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Mihaiii
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gte-micro-v4

Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
feature-extraction
gte
mteb
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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@@ -8,6 +8,472 @@ tags:
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  - sentence-similarity
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  - gte
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  - mteb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11
  ---
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  # gte-micro-v4
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8
  - sentence-similarity
9
  - gte
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  - mteb
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+ model-index:
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+ - name: gte-micro-v4
13
+ results:
14
+ - task:
15
+ type: Classification
16
+ dataset:
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+ type: mteb/amazon_counterfactual
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+ name: MTEB AmazonCounterfactualClassification (en)
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+ config: en
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+ split: test
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+ revision: e8379541af4e31359cca9fbcf4b00f2671dba205
22
+ metrics:
23
+ - type: accuracy
24
+ value: 71.83582089552239
25
+ - type: ap
26
+ value: 34.436093320979126
27
+ - type: f1
28
+ value: 65.82844954638102
29
+ - task:
30
+ type: Classification
31
+ dataset:
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+ type: mteb/amazon_polarity
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+ name: MTEB AmazonPolarityClassification
34
+ config: default
35
+ split: test
36
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
37
+ metrics:
38
+ - type: accuracy
39
+ value: 80.03957500000001
40
+ - type: ap
41
+ value: 74.4510899901909
42
+ - type: f1
43
+ value: 79.98034714963279
44
+ - task:
45
+ type: Classification
46
+ dataset:
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+ type: mteb/amazon_reviews_multi
48
+ name: MTEB AmazonReviewsClassification (en)
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+ config: en
50
+ split: test
51
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
52
+ metrics:
53
+ - type: accuracy
54
+ value: 39.754
55
+ - type: f1
56
+ value: 39.423135672769796
57
+ - task:
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+ type: Clustering
59
+ dataset:
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+ type: mteb/arxiv-clustering-p2p
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+ name: MTEB ArxivClusteringP2P
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+ config: default
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+ split: test
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+ revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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+ metrics:
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+ - type: v_measure
67
+ value: 42.85928858083004
68
+ - task:
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+ type: Clustering
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+ dataset:
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+ type: mteb/arxiv-clustering-s2s
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+ name: MTEB ArxivClusteringS2S
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+ config: default
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+ split: test
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+ revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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+ metrics:
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+ - type: v_measure
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+ value: 32.475201371814784
79
+ - task:
80
+ type: Reranking
81
+ dataset:
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+ type: mteb/askubuntudupquestions-reranking
83
+ name: MTEB AskUbuntuDupQuestions
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+ config: default
85
+ split: test
86
+ revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
87
+ metrics:
88
+ - type: map
89
+ value: 58.01141755339977
90
+ - type: mrr
91
+ value: 71.70821791320407
92
+ - task:
93
+ type: Classification
94
+ dataset:
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+ type: mteb/banking77
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+ name: MTEB Banking77Classification
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+ config: default
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+ split: test
99
+ revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
100
+ metrics:
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+ - type: accuracy
102
+ value: 80.9220779220779
103
+ - type: f1
104
+ value: 80.86851039874094
105
+ - task:
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+ type: Clustering
107
+ dataset:
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+ type: mteb/biorxiv-clustering-p2p
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+ name: MTEB BiorxivClusteringP2P
