000000005
270
+ - type: map_at_10
271
+ value: 72.442
272
+ - type: map_at_100
273
+ value: 72.79299999999999
274
+ - type: map_at_1000
275
+ value: 72.80499999999999
276
+ - type: map_at_3
277
+ value: 70.293
278
+ - type: map_at_5
279
+ value: 71.571
280
+ - type: mrr_at_1
281
+ value: 63.541000000000004
282
+ - type: mrr_at_10
283
+ value: 72.502
284
+ - type: mrr_at_100
285
+ value: 72.846
286
+ - type: mrr_at_1000
287
+ value: 72.858
288
+ - type: mrr_at_3
289
+ value: 70.39
290
+ - type: mrr_at_5
291
+ value: 71.654
292
+ - type: ndcg_at_1
293
+ value: 63.541000000000004
294
+ - type: ndcg_at_10
295
+ value: 76.774
296
+ - type: ndcg_at_100
297
+ value: 78.389
298
+ - type: ndcg_at_1000
299
+ value: 78.678
300
+ - type: ndcg_at_3
301
+ value: 72.47
302
+ - type: ndcg_at_5
303
+ value: 74.748
304
+ - type: precision_at_1
305
+ value: 63.541000000000004
306
+ - type: precision_at_10
307
+ value: 9.115
308
+ - type: precision_at_100
309
+ value: 0.9860000000000001
310
+ - type: precision_at_1000
311
+ value: 0.101
312
+ - type: precision_at_3
313
+ value: 26.379
314
+ - type: precision_at_5
315
+ value: 16.965
316
+ - type: recall_at_1
317
+ value: 63.251000000000005
318
+ - type: recall_at_10
319
+ value: 90.253
320
+ - type: recall_at_100
321
+ value: 97.576
322
+ - type: recall_at_1000
323
+ value: 99.789
324
+ - type: recall_at_3
325
+ value: 78.635
326
+ - type: recall_at_5
327
+ value: 84.141
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: 23.597
339
+ - type: map_at_10
340
+ value: 72.411
341
+ - type: map_at_100
342
+ value: 75.58500000000001
343
+ - type: map_at_1000
344
+ value: 75.64800000000001
345
+ - type: map_at_3
346
+ value: 49.61
347
+ - type: map_at_5
348
+ value: 62.527
349
+ - type: mrr_at_1
350
+ value: 84.65
351
+ - type: mrr_at_10
352
+ value: 89.43900000000001
353
+ - type: mrr_at_100
354
+ value: 89.525
355
+ - type: mrr_at_1000
356
+ value: 89.529
357
+ - type: mrr_at_3
358
+ value: 89.0
359
+ - type: mrr_at_5
360
+ value: 89.297
361
+ - type: ndcg_at_1
362
+ value: 84.65
363
+ - type: ndcg_at_10
364
+ value: 81.47
365
+ - type: ndcg_at_100
366
+ value: 85.198
367
+ - type: ndcg_at_1000
368
+ value: 85.828
369
+ - type: ndcg_at_3
370
+ value: 79.809
371
+ - type: ndcg_at_5
372
+ value: 78.55
373
+ - type: precision_at_1
374
+ value: 84.65
375
+ - type: precision_at_10
376
+ value: 39.595
377
+ - type: precision_at_100
378
+ value: 4.707
379
+ - type: precision_at_1000
380
+ value: 0.485
381
+ - type: precision_at_3
382
+ value: 71.61699999999999
383
+ - type: precision_at_5
384
+ value: 60.45
385
+ - type: recall_at_1
386
+ value: 23.597
387
+ - type: recall_at_10
388
+ value: 83.34
389
+ - type: recall_at_100
390
+ value: 95.19800000000001
391
+ - type: recall_at_1000
392
+ value: 98.509
393
+ - type: recall_at_3
394
+ value: 52.744
395
+ - type: recall_at_5
396
+ value: 68.411
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: 53.1
408
+ - type: map_at_10
409
+ value: 63.359
410
+ - type: map_at_100
411
+ value: 63.9
412
+ - type: map_at_1000
413
+ value: 63.909000000000006
414
+ - type: map_at_3
415
+ value: 60.95
416
+ - type: map_at_5
417
+ value: 62.305
418
+ - type: mrr_at_1
419
+ value: 53.1
420
+ - type: mrr_at_10
421
+ value: 63.359
422
+ - type: mrr_at_100
423
+ value: 63.9
424
+ - type: mrr_at_1000
425
+ value: 63.909000000000006
426
+ - type: mrr_at_3
427
+ value: 60.95
428
+ - type: mrr_at_5
429
+ value: 62.305
430
+ - type: ndcg_at_1
431
+ value: 53.1
432
+ - type: ndcg_at_10
433
+ value: 68.418
434
+ - type: ndcg_at_100
435
+ value: 70.88499999999999
436
+ - type: ndcg_at_1000
437
+ value: 71.135
438
+ - type: ndcg_at_3
439
+ value: 63.50599999999999
440
+ - type: ndcg_at_5
441
+ value: 65.92
442
+ - type: precision_at_1
443
+ value: 53.1
444
+ - type: precision_at_10
445
+ value: 8.43
446
+ - type: precision_at_100
