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bwang0911
/
jev2-legal

Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
Generated from Trainer
dataset_size:53224
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
bwang0911 commited on
Commit
c4f9258
·
verified ·
1 Parent(s): 34c8155

Add new SentenceTransformer model

Browse files
Files changed (2) hide show
  1. README.md +96 -96
  2. model.safetensors +1 -1
README.md CHANGED
@@ -357,40 +357,40 @@ model-index:
357
  value: 0.38
358
  name: Cosine Accuracy@5
359
  - type: cosine_accuracy@10
360
- value: 0.52
361
  name: Cosine Accuracy@10
362
  - type: cosine_precision@1
363
  value: 0.22
364
  name: Cosine Precision@1
365
  - type: cosine_precision@3
366
- value: 0.18
367
  name: Cosine Precision@3
368
  - type: cosine_precision@5
369
- value: 0.14
370
  name: Cosine Precision@5
371
  - type: cosine_precision@10
372
- value: 0.102
373
  name: Cosine Precision@10
374
  - type: cosine_recall@1
375
- value: 0.06278088578088578
376
  name: Cosine Recall@1
377
  - type: cosine_recall@3
378
- value: 0.16231934731934733
379
  name: Cosine Recall@3
380
  - type: cosine_recall@5
381
- value: 0.1968951048951049
382
  name: Cosine Recall@5
383
  - type: cosine_recall@10
384
- value: 0.2871608391608392
385
  name: Cosine Recall@10
386
  - type: cosine_ndcg@10
387
- value: 0.2401570635084822
388
  name: Cosine Ndcg@10
389
  - type: cosine_mrr@10
390
- value: 0.29976984126984124
391
  name: Cosine Mrr@10
392
  - type: cosine_map@100
393
- value: 0.21500479010135787
394
  name: Cosine Map@100
395
  - task:
396
  type: information-retrieval
@@ -400,49 +400,49 @@ model-index:
400
  type: mteb/AILA_statutes
401
  metrics:
402
  - type: cosine_accuracy@1
403
- value: 0.26
404
  name: Cosine Accuracy@1
405
  - type: cosine_accuracy@3
406
- value: 0.52
407
  name: Cosine Accuracy@3
408
  - type: cosine_accuracy@5
409
- value: 0.56
410
  name: Cosine Accuracy@5
411
  - type: cosine_accuracy@10
412
- value: 0.78
413
  name: Cosine Accuracy@10
414
  - type: cosine_precision@1
415
- value: 0.26
416
  name: Cosine Precision@1
417
  - type: cosine_precision@3
418
- value: 0.2
419
  name: Cosine Precision@3
420
  - type: cosine_precision@5
421
- value: 0.15200000000000002
422
  name: Cosine Precision@5
423
  - type: cosine_precision@10
424
- value: 0.132
425
  name: Cosine Precision@10
426
  - type: cosine_recall@1
427
- value: 0.066
428
  name: Cosine Recall@1
429
  - type: cosine_recall@3
430
- value: 0.15100000000000002
431
  name: Cosine Recall@3
432
  - type: cosine_recall@5
433
- value: 0.187
434
  name: Cosine Recall@5
435
  - type: cosine_recall@10
436
- value: 0.3096666666666667
437
  name: Cosine Recall@10
438
  - type: cosine_ndcg@10
439
- value: 0.26445384560452256
440
  name: Cosine Ndcg@10
441
  - type: cosine_mrr@10
442
- value: 0.40640476190476194
443
  name: Cosine Mrr@10
444
  - type: cosine_map@100
445
- value: 0.22270227651626964
446
  name: Cosine Map@100
447
  - task:
448
  type: information-retrieval
@@ -452,49 +452,49 @@ model-index:
452
  type: mteb/legalbench_consumer_contracts_qa
453
  metrics:
454
  - type: cosine_accuracy@1
455
- value: 0.4393939393939394
456
  name: Cosine Accuracy@1
457
  - type: cosine_accuracy@3
458
- value: 0.6616161616161617
459
  name: Cosine Accuracy@3
460
  - type: cosine_accuracy@5
461
- value: 0.7626262626262627
462
  name: Cosine Accuracy@5
463
  - type: cosine_accuracy@10
464
- value: 0.8611111111111112
465
  name: Cosine Accuracy@10
466
  - type: cosine_precision@1
467
- value: 0.4393939393939394
468
  name: Cosine Precision@1
469
  - type: cosine_precision@3
470
- value: 0.22053872053872056
471
  name: Cosine Precision@3
472
  - type: cosine_precision@5
473
- value: 0.1525252525252525