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+ config: default
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+ split: test
112
+ revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
113
+ metrics:
114
+ - type: v_measure
115
+ value: 36.82555236565894
116
+ - task:
117
+ type: Clustering
118
+ dataset:
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+ type: mteb/biorxiv-clustering-s2s
120
+ name: MTEB BiorxivClusteringS2S
121
+ config: default
122
+ split: test
123
+ revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
124
+ metrics:
125
+ - type: v_measure
126
+ value: 29.243444611175995
127
+ - task:
128
+ type: Classification
129
+ dataset:
130
+ type: mteb/emotion
131
+ name: MTEB EmotionClassification
132
+ config: default
133
+ split: test
134
+ revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
135
+ metrics:
136
+ - type: accuracy
137
+ value: 44.87500000000001
138
+ - type: f1
139
+ value: 39.78455417008123
140
+ - task:
141
+ type: Classification
142
+ dataset:
143
+ type: mteb/imdb
144
+ name: MTEB ImdbClassification
145
+ config: default
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+ split: test
147
+ revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
148
+ metrics:
149
+ - type: accuracy
150
+ value: 71.9568
151
+ - type: ap
152
+ value: 65.91179027501194
153
+ - type: f1
154
+ value: 71.85575290323182
155
+ - task:
156
+ type: Classification
157
+ dataset:
158
+ type: mteb/mtop_domain
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+ name: MTEB MTOPDomainClassification (en)
160
+ config: en
161
+ split: test
162
+ revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
163
+ metrics:
164
+ - type: accuracy
165
+ value: 90.87323301413589
166
+ - type: f1
167
+ value: 90.45433994230181
168
+ - task:
169
+ type: Classification
170
+ dataset:
171
+ type: mteb/mtop_intent
172
+ name: MTEB MTOPIntentClassification (en)
173
+ config: en
174
+ split: test
175
+ revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
176
+ metrics:
177
+ - type: accuracy
178
+ value: 68.53169174646602
179
+ - type: f1
180
+ value: 50.49367676485481
181
+ - task:
182
+ type: Classification
183
+ dataset:
184
+ type: mteb/amazon_massive_intent
185
+ name: MTEB MassiveIntentClassification (en)
186
+ config: en
187
+ split: test
188
+ revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
189
+ metrics:
190
+ - type: accuracy
191
+ value: 69.11230665770007
192
+ - type: f1
193
+ value: 66.9035022957204
194
+ - task:
195
+ type: Classification
196
+ dataset:
197
+ type: mteb/amazon_massive_scenario
198
+ name: MTEB MassiveScenarioClassification (en)
199
+ config: en
200
+ split: test
201
+ revision: 7d571f92784cd94a019292a1f45445077d0ef634
202
+ metrics:
203
+ - type: accuracy
204
+ value: 74.15601882985877
205
+ - type: f1
206
+ value: 74.059011768806
207
+ - task:
208
+ type: Clustering
209
+ dataset:
210
+ type: mteb/medrxiv-clustering-p2p
211
+ name: MTEB MedrxivClusteringP2P
212
+ config: default
213
+ split: test
214
+ revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
215
+ metrics:
216
+ - type: v_measure
217
+ value: 32.551619758274406
218
+ - task:
219
+ type: Clustering
220
+ dataset:
221
+ type: mteb/medrxiv-clustering-s2s
222
+ name: MTEB MedrxivClusteringS2S
223
+ config: default
224
+ split: test
225
+ revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
226
+ metrics:
227
+ - type: v_measure
228
+ value: 30.80210958999942
229
+ - task:
230
+ type: Clustering
231
+ dataset:
232
+ type: mteb/reddit-clustering
233
+ name: MTEB RedditClustering
234
+ config: default
235
+ split: test
236
+ revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
237
+ metrics:
238
+ - type: v_measure
239
+ value: 48.27542501963987
240
+ - task:
241
+ type: Clustering
242
+ dataset:
243
+ type: mteb/reddit-clustering-p2p
244
+ name: MTEB RedditClusteringP2P
245
+ config: default
246
+ split: test
247
+ revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
248
+ metrics:
249
+ - type: v_measure
250
+ value: 53.55942763860501
251
+ - task:
252
+ type: PairClassification
253
+ dataset:
254
+ type: mteb/sprintduplicatequestions-pairclassification
255
+ name: MTEB SprintDuplicateQuestions
256
+ config: default
257
+ split: test
258
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
259
+ metrics:
260
+ - type: cos_sim_accuracy
261
+ value: 99.82673267326733
262
+ - type: cos_sim_ap
263
+ value: 95.53621808930455
264
+ - type: cos_sim_f1
265
+ value: 91.19275289380975
266
+ - type: cos_sim_precision
267
+ value: 91.7933130699088
268
+ - type: cos_sim_recall
269
+ value: 90.60000000000001
270
+ - type: dot_accuracy
271
+ value: 99.75445544554455
272
+ - type: dot_ap
273
+ value: 92.76410342229411
274
+ - type: dot_f1
275
+ value: 87.50612444879961
276
+ - type: dot_precision
277
+ value: 85.78290105667628
278
+ - type: dot_recall
279
+ value: 89.3
280
+ - type: euclidean_accuracy
281
+ value: 99.82673267326733
282
+ - type: euclidean_ap
283
+ value: 95.46124795179632
284
+ - type: euclidean_f1
285
+ value: 91.01181304571135
286
+ - type: euclidean_precision
287
+ value: 93.55860612460401
288
+ - type: euclidean_recall
289
+ value: 88.6
290
+ - type: manhattan_accuracy
291
+ value: 99.82871287128712
292
+ - type: manhattan_ap
293
+ value: 95.51436288466519
