File size: 111,162 Bytes
d6e1f65
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:638837
- loss:GISTEmbedLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: The Co-operative has beaten sales growth at its major supermarket
    rivals for the first time in five years.
  sentences:
  - 'Evans had been due to remain with the Saddlers until 26 January but was recalled
    from his loan on Saturday.

    The 21-year-old, who scored four goals in 16 appearances on loan at Walsall, moves
    on a three-and-a-half-year deal.

    "I''m sure he will become an even better player working in our environment at
    Reading," said manager Brian McDermott.

    Find all the latest football transfers on our dedicated page.'
  - 'A jury decided the case against Peter Barrowman, 35, was not proven following
    a trial at the High Court in Glasgow.

    Speaking outside court, the father-of-two from Stenhousemuir told BBC Scotland
    that the verdict was a "massive relief".

    He said: "I am so happy that I can go home. I can''t really put it into words,
    but I''m just very happy."

    Mr Barrowman, who worked at Cornton Vale prison, is currently suspended by the
    Nursing and Midwifery Council.

    Asked if he would seek a return to nursing, he replied: "That''s a decision I''ve
    still to make."

    Three women - all prisoners in the jail - claimed Mr Barrowman raped them between
    January and March 2014 while they were his patients.

    He denied any sexual contact with the women and claimed they were lying.

    The nurse was suspended from working at Cornton Vale in March 2014, after the
    allegations were made and he was dismissed in July last year.

    Mr Barrowman was also acquitted of charges of supplying drugs to prisoners. The
    charges were withdrawn by the Crown before the end of the trial.'
  - 'The Co-op''s sales grew by 1.4% in the 12 weeks to 31 January, according to research
    company Kantar Worldpanel.

    People shopped there most frequently, at an average 19 times over the period,
    compared with a market average of 11 visits.

    In contrast, Sainsbury''s was the only major chain to report rising sales, up
    0.6%.

    Revenues at Tesco fell by 1.6% over the last three months, although Kantar said
    it was the supermarket group''s best reading since last September.

    At Morrisons, sales shrank by 2.2%, while at Asda, which is owned by US chain
    Walmart, revenue fell by 3.8%.

    Sainsbury''s, which recently made a £1.3bn bid for Argos-owner Home Retail Group,
    marginally increased its share of the UK market to 16.8%, ahead of Asda''s 16.2%
    share and Morrisons with 10.8%. Tesco remains the market leader with 28.5%.

    The Co-op maintained its 5.9% share of the British market.

    Kantar said it was the first time since 2011 that the Co-op had surpassed its
    non-discounter rivals in terms of sales.

    The retail-to-funerals group, the UK''s largest mutual, has been beset with problems
    in recent years, most notably at the Co-operative Bank, which is now majority
    owned by bondholders, although Co-op Group retains a 20% stake.