447
+ value: 0.955
448
+ - type: precision_at_1000
449
+ value: 0.098
450
+ - type: precision_at_3
451
+ value: 23.633000000000003
452
+ - type: precision_at_5
453
+ value: 15.340000000000002
454
+ - type: recall_at_1
455
+ value: 53.1
456
+ - type: recall_at_10
457
+ value: 84.3
458
+ - type: recall_at_100
459
+ value: 95.5
460
+ - type: recall_at_1000
461
+ value: 97.5
462
+ - type: recall_at_3
463
+ value: 70.89999999999999
464
+ - type: recall_at_5
465
+ value: 76.7
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: 48.303193535975375
477
+ - type: f1
478
+ value: 35.96559358693866
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: 85.06566604127579
490
+ - type: ap
491
+ value: 52.0596483757231
492
+ - type: f1
493
+ value: 79.5196835127668
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: 74.48499423626059
505
+ - type: cos_sim_spearman
506
+ value: 78.75806756061169
507
+ - type: euclidean_pearson
508
+ value: 78.47917601852879
509
+ - type: euclidean_spearman
510
+ value: 78.75807199272622
511
+ - type: manhattan_pearson
512
+ value: 78.40207586289772
513
+ - type: manhattan_spearman
514
+ value: 78.6911776964119
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: 24.75987466552363
526
+ - type: mrr
527
+ value: 23.40515873015873
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: 58.026999999999994
539
+ - type: map_at_10
540
+ value: 67.50699999999999
541
+ - type: map_at_100
542
+ value: 67.946
543
+ - type: map_at_1000
544
+ value: 67.96600000000001
545
+ - type: map_at_3
546
+ value: 65.503
547
+ - type: map_at_5
548
+ value: 66.649
549
+ - type: mrr_at_1
550
+ value: 60.20100000000001
551
+ - type: mrr_at_10
552
+ value: 68.271
553
+ - type: mrr_at_100
554
+ value: 68.664
555
+ - type: mrr_at_1000
556
+ value: 68.682
557
+ - type: mrr_at_3
558
+ value: 66.47800000000001
559
+ - type: mrr_at_5
560
+ value: 67.499
561
+ - type: ndcg_at_1
562
+ value: 60.20100000000001
563
+ - type: ndcg_at_10
564
+ value: 71.697
565
+ - type: ndcg_at_100
566
+ value: 73.736
567
+ - type: ndcg_at_1000
568
+ value: 74.259
569
+ - type: ndcg_at_3
570
+ value: 67.768
571
+ - type: ndcg_at_5
572
+ value: 69.72
573
+ - type: precision_at_1
574
+ value: 60.20100000000001
575
+ - type: precision_at_10
576
+ value: 8.927999999999999
577
+ - type: precision_at_100
578
+ value: 0.9950000000000001
579
+ - type: precision_at_1000
580
+ value: 0.104
581
+ - type: precision_at_3
582
+ value: 25.883
583
+ - type: precision_at_5
584
+ value: 16.55
585
+ - type: recall_at_1
586
+ value: 58.026999999999994
587
+ - type: recall_at_10
588
+ value: 83.966
589
+ - type: recall_at_100
590
+ value: 93.313
591
+ - type: recall_at_1000
592
+ value: 97.426
593
+ - type: recall_at_3
594
+ value: 73.342
595
+ - type: recall_at_5
596
+ value: 77.997
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: 71.1600537995965
608
+ - type: f1
609
+ value: 68.8126216609964
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: 73.54068594485541
621
+ - type: f1
622
+ value: 73.46845879869848
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: 54.900000000000006
634
+ - type: map_at_10
635
+ value: 61.363
636
+ - type: map_at_100
637
+ value: 61.924
638
+ - type: map_at_1000
639
+ value: 61.967000000000006
640
+ - type: map_at_3
641
+ value: 59.767
642
+ - type: map_at_5
643
+ value: 60.802
644
+ - type: mrr_at_1
645
+ value: 55.1
646
+ - type: mrr_at_10
647
+ value: 61.454
648
+ - type: mrr_at_100
649
+ value: 62.016000000000005
650
+ - type: mrr_at_1000
651
+ value: 62.059
652
+ - type: mrr_at_3
653
+ value: 59.882999999999996
654
+ - type: mrr_at_5
655
+ value: 60.893
656
+ - type: ndcg_at_1
657
+ value: 54.900000000000006
658
+ - type: ndcg_at_10
659
+ value: 64.423
660
+ - type: ndcg_at_100
661
+ value: 67.35900000000001
662
+ - type: ndcg_at_1000
663
+ value: 68.512
664
+ - type: ndcg_at_3