294
+ - type: manhattan_f1
295
+ value: 91.11891620672353
296
+ - type: manhattan_precision
297
+ value: 91.44008056394763
298
+ - type: manhattan_recall
299
+ value: 90.8
300
+ - type: max_accuracy
301
+ value: 99.82871287128712
302
+ - type: max_ap
303
+ value: 95.53621808930455
304
+ - type: max_f1
305
+ value: 91.19275289380975
306
+ - task:
307
+ type: Clustering
308
+ dataset:
309
+ type: mteb/stackexchange-clustering
310
+ name: MTEB StackExchangeClustering
311
+ config: default
312
+ split: test
313
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
314
+ metrics:
315
+ - type: v_measure
316
+ value: 55.0721745308552
317
+ - task:
318
+ type: Clustering
319
+ dataset:
320
+ type: mteb/stackexchange-clustering-p2p
321
+ name: MTEB StackExchangeClusteringP2P
322
+ config: default
323
+ split: test
324
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
325
+ metrics:
326
+ - type: v_measure
327
+ value: 31.91639764792279
328
+ - task:
329
+ type: Classification
330
+ dataset:
331
+ type: mteb/toxic_conversations_50k
332
+ name: MTEB ToxicConversationsClassification
333
+ config: default
334
+ split: test
335
+ revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
336
+ metrics:
337
+ - type: accuracy
338
+ value: 66.0402
339
+ - type: ap
340
+ value: 12.106715125588833
341
+ - type: f1
342
+ value: 50.67443088623853
343
+ - task:
344
+ type: Classification
345
+ dataset:
346
+ type: mteb/tweet_sentiment_extraction
347
+ name: MTEB TweetSentimentExtractionClassification
348
+ config: default
349
+ split: test
350
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
351
+ metrics:
352
+ - type: accuracy
353
+ value: 59.42840973401245
354
+ - type: f1
355
+ value: 59.813350770208665
356
+ - task:
357
+ type: Clustering
358
+ dataset:
359
+ type: mteb/twentynewsgroups-clustering
360
+ name: MTEB TwentyNewsgroupsClustering
361
+ config: default
362
+ split: test
363
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
364
+ metrics:
365
+ - type: v_measure
366
+ value: 41.37273187829312
367
+ - task:
368
+ type: PairClassification
369
+ dataset:
370
+ type: mteb/twittersemeval2015-pairclassification
371
+ name: MTEB TwitterSemEval2015
372
+ config: default
373
+ split: test
374
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
375
+ metrics:
376
+ - type: cos_sim_accuracy
377
+ value: 84.10919711509806
378
+ - type: cos_sim_ap
379
+ value: 67.55255054010537
380
+ - type: cos_sim_f1
381
+ value: 64.22774378823823
382
+ - type: cos_sim_precision
383
+ value: 60.9623133443944
384
+ - type: cos_sim_recall
385
+ value: 67.86279683377309
386
+ - type: dot_accuracy
387
+ value: 80.62228050306967
388
+ - type: dot_ap
389
+ value: 54.81480289413879
390
+ - type: dot_f1
391
+ value: 54.22550997534184
392
+ - type: dot_precision
393
+ value: 47.13561964146532
394
+ - type: dot_recall
395
+ value: 63.82585751978892
396
+ - type: euclidean_accuracy
397
+ value: 84.04363116170948
398
+ - type: euclidean_ap
399
+ value: 67.77652401372912
400
+ - type: euclidean_f1
401
+ value: 64.46694460988684
402
+ - type: euclidean_precision
403
+ value: 58.762214983713356
404
+ - type: euclidean_recall
405
+ value: 71.39841688654354
406
+ - type: manhattan_accuracy
407
+ value: 83.94230196101806
408
+ - type: manhattan_ap
409
+ value: 67.419155052755
410
+ - type: manhattan_f1
411
+ value: 64.15049692380501
412
+ - type: manhattan_precision
413
+ value: 58.151008151008156
414
+ - type: manhattan_recall
415
+ value: 71.53034300791556
416
+ - type: max_accuracy
417
+ value: 84.10919711509806
418
+ - type: max_ap
419
+ value: 67.77652401372912
420
+ - type: max_f1
421
+ value: 64.46694460988684
422
+ - task:
423
+ type: PairClassification
424
+ dataset:
425
+ type: mteb/twitterurlcorpus-pairclassification
426
+ name: MTEB TwitterURLCorpus
427
+ config: default
428
+ split: test
429
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
430
+ metrics:
431
+ - type: cos_sim_accuracy
432
+ value: 88.25823728024217
433
+ - type: cos_sim_ap
434
+ value: 84.67785320317506
435
+ - type: cos_sim_f1
436
+ value: 76.67701296330108
437
+ - type: cos_sim_precision
438
+ value: 72.92491491282907
439
+ - type: cos_sim_recall
440
+ value: 80.83615645210965
441
+ - type: dot_accuracy
442
+ value: 84.63344588038964
443
+ - type: dot_ap
444
+ value: 75.25182203961072
445
+ - type: dot_f1
446
+ value: 70.35217601881962
447
+ - type: dot_precision
448
+ value: 63.87737152908657
449
+ - type: dot_recall
450
+ value: 78.28765013858947
451
+ - type: euclidean_accuracy
452
+ value: 88.2504754142896
453
+ - type: euclidean_ap
454
+ value: 84.68882859374924
455
+ - type: euclidean_f1
456
+ value: 76.69534508021188
457
+ - type: euclidean_precision
458
+ value: 74.89177489177489
459
+ - type: euclidean_recall
460
+ value: 78.58792731752386
461
+ - type: manhattan_accuracy
462
+ value: 88.26211821321846
463
+ - type: manhattan_ap
464
+ value: 84.60061548046698
465
+ - type: manhattan_f1
466
+ value: 76.63928519959647
467
+ - type: manhattan_precision
468
+ value: 72.02058504875406
469
+ - type: manhattan_recall
470
+ value: 81.89097628580228
471
+ - type: max_accuracy
472
+ value: 88.26211821321846
473
+ - type: max_ap
474
+ value: 84.68882859374924
475
+ - type: max_f1
476
+ value: 76.69534508021188
477
  ---
478
  # gte-micro-v4
479