    The discounters, Lidl and Aldi, both saw double-digit revenue growth, up 18.7%
    and 13.7% respectively. At the same time, Waitrose''s sales were up 0.1% for the
    three months.'
- source_sentence: A standing man talks to a seated woman while on some sort of vehicle.
  sentences:
  - Two people are talking.
  - A photo set of two women at work
  - No two people are talking.
- source_sentence: who knows the art of chakravyuh in mahabharat
  sentences:
  - History of the Detroit Red Wings The Detroit Red Wings professional ice hockey
    club was founded as the Detroit Cougars on September 25, 1926, one of three teams
    to join the National Hockey League (NHL) in 1926. With the demise of the Western
    Canada Hockey League (WCHL), the rights to the players of the Victoria Cougars
    were purchased by a Detroit group led by Charles A. Hughes who kept the name "Cougars"
    for their NHL club. The new team struggled financially; in 1930, the Cougars changed
    their name to the Detroit Falcons, and after being bought out of receivership
    by James E. Norris were renamed as the Detroit Red Wings in 1932. The team played
    their first game on November 18, 1926, and won their first two Stanley Cup titles
    in 1936 and 1937. The Red Wings have won the Cup eleven times, more than any other
    American team in NHL history.
  - 'Padmavyuha The Chakravyūha or Padmavyūha was a very special formation (vyuha),
    and knowledge of how to penetrate it was limited to only a handful of warriors
    on the Pandavas'' side, namely: Abhimanyu, Arjuna, Krishna and Pradyumna, of which
    only Abhimanyu was present when the Kauravas used it on the battlefield. In the
    Mahabharata it is mentioned that Abhimanyu learnt about the Chakravyūha while
    in his mother''s womb but he was not able to hear how to escape the formation.
    After Abhimanyu had penetrated the sixth tier of the formation, all the Kauravas''
    commanders attacked him simultaneously, which was against the righteous rules
    of warfare Dharmayuddha, and gradually exhausted and killed him.[2]'
  - Ford Power Stroke engine The 7.3 L DI Power Stroke was in production until the
    second quarter of model year 2003 when it was replaced by the 6.0L because of
    its inability to meet newer emission requirements. Nearly 2 million 7.3s were
    produced from International's Indianapolis plant.[2]
- source_sentence: who elect the deputy chairman of rajya sabha
  sentences:
  - Mishael Morgan Marie-Charms Mishael Morgan (born July 15, 1986) known professionally
    as Mishael Morgan, is a Trinidadian-Canadian actress known for the role of Hilary
    Curtis on CBS Daytime soap opera, The Young and the Restless.
  - Rajya Sabha The Vice President of India (currently, Venkaiah Naidu) is the ex-officio
    Chairman of the Rajya Sabha, who presides over its sessions. The Deputy Chairman,
    who is elected from amongst the house's members, takes care of the day-to-day
    matters of the house in the absence of the Chairman. The Rajya Sabha held its
    first sitting on 13 May 1952.[6] The salary and other benefits for a member of
    Rajya Sabha are same as for a member of Lok Sabha.
  - 'History of film D. W. Griffith had the highest standing among American directors
    in the industry, because of the dramatic excitement he conveyed to the audience
    through his films. The American film industry, or "Hollywood", as it was becoming
    known after its new geographical center in Hollywood, a neighborhood in Los Angeles,
    California, gained the position it has held, more or less, ever since: film factory
    for the world and exporting its product to most countries. By the 1920s, the United
    States reached what is still its era of greatest-ever output, producing an average
    of 800 feature films annually,[1] or 82% of the global total (Eyman, 1997). During
    late 1927, Warner''s released The Jazz Singer, with the first synchronized dialogue
    (and singing) in a feature film. By the end of 1929, Hollywood was almost all-talkie,
    with several competing sound systems (soon to be standardized). Sound saved the
    Hollywood studio system in the face of the Great Depression (Parkinson, 1995).'
- source_sentence: In Sri Lanka , the title of Chartered Accountant ( CA Sri Lanka
    ) can be used by only members of the Institute of Chartered Accountants of Sri
    Lanka .
  sentences:
  - Holly Holly was influenced by Elton John musically .
  - In Sri Lanka , the title of an accountant ( CA Sri Lanka ) can only be used by
    members of the Institute of Accountants in Sri Lanka .
  - He was the half-brother of Lord Alfred Paget , Lord George Paget and Lord Clarence
    Paget .
datasets:
- bobox/enhanced_NLI-50K
- sentence-transformers/natural-questions
- tals/vitaminc
- bobox/xSum-processed
- google-research-datasets/paws
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.9037962998590239
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9228968065127588
      name: Spearman Cosine
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: allNLI dev
      type: allNLI-dev
    metrics:
    - type: cosine_accuracy
      value: 0.7734375
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7482618689537048
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6769230769230768
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.694869875907898
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5789473684210527
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8148148148148148
      name: Cosine Recall
    - type: cosine_ap
      value: 0.6542135747366074
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.5058060457173612
      name: Cosine Mcc
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Qnli dev
      type: Qnli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.69921875
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.6639883518218994
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6956521739130435
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.6639883518218994
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.676923076923077
      name: Cosine Precision
    - type: cosine_recall
      value: 0.7154471544715447
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7520437823272439
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.3994014078011956
      name: Cosine Mcc
---

# SentenceTransformer based on BAAI/bge-m3

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the [NLI](https://huggingface.co/datasets/bobox/enhanced_NLI-50K), [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions), [vitaminc](https://huggingface.co/datasets/tals/vitaminc), [xsum](https://huggingface.co/datasets/bobox/xSum-processed), [paws](https://huggingface.co/datasets/google-research-datasets/paws) and global_dataset datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - [NLI](https://huggingface.co/datasets/bobox/enhanced_NLI-50K)
    - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
    - [vitaminc](https://huggingface.co/datasets/tals/vitaminc)
    - [xsum](https://huggingface.co/datasets/bobox/xSum-processed)
    - [paws](https://huggingface.co/datasets/google-research-datasets/paws)
    - global_dataset
- **Language:** en
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): AdvancedWeightedPooling(
    (mha): MultiheadAttention(
      (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True)
    )
    (MLP): Sequential(
      (0): SwiGLUBlock(
        (in_proj_swish): Linear(in_features=1024, out_features=2048, bias=True)
        (in_proj_gate): Linear(in_features=1024, out_features=2048, bias=True)
      )
      (1): Dropout(p=0.05, inplace=False)
      (2): Linear(in_features=2048, out_features=1024, bias=True)
    )
    (layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
  )
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bobox/XLMRoBERTaM3-CustomPoolin-v1-s1-checkpoints-tmp")
# Run inference
sentences = [
    'In Sri Lanka , the title of Chartered Accountant ( CA Sri Lanka ) can be used by only members of the Institute of Chartered Accountants of Sri Lanka .',
    'In Sri Lanka , the title of an accountant ( CA Sri Lanka ) can only be used by members of the Institute of Accountants in Sri Lanka .',
    'Holly Holly was influenced by Elton John musically .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Semantic Similarity

* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9038     |
| **spearman_cosine** | **0.9229** |

#### Binary Classification

* Datasets: `allNLI-dev` and `Qnli-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                    | allNLI-dev | Qnli-dev  |
|:--------------------------|:-----------|:----------|
| cosine_accuracy           | 0.7734     | 0.6992    |
| cosine_accuracy_threshold | 0.7483     | 0.664     |
| cosine_f1                 | 0.6769     | 0.6957    |
| cosine_f1_threshold       | 0.6949     | 0.664     |
| cosine_precision          | 0.5789     | 0.6769    |
| cosine_recall             | 0.8148     | 0.7154    |
| **cosine_ap**             | **0.6542** | **0.752** |
| cosine_mcc                | 0.5058     | 0.3994    |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Datasets
<details><summary>NLI</summary>

#### NLI

* Dataset: [NLI](https://huggingface.co/datasets/bobox/enhanced_NLI-50K) at [d43e6fe](https://huggingface.co/datasets/bobox/enhanced_NLI-50K/tree/d43e6fe7f1e171f916502c123235d4b9ec997cb4)
* Size: 20,100 training samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | entailment                                                                        | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 25.86 tokens</li><li>max: 170 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.58 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.65 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                               | entailment                                                                              | negative                                                                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
  | <code>Cugny is a commune . It is found in the region Picardie in the Aisne department in the north of France .</code>                | <code>Cugny is a commune in the Aisne department in Picardy in northern France .</code> | <code>Cugny is a commune in the Var department in Provence-Alpes-Côte d`Azur in southern France.</code> |
  | <code>Two dogs on leashes look toward the door. </code>                                                                              | <code>The dogs are ready to go out for a walk.</code>                                   | <code>The dogs are not ready to go out for a walk.</code>                                               |
  | <code>Denver Broncos Kyle Orton and Knowshon Moreno will both be listed as questionable for Sunday's game against Cincinnati.</code> | <code>Kyle Orton, Knowshon Moreno are questionable for Sunday</code>                    | <code>Kyle Borton, Knowshon Merino are available for Monday</code>                                      |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>natural-questions</summary>

#### natural-questions

* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 20,100 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 10 tokens</li><li>mean: 13.39 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 151.47 tokens</li><li>max: 652 tokens</li></ul> |
* Samples:
  | sentence1                                                                   | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  |:----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>who was the first person to use fossils for dating rock layers</code> | <code>Relative dating The regular order of occurrence of fossils in rock layers was discovered around 1800 by William Smith. While digging the Somerset Coal Canal in southwest England, he found that fossils were always in the same order in the rock layers. As he continued his job as a surveyor, he found the same patterns across England. He also found that certain animals were in only certain layers and that they were in the same layers all across England. Due to that discovery, Smith was able to recognize the order that the rocks were formed. Sixteen years after his discovery, he published a geological map of England showing the rocks of different geologic time eras.</code>                                                                                                                                                                                                                                                                                                                                               |
  | <code>how many games are in the eastern conference finals</code>            | <code>NBA Conference Finals Initially, the BAA teams were aligned into two divisions, the Eastern Division and the Western Division. The Divisional Finals were first played in 1949, the league's third season. The first two seasons used a playoffs format where Eastern and Western Division teams would face each other before the BAA Finals, hence there were no divisional finals. In the 1949–50 season, the league realigned itself to three divisions, with the addition of the Central Division. However, the arrangement was only used for one season and the league went back into two divisions format in 1951. The two divisions format remained until 1970, when the NBA realigned itself into two conferences with two divisions each, which led to the renaming to Conference Finals. The finals was a best-of-3 series from 1949 to 1950 to; a best-of-5 series from 1951–56, and a best-of-7 series since 1957. Currently, the Conference Finals are played in a best-of-7 series like the NBA Playoffs and Finals. The t...</code> |
  | <code>what is the difference between sheraton and four points</code>        | <code>Four Points by Sheraton In April 1995, Sheraton Hotels and Resorts introduced a new, up-scale hotel brand Four Points by Sheraton Hotels, to replace the designation of certain hotels as Sheraton Inns. In 1998, Starwood Hotels & Resorts Worldwide, Inc. acquired ITT Sheraton, outbidding Hilton. In 2000, Starwood re-launched Four Points by Sheraton, now targeted as a premier upscale five star hotel chain for business and leisure travelers.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>vitaminc</summary>