665
+ value: 61.224000000000004
666
+ - type: ndcg_at_5
667
+ value: 63.083
668
+ - type: precision_at_1
669
+ value: 54.900000000000006
670
+ - type: precision_at_10
671
+ value: 7.3999999999999995
672
+ - type: precision_at_100
673
+ value: 0.882
674
+ - type: precision_at_1000
675
+ value: 0.097
676
+ - type: precision_at_3
677
+ value: 21.8
678
+ - type: precision_at_5
679
+ value: 13.98
680
+ - type: recall_at_1
681
+ value: 54.900000000000006
682
+ - type: recall_at_10
683
+ value: 74.0
684
+ - type: recall_at_100
685
+ value: 88.2
686
+ - type: recall_at_1000
687
+ value: 97.3
688
+ - type: recall_at_3
689
+ value: 65.4
690
+ - type: recall_at_5
691
+ value: 69.89999999999999
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: 75.15666666666667
703
+ - type: f1
704
+ value: 74.8306375354435
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: 83.10774228478614
716
+ - type: cos_sim_ap
717
+ value: 87.17679348388666
718
+ - type: cos_sim_f1
719
+ value: 84.59302325581395
720
+ - type: cos_sim_precision
721
+ value: 78.15577439570276
722
+ - type: cos_sim_recall
723
+ value: 92.18585005279832
724
+ - type: dot_accuracy
725
+ value: 83.10774228478614
726
+ - type: dot_ap
727
+ value: 87.17679348388666
728
+ - type: dot_f1
729
+ value: 84.59302325581395
730
+ - type: dot_precision
731
+ value: 78.15577439570276
732
+ - type: dot_recall
733
+ value: 92.18585005279832
734
+ - type: euclidean_accuracy
735
+ value: 83.10774228478614
736
+ - type: euclidean_ap
737
+ value: 87.17679348388666
738
+ - type: euclidean_f1
739
+ value: 84.59302325581395
740
+ - type: euclidean_precision
741
+ value: 78.15577439570276
742
+ - type: euclidean_recall
743
+ value: 92.18585005279832
744
+ - type: manhattan_accuracy
745
+ value: 82.67460747157553
746
+ - type: manhattan_ap
747
+ value: 86.94296334435238
748
+ - type: manhattan_f1
749
+ value: 84.32327166504382
750
+ - type: manhattan_precision
751
+ value: 78.22944896115628
752
+ - type: manhattan_recall
753
+ value: 91.4466737064414
754
+ - type: max_accuracy
755
+ value: 83.10774228478614
756
+ - type: max_ap
757
+ value: 87.17679348388666
758
+ - type: max_f1
759
+ value: 84.59302325581395
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: 93.24999999999999
771
+ - type: ap
772
+ value: 90.98617641063584
773
+ - type: f1
774
+ value: 93.23447883650289
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: 41.071417937737856
786
+ - type: cos_sim_spearman
787
+ value: 45.049199344455424
788
+ - type: euclidean_pearson
789
+ value: 44.913450096830786
790
+ - type: euclidean_spearman
791
+ value: 45.05733424275291
792
+ - type: manhattan_pearson
793
+ value: 44.881623825912065
794
+ - type: manhattan_spearman
795
+ value: 44.989923561416596
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: 41.38238052689359
807
+ - type: cos_sim_spearman
808
+ value: 42.61949690594399
809
+ - type: euclidean_pearson
810
+ value: 40.61261500356766
811
+ - type: euclidean_spearman
812
+ value: 42.619626605620724
813
+ - type: manhattan_pearson
814
+ value: 40.8886109204474
815
+ - type: manhattan_spearman
816
+ value: 42.75791523010463
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: 62.10977863727196
828
+ - type: cos_sim_spearman
829
+ value: 63.843727112473225
830
+ - type: euclidean_pearson
831
+ value: 63.25133487817196
832
+ - type: euclidean_spearman
833
+ value: 63.843727112473225
834
+ - type: manhattan_pearson
835
+ value: 63.58749018644103
836
+ - type: manhattan_spearman
837
+ value: 63.83820575456674
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: 79.30616496720054
849
+ - type: cos_sim_spearman
850
+ value: 80.767935782436
851
+ - type: euclidean_pearson
852
+ value: 80.4160642670106
853
+ - type: euclidean_spearman
854
+ value: 80.76820284024356
855
+ - type: manhattan_pearson
856
+ value: 80.27318714580251