#### vitaminc

* Dataset: [vitaminc](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
* Size: 370,653 training samples
* Columns: <code>claim</code> and <code>evidence</code>
* Approximate statistics based on the first 1000 samples:
  |         | claim                                                                             | evidence                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 21.17 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 46.62 tokens</li><li>max: 293 tokens</li></ul> |
* Samples:
  | claim                                                                                                                           | evidence                                                                                                                                                                                                                |
  |:--------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>In 2013 Copa Sudamericana finals , Lanus won against Ponte Preta with Agustin Marchesin starting in both matches .</code> | <code>In 2013 , Marches�n won the Copa Sudamericana with Lan�s , starting in both matches of the finals against Ponte Preta of Brazil.</code>                                                                           |
  | <code>More Life ( album ) debuted number one on the US Billboard 200 .</code>                                                   | <code>More Life received generally positive reviews and debuted at number one on the US Billboard 200 , becoming his seventh consecutive number one album , while also breaking several streaming records.</code>       |
  | <code>Brandon Paak Anderson was born in Ventura County , California .</code>                                                    | <code>Brandon Paak Anderson ( born February 8 , 1986 ) , known professionally as Anderson .Paak , is an American singer , songwriter , rapper , drummer , and record producer from Ventura County , California .</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>xsum</summary>

#### xsum

* Dataset: [xsum](https://huggingface.co/datasets/bobox/xSum-processed) at [044020f](https://huggingface.co/datasets/bobox/xSum-processed/tree/044020f516c1830da392e567474cd5452971366f)
* Size: 131,779 training samples
* Columns: <code>summary</code> and <code>document</code>
* Approximate statistics based on the first 1000 samples:
  |         | summary                                                                           | document                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 8 tokens</li><li>mean: 30.94 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 60 tokens</li><li>mean: 336.79 tokens</li><li>max: 616 tokens</li></ul> |
* Samples:
  | summary                                                                                                                                                                              | document                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Salford Red Devils hooker Logan Tomkins and winger Greg Johnson have both signed new undisclosed-length contracts with the Super League club.</code>                           | <code>Tomkins, 24, joined Salford on a permanent deal from Wigan in 2016 and has become their first-choice hooker.<br>Johnson, 27, has been at the AJ Bell Stadium since 2014 and scored 10 tries this season in 18 appearances.<br>"Logan is one of the hardest workers around, he does so much unseen work," head coach Ian Watson said.<br>"Greg is one of the best wingers in Super League when it comes to getting the team on the front foot. He's an extremely good trainer and that shows on the field," Watson added.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
  | <code>Valtteri Bottas expects to be allowed to race on "equal terms" with Lewis Hamilton after being confirmed as Nico Rosberg's replacement at Mercedes.</code>                     | <code>Media playback is not supported on this device<br>Bottas, 27, has signed a one-year deal with the option for more after arriving from Williams.<br>"We are in this together, with similar cars and equipment to win races. I'm sure they will let us race hard and fair," the Finn told BBC Radio 5 live.<br>"Lewis is a great driver and everyone knows how quick he is."<br>Bottas, who made his grand prix debut with Williams in 2013, has not won a race in F1, while Briton Hamilton is a 53-time race winner and three-time world champion.<br>I certainly think Valtteri can give Lewis a run for his money<br>"It's going to be a big challenge for me to be quicker than him," added Bottas.<br>"He knows this team really well, has been part of this team a long time, so for me it's going to be a great reference and we can be a strong pair."<br>Rosberg's shock retirement, announced just five days after he wrapped up his maiden world title, presented Bottas with an unexpected chance to land the most coveted seat in the sport, and tea...</code> |
  | <code>The family of an Indian man who was lynched by a mob over rumours he consumed beef has denied reports that they no longer want a police investigation into the killing.</code> | <code>Mohammad Akhlaq was beaten to death by a mob in Dadri in Uttar Pradesh state in late September.<br>His son, Mohammed Sartaj, told BBC Hindi's Salman Ravi that he "is waiting for the police to charge the suspects".<br>Six people have been arrested in connection with the attack.<br>"We will go to the president of India if we have to. We will also demand an inquiry by the Central Bureau of Investigation [India's top investigative body] if we feel that the Uttar Pradesh police is trying to save those involved in the case," Mr Sartaj, who works for the Indian Air Force, told BBC Hindi's Salman Ravi.<br>He was responding to reports which said Mr Akhlaq's family had said they were satisfied with the compensation they had received and did not want further investigations.<br>Mr Sartaj said that the police had already spoken to his family.<br>"They have also taken statements from eye witnesses. My family has told the police about the people who were involved in the attack.  Now we have to see if the police tries ...</code>       |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>paws</summary>

#### paws

* Dataset: [paws](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 49,401 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 31.51 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 31.48 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                             | sentence2                                                                                                                                                                             |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>In 2014 , Harris Publications acquired `` XXL '' , `` King '' and `` Antenna '' from Townsquare Media .</code>                                                                  | <code>In 2014 Harris Publications '' XXL `` , '' King `` and '' Antenna `` acquired by Townsquare Media .</code>                                                                      |
  | <code>Kallir is incompetent , but in politics very honest .</code>                                                                                                                    | <code>Kallir is incompetent , but very honest in politics .</code>                                                                                                                    |
  | <code>This association was further enhanced after the female Christian missionary , Nino , converted Mirian , his wife Nana and household into Christianity in or around 337 .</code> | <code>This association was further strengthened after the Christian female missionary Nino , Mirian , his wife Nana and household converted to Christianity in or around 337 .</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>global_dataset</summary>

#### global_dataset

* Dataset: global_dataset
* Size: 46,804 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              |
  | details | <ul><li>min: 7 tokens</li><li>mean: 24.52 tokens</li><li>max: 129 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 119.66 tokens</li><li>max: 575 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                  | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The Organizing Committee banned the unauthorized filming , and on 6 August 2008 , investigated SBS cameras inside the stadium during the ceremony as reprisals for the leak .</code> | <code>The organizing committee banned the unauthorized filming and investigated SBS cameras in the stadium on August 6 , 2008 during the ceremony as retaliation for the leak .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
  | <code>Aaron O'Connor, Lenny Pidgeley, Lee Minshull and captain Andy Sandell are among 11 senior players to be released by Newport County.</code>                                           | <code>Striker O'Connor tweeted: "Wanna thank everyone at Newport County for an amazing few years.<br>Goalkeeper Pidgeley said "won't be getting another contract at Newport", while midfielder Minshull said "it's official, I'm a free agent".<br>Midfielder Adam Chapman has turned down a new deal to stay at Rodney Parade.<br>Shaun Jeffers, Max Porter, Jamie Stephens, Robbie Willmott, Ismail Yakubu and Mike Flynn are the other senior players to be released.<br>Youngsters Joe Parker and Kyle Patten have also left County.<br>Just seven players at the club are under contract for next season: Joe Day, Mark Byrne, Kevin Feely, Yan Klukowski, Aaron Collins, Tom Owen-Evans and Kieran Parselle.<br>New manager Terry Butcher has offered contract extensions to Darren Jones, Ryan Jackson and Andrew Hughes.<br>Pidgeley finished the season on loan at Mansfield, but O'Connor, Minshull and Sandell have been three of the Exiles' key players this term.<br>O'Connor scored 11 goals in 42 appearances, Minshull also made 42 appearances for t...</code> |
  | <code>A former senior police officer has warned that Brexit could lead to "civil unrest" in Northern Ireland.</code>                                                                       | <code>Peter Sheridan, now chief executive at Cooperation Ireland said the peace process is in a "fragile state".<br>He said the numerous agreements that make up Northern Ireland's peace process show the brittle nature of its politics.<br>"Less than a year ago, both governments had to step in to rescue the executive," said Mr Sheridan.<br>Although stressing that he did not predict the violence "in the way it was [during the Troubles]", he added: "We've already seen peaceful protests along the border, even though nothing has happened on the border yet.<br>"The history of this place is that mass protest can lead to confrontation and unrest.<br>"We've had three shootings in the last few days, so we shouldn't be surprised that there would be people who would seek to exploit a change in the border."<br>The former assistant chief constable denied that he was scaremongering: "I would challenge anyone who says there won't be people who will seek to exploit the border," he told the BBC's Good Morning Ulster programme.<br>...</code>    |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>

### Evaluation Datasets
<details><summary>NLI</summary>

#### NLI

* Dataset: [NLI](https://huggingface.co/datasets/bobox/enhanced_NLI-50K) at [d43e6fe](https://huggingface.co/datasets/bobox/enhanced_NLI-50K/tree/d43e6fe7f1e171f916502c123235d4b9ec997cb4)
* Size: 85 evaluation samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 85 samples:
  |         | anchor                                                                             | entailment                                                                        | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 10 tokens</li><li>mean: 18.18 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.54 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.82 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                    | entailment                                                                                     | negative                                                                                         |
  |:------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | <code>A crowd is watching a group of men in suits with briefcases walk in formation down the street led by a woman holding a sign.</code> | <code>A group of well dressed people walk down a block with one of them holding a sign.</code> | <code>A group of poorly dressed people walk down a block with one of them holding a sign.</code> |
  | <code>two women loading their bicycles onto a bus rack</code>                                                                             | <code>Two woman loading bikes onto a public bus.</code>                                        | <code>Two women unloading bikes from a private bus.</code>                                       |
  | <code>A person wearing a straw hat, standing outside working a steel apparatus with a pile of coconuts on the ground.</code>              | <code>A person is near a pile of coconuts.</code>                                              | <code>A person is far from a pile of coconuts.</code>                                            |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>natural-questions</summary>