857
+ - type: manhattan_spearman
858
+ value: 80.61030164164964
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: 66.26242871142425
870
+ - type: mrr
871
+ value: 76.20689863623174
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: 26.240999999999996
883
+ - type: map_at_10
884
+ value: 73.009
885
+ - type: map_at_100
886
+ value: 76.893
887
+ - type: map_at_1000
888
+ value: 76.973
889
+ - type: map_at_3
890
+ value: 51.339
891
+ - type: map_at_5
892
+ value: 63.003
893
+ - type: mrr_at_1
894
+ value: 87.458
895
+ - type: mrr_at_10
896
+ value: 90.44
897
+ - type: mrr_at_100
898
+ value: 90.558
899
+ - type: mrr_at_1000
900
+ value: 90.562
901
+ - type: mrr_at_3
902
+ value: 89.89
903
+ - type: mrr_at_5
904
+ value: 90.231
905
+ - type: ndcg_at_1
906
+ value: 87.458
907
+ - type: ndcg_at_10
908
+ value: 81.325
909
+ - type: ndcg_at_100
910
+ value: 85.61999999999999
911
+ - type: ndcg_at_1000
912
+ value: 86.394
913
+ - type: ndcg_at_3
914
+ value: 82.796
915
+ - type: ndcg_at_5
916
+ value: 81.219
917
+ - type: precision_at_1
918
+ value: 87.458
919
+ - type: precision_at_10
920
+ value: 40.534
921
+ - type: precision_at_100
922
+ value: 4.96
923
+ - type: precision_at_1000
924
+ value: 0.514
925
+ - type: precision_at_3
926
+ value: 72.444
927
+ - type: precision_at_5
928
+ value: 60.601000000000006
929
+ - type: recall_at_1
930
+ value: 26.240999999999996
931
+ - type: recall_at_10
932
+ value: 80.42
933
+ - type: recall_at_100
934
+ value: 94.118
935
+ - type: recall_at_1000
936
+ value: 98.02199999999999
937
+ - type: recall_at_3
938
+ value: 53.174
939
+ - type: recall_at_5
940
+ value: 66.739
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: 52.40899999999999
952
+ - type: f1
953
+ value: 50.68532128056062
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: 65.57616085176686
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: 58.844999922904925
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: 58.4
987
+ - type: map_at_10
988
+ value: 68.64
989
+ - type: map_at_100
990
+ value: 69.062
991
+ - type: map_at_1000
992
+ value: 69.073
993
+ - type: map_at_3
994
+ value: 66.567
995
+ - type: map_at_5
996
+ value: 67.89699999999999
997
+ - type: mrr_at_1
998
+ value: 58.4
999
+ - type: mrr_at_10
1000
+ value: 68.64
1001
+ - type: mrr_at_100
1002
+ value: 69.062
1003
+ - type: mrr_at_1000
1004
+ value: 69.073
1005
+ - type: mrr_at_3
1006
+ value: 66.567
1007
+ - type: mrr_at_5
1008
+ value: 67.89699999999999
1009
+ - type: ndcg_at_1
1010
+ value: 58.4
1011
+ - type: ndcg_at_10
1012
+ value: 73.30600000000001
1013
+ - type: ndcg_at_100
1014
+ value: 75.276
1015
+ - type: ndcg_at_1000
1016
+ value: 75.553
1017
+ - type: ndcg_at_3
1018
+ value: 69.126
1019
+ - type: ndcg_at_5
1020
+ value: 71.519
1021
+ - type: precision_at_1
1022
+ value: 58.4
1023
+ - type: precision_at_10
1024
+ value: 8.780000000000001
1025
+ - type: precision_at_100
1026
+ value: 0.968
1027
+ - type: precision_at_1000
1028
+ value: 0.099
1029
+ - type: precision_at_3
1030
+ value: 25.5
1031
+ - type: precision_at_5
1032
+ value: 16.46
1033
+ - type: recall_at_1
1034
+ value: 58.4
1035
+ - type: recall_at_10
1036
+ value: 87.8
1037
+ - type: recall_at_100
1038
+ value: 96.8
1039
+ - type: recall_at_1000
1040
+ value: 99.0
1041
+ - type: recall_at_3
1042
+ value: 76.5
1043
+ - type: recall_at_5
1044
+ value: 82.3
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: 86.21000000000001
1056
+ - type: ap
1057
+ value: 69.17460264576461
1058
+ - type: f1
1059
+ value: 84.68032984659226
1060
+
1061
 
1062
  ---
1063
 
1064
+ # {Dmeta-embedding}
1065
 
 
1066
 
1067
  <!--- Describe your model here -->
1068
 
 
1086
  ```
1087
 
1088
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1089
  ## Citing & Authors
1090
 
1091
  <!--- Describe where people can find more information -->