#### natural-questions

* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 113 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 113 samples:
  |         | sentence1                                                                          | sentence2                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 10 tokens</li><li>mean: 13.02 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 149.73 tokens</li><li>max: 590 tokens</li></ul> |
* Samples:
  | sentence1                                                          | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
  |:-------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>who is the chief minister of all india</code>                | <code>List of current Indian chief ministers In the Republic of India, a chief minister is the head of government of each of twenty-nine states and two union territories (Delhi and Puducherry). According to the Constitution of India, at the state-level, the governor is de jure head, but de facto executive authority rests with the chief minister. Following elections to the state legislative assembly, the governor usually invites the party (or coalition) with a majority of seats to form the government. The governor appoints the chief minister, whose council of ministers are collectively responsible to the assembly. Given he has the assembly's confidence, the chief minister's term is usually for a maximum of five years; there are no limits to the number of terms he/she can serve.[1]</code>                                                                                                                                                                          |
  | <code>when does elena come back from being katherine</code>        | <code>No Exit (The Vampire Diaries) Damon wakes up chained on the Salvatore house's basement. Stefan is there and Damon tries to warn him that because of his cravings, one day he will kill him but Stefan tells him that they will find a solution. Damon points to Stefan that "Elena" provoked him to feed on her and then she kicked a stake towards him so he will kill him. Stefan does not believe that Elena would want that, he locks Damon up and gets upstairs where Caroline is. The two of them discuss what happened between Stefan and "Elena" and Caroline also tells him about Nadia and Matt and the text he sent to her. From Matt's text, who used "K" and not "E", they put the pieces together and they realize that Katherine is in Elena's body.</code>                                                                                                                                                                                                                       |
  | <code>is the eureka tower the tallest building in australia</code> | <code>Eureka Tower Eureka Tower is a 297.3-metre (975 ft) skyscraper located in the Southbank precinct of Melbourne, Victoria, Australia.[3] Construction began in August 2002 and the exterior completed on 1 June 2006. The plaza was finished in June 2006 and the building was officially opened on 11 October 2006.[4] The project was designed by Melbourne architectural firm Fender Katsalidis Architects and was built by Grocon (Grollo Australia). The developer of the tower was Eureka Tower Pty Ltd, a joint venture consisting of Daniel Grollo (Grocon), investor Tab Fried and one of the Tower's architects Nonda Katsalidis. It was the world's tallest residential tower when measured to its highest floor,[5] until surpassed by Ocean Heights and the HHHR Tower in Dubai. It is the second tallest building in Australia, behind Q1, Queensland, and is the tallest to roof (excluding spire).[6] As of 2016 it is the 15th tallest residential building in the world.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>vitaminc</summary>

#### vitaminc

* Dataset: [vitaminc](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
* Size: 63,054 evaluation samples
* Columns: <code>claim</code> and <code>evidence</code>
* Approximate statistics based on the first 1000 samples:
  |         | claim                                                                              | evidence                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 22.27 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 37.93 tokens</li><li>max: 81 tokens</li></ul> |
* Samples:
  | claim                                                                                                        | evidence                                                                                                                                                                                                  |
  |:-------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>By March 23 , 99,000 patients had recovered from coronavirus .</code>                                  | <code>As of 23 March , more than 341,000 cases of COVID-19 have been reported in over 190 countries and territories , resulting in more than 14,700 deaths and over 99,000 recoveries .</code>            |
  | <code>More than 681,500 cases of COVID-19 have been reported along with less than 32,000 deaths .</code>     | <code>more than 683,000 cases of COVID-19 have been reported in over 190 countries and territories , resulting in approximately 32,100 deaths .</code>                                                    |
  | <code>On March 13th , over 145,000 cases of COVID-19 have been confirmed in more than 130 countries .</code> | <code>As of March 13th , over 145,000 cases of COVID-19 have been confirmed in more than 130 countries and territories , with major outbreaks in mainland China , Italy , South Korea , and Iran .</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>xsum</summary>

#### xsum

* Dataset: [xsum](https://huggingface.co/datasets/bobox/xSum-processed) at [044020f](https://huggingface.co/datasets/bobox/xSum-processed/tree/044020f516c1830da392e567474cd5452971366f)
* Size: 131,779 evaluation samples
* Columns: <code>summary</code> and <code>document</code>
* Approximate statistics based on the first 1000 samples:
  |         | summary                                                                            | document                                                                             |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 17 tokens</li><li>mean: 30.55 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 306.88 tokens</li><li>max: 554 tokens</li></ul> |
* Samples:
  | summary                                                                                                                                                                        | document                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Jim Atchison, the chief executive of SeaWorld, has resigned.</code>                                                                                                      | <code>The aquatic amusement park has struggled to attract visitors in the wake of a 2013 film, Blackfish, which criticised its treatment of killer whales.<br>SeaWorld acknowledged in August that the film had hurt revenues at its San Diego, California park.<br>The company's share price has fallen 44% this year, and now trades around $16 per share.<br>In a statement, SeaWorld said that current board chairman David F D'Alessandro would serve as interim chief executive, and that the firm would also continue with its plans to reorganise in order to save $50m by the end of 2015.<br>The company said that part of the restructuring would involve job cuts.<br>SeaWorld operates 11 theme parks globally.<br>In its most recent earnings report, it said attendance had dropped to 8.4 million visitors in the third-quarter of 2014 from 8.9 million in the same period a year earlier.<br>It attributed the decline to "a combination of factors including negative media attention in California along with a challenging competitive environ...</code> |
  | <code>Organised criminal gangs are believed to be behind the thefts of more than 100 sheep and other animals.</code>                                                           | <code>North Wales Police have warned farmers after livestock was taken from farms in Gwynedd, Conwy county and Denbighshire in the last few weeks.<br>Officers have said 80 sheep have gone missing in Bala, Llanrwst, Betws y Coed and Rhuallt in the last week<br>They said the thefts appeared to be a professional operation.<br>Rob Taylor, from the force's rural crime team, said the animals are likely being sold on or slaughtered illegally.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  | <code>Scottish FA performance director Malky Mackay will this week attempt to convince clubs to back proposed reductions to the number of funded academies in Scotland.</code> | <code>The suggestion is part of Project Brave - the outcome of an SFA working group tasked with improving and increasing the development of elite players.<br>It recommends reducing the 29 funded academies to a maximum of 16.<br>Mackay will consult member clubs in detail at four events this week.<br>Under the plans, clubs will have their academies assessed by an independent company to assess which ones meet the new criteria.<br>"We need to focus on the very best players in the very best academies with our limited resources we have," SFA chief executive Stewart Regan said.<br>"One of the recommendations from the working group was to have no more than 16 academies in Scotland defined as elite. Any club can put a bid in, and they will be independently audited against a defined set of criteria. If they are successful, they will be included in Scotland's list of elite academies."<br>Project Brave's recommendations:<br>The working group for the strategy was formed in March last year, and initiated by Mackay's predecess...</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>paws</summary>

#### paws

* Dataset: [paws](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 8,000 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 15 tokens</li><li>mean: 30.68 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 30.42 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                             | sentence2                                                                                                                             |
  |:--------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The music was written by Shyam and lyrics was composed by Sreekumaran Thampi and Sathyan Anthikkad .</code>                     | <code>The music was written by Shyam and the lyrics by Sreekumaran Thampi and Sathyan Anthikkad composed .</code>                     |
  | <code>Wright moved from Chapel Hill , NC to New York .</code>                                                                         | <code>Wright moved to New York from Chapel Hill , NC .</code>                                                                         |
  | <code>Massé was born in Holland , Michigan , grew up in Westchester County , New York , and lived during her youth in Europe .</code> | <code>Massé was born in Holland , Michigan , grew up in Westchester County , New York , and lived in Europe during her teens .</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>
<details><summary>global_dataset</summary>

#### global_dataset

* Dataset: global_dataset
* Size: 256 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 256 samples:
  |         | sentence1                                                                         | sentence2                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 24.09 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 114.19 tokens</li><li>max: 572 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                   | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
  |:----------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Cardiff City will want at least £5m for striker Joe Mason, BBC Wales Sport has learned.</code>                        | <code>The 24-year-old is understood to have attracted interest from Championship rivals Wolverhampton Wanderers.<br>But a senior Cardiff source has indicated the Bluebirds will want "at least £5m or £6m" for the former Republic of Ireland Under-21 forward.<br>Mason joined Cardiff from Plymouth Argyle in 2011 for £250,000 and has scored 22 goals in 60 League starts.<br>This season Mason has chipped in with six goals, enjoying a prolonged run in the team having had three loan spells at Bolton Wanderers during previous campaigns.<br>Cardiff are under a transfer embargo, but chief executive Ken Choo made it clear the club would not be forced to sell their best players as a result of the restriction.<br>The club are also looking to draft in at least three players on loan.<br>Mason and midfielder Joe Ralls, 22, are seen by the club as prized young assets.</code> |
  | <code>A crowded city street with lots of pedestrians.</code>                                                                | <code>There are many pedestrians on the city street.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  | <code>Until 1951 , he was active as a socialist in post-war legislation when he decided to focus on local politics .</code> | <code>He was active as a socialist in the post-war legislature until 1951 , when he decided to focus on local politics .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  ```
</details>

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 256
- `gradient_accumulation_steps`: 3
- `learning_rate`: 0.001
- `weight_decay`: 0.001
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 0.0003333333333333333}
- `warmup_ratio`: 0.2
- `save_safetensors`: False
- `fp16`: True
- `remove_unused_columns`: False
- `push_to_hub`: True
- `hub_model_id`: bobox/XLMRoBERTaM3-CustomPoolin-v1-s1-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `hub_private_repo`: False
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 3
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.001
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 0.0003333333333333333}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: False
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/XLMRoBERTaM3-CustomPoolin-v1-s1-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step | Training Loss | NLI loss | natural-questions loss | vitaminc loss | xsum loss | paws loss | global dataset loss | sts-test_spearman_cosine | allNLI-dev_cosine_ap | Qnli-dev_cosine_ap |
|:------:|:----:|:-------------:|:--------:|:----------------------:|:-------------:|:---------:|:---------:|:-------------------:|:------------------------:|:--------------------:|:------------------:|
| 0.0027 | 1    | 7.8455        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0054 | 2    | 10.2716       | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0081 | 3    | 8.8104        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0108 | 4    | 9.723         | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0135 | 5    | 8.2787        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0162 | 6    | 2.1849        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0189 | 7    | 6.9562        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0216 | 8    | 8.0012        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0243 | 9    | 7.2178        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0270 | 10   | 3.7548        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0296 | 11   | 1.9087        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0323 | 12   | 2.4871        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0350 | 13   | 2.0275        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0377 | 14   | 1.7456        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0404 | 15   | 1.8779        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0431 | 16   | 1.3286        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0458 | 17   | 1.9446        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0485 | 18   | 1.7784        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0512 | 19   | 2.2488        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0539 | 20   | 1.4934        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0566 | 21   | 1.6026        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0593 | 22   | 1.1284        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0620 | 23   | 0.8786        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0647 | 24   | 0.9379        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0674 | 25   | 1.4386        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0701 | 26   | 1.4041        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0728 | 27   | 0.5954        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0755 | 28   | 1.0351        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0782 | 29   | 1.1524        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0809 | 30   | 0.4302        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0836 | 31   | 1.3629        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0863 | 32   | 0.801         | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0889 | 33   | 0.7135        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0916 | 34   | 1.0678        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0943 | 35   | 0.8164        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0970 | 36   | 0.7157        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.0997 | 37   | 0.4396        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1024 | 38   | 0.9716        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1051 | 39   | 0.9321        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1078 | 40   | 0.5171        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1105 | 41   | 1.9291        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1132 | 42   | 1.0919        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1159 | 43   | 1.4841        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1186 | 44   | 1.1497        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1213 | 45   | 0.7006        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1240 | 46   | 1.7046        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1267 | 47   | 0.8556        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1294 | 48   | 1.6158        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1321 | 49   | 1.3545        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1348 | 50   | 1.0598        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1375 | 51   | 1.3435        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1402 | 52   | 0.5441        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1429 | 53   | 0.8275        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1456 | 54   | 1.0796        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1482 | 55   | 0.9102        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1509 | 56   | 0.6044        | 0.7544   | 0.1253                 | 1.7376        | 0.3028    | 0.0228    | 0.3648              | 0.9132                   | 0.6860               | 0.7579             |
| 0.1536 | 57   | 0.6791        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1563 | 58   | 2.0332        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1590 | 59   | 1.6908        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1617 | 60   | 1.9835        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1644 | 61   | 0.5596        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1671 | 62   | 0.5455        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1698 | 63   | 1.4403        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1725 | 64   | 1.3612        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1752 | 65   | 0.4134        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1779 | 66   | 1.1737        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1806 | 67   | 0.9298        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1833 | 68   | 1.1334        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1860 | 69   | 1.0759        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1887 | 70   | 0.961         | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1914 | 71   | 0.329         | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1941 | 72   | 1.4607        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1968 | 73   | 1.13          | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.1995 | 74   | 0.4843        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2022 | 75   | 1.1167        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2049 | 76   | 0.7438        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2075 | 77   | 1.0913        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2102 | 78   | 1.0479        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2129 | 79   | 1.1103        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2156 | 80   | 0.9204        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2183 | 81   | 0.9669        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2210 | 82   | 1.6861        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2237 | 83   | 1.076         | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2264 | 84   | 1.2668        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2291 | 85   | 1.4458        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2318 | 86   | 1.0282        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2345 | 87   | 0.9422        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2372 | 88   | 0.8485        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2399 | 89   | 0.6634        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2426 | 90   | 0.5559        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2453 | 91   | 0.4284        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2480 | 92   | 0.8843        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2507 | 93   | 1.1379        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2534 | 94   | 0.8325        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2561 | 95   | 0.7055        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2588 | 96   | 0.5176        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2615 | 97   | 0.6018        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2642 | 98   | 1.2091        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2668 | 99   | 1.1578        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2695 | 100  | 1.3774        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2722 | 101  | 1.6864        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2749 | 102  | 1.5131        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2776 | 103  | 1.5626        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2803 | 104  | 1.5972        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2830 | 105  | 1.1608        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2857 | 106  | 1.7296        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2884 | 107  | 0.7756        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2911 | 108  | 1.4043        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2938 | 109  | 1.0434        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2965 | 110  | 0.7851        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.2992 | 111  | 0.8959        | -        | -                      | -             | -         | -         | -                   | -                        | -                    | -                  |
| 0.3019 | 112  | 1.1451        | 0.7854   | 0.1174                 | 2.1760        | 0.1466    | 0.0221    | 0.5730              | 0.9229                   | 0.6542               | 0.7520             |

</details>

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.51.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.5.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
    author={Aivin V. Solatorio},
    year={2024},
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->