474
  name: Cosine Precision@5
475
  - type: cosine_precision@10
476
- value: 0.0861111111111111
477
  name: Cosine Precision@10
478
  - type: cosine_recall@1
479
- value: 0.4393939393939394
480
  name: Cosine Recall@1
481
  - type: cosine_recall@3
482
- value: 0.6616161616161617
483
  name: Cosine Recall@3
484
  - type: cosine_recall@5
485
- value: 0.7626262626262627
486
  name: Cosine Recall@5
487
  - type: cosine_recall@10
488
- value: 0.8611111111111112
489
  name: Cosine Recall@10
490
  - type: cosine_ndcg@10
491
- value: 0.6446558121791953
492
  name: Cosine Ndcg@10
493
  - type: cosine_mrr@10
494
- value: 0.5759569905403237
495
  name: Cosine Mrr@10
496
  - type: cosine_map@100
497
- value: 0.5822763318784706
498
  name: Cosine Map@100
499
  - task:
500
  type: information-retrieval
@@ -504,49 +504,49 @@ model-index:
504
  type: mteb/legalbench_corporate_lobbying
505
  metrics:
506
  - type: cosine_accuracy@1
507
- value: 0.711764705882353
508
  name: Cosine Accuracy@1
509
  - type: cosine_accuracy@3
510
- value: 0.8705882352941177
511
  name: Cosine Accuracy@3
512
  - type: cosine_accuracy@5
513
- value: 0.9088235294117647
514
  name: Cosine Accuracy@5
515
  - type: cosine_accuracy@10
516
- value: 0.9470588235294117
517
  name: Cosine Accuracy@10
518
  - type: cosine_precision@1
519
- value: 0.711764705882353
520
  name: Cosine Precision@1
521
  - type: cosine_precision@3
522
- value: 0.2901960784313726
523
  name: Cosine Precision@3
524
  - type: cosine_precision@5
525
- value: 0.1817647058823529
526
  name: Cosine Precision@5
527
  - type: cosine_precision@10
528
- value: 0.09470588235294117
529
  name: Cosine Precision@10
530
  - type: cosine_recall@1
531
- value: 0.711764705882353
532
  name: Cosine Recall@1
533
  - type: cosine_recall@3
534
- value: 0.8705882352941177
535
  name: Cosine Recall@3
536
  - type: cosine_recall@5
537
- value: 0.9088235294117647
538
  name: Cosine Recall@5
539
  - type: cosine_recall@10
540
- value: 0.9470588235294117
541
  name: Cosine Recall@10
542
  - type: cosine_ndcg@10
543
- value: 0.8320806842083546
544
  name: Cosine Ndcg@10
545
  - type: cosine_mrr@10
546
- value: 0.79484243697479
547
  name: Cosine Mrr@10
548
  - type: cosine_map@100
549
- value: 0.7971232314431899
550
  name: Cosine Map@100
551
  - task:
552
  type: information-retrieval
@@ -556,49 +556,49 @@ model-index:
556
  type: mteb/legal_summarization
557
  metrics:
558
  - type: cosine_accuracy@1
559
- value: 0.4788732394366197
560
  name: Cosine Accuracy@1
561
  - type: cosine_accuracy@3
562
- value: 0.6338028169014085
563
  name: Cosine Accuracy@3
564
  - type: cosine_accuracy@5
565
- value: 0.676056338028169
566
  name: Cosine Accuracy@5
567
  - type: cosine_accuracy@10
568
- value: 0.7676056338028169
569
  name: Cosine Accuracy@10
570
  - type: cosine_precision@1
571
- value: 0.4788732394366197
572
  name: Cosine Precision@1
573
  - type: cosine_precision@3
574
- value: 0.22887323943661966
575
  name: Cosine Precision@3
576
  - type: cosine_precision@5
577
- value: 0.15492957746478872
578
  name: Cosine Precision@5
579
  - type: cosine_precision@10
580
- value: 0.09542253521126762
581
  name: Cosine Precision@10
582
  - type: cosine_recall@1
583
- value: 0.42582045302115723
584
  name: Cosine Recall@1
585
  - type: cosine_recall@3
586
- value: 0.5613769739650022
587
  name: Cosine Recall@3
588
  - type: cosine_recall@5
589
- value: 0.6038934363758307
590
  name: Cosine Recall@5
591
  - type: cosine_recall@10
592
- value: 0.7029716175842936
593
  name: Cosine Recall@10
594
  - type: cosine_ndcg@10
595
- value: 0.5814202795676716
596
  name: Cosine Ndcg@10
597
  - type: cosine_mrr@10
598
- value: 0.5694011289961997
599
  name: Cosine Mrr@10
600
  - type: cosine_map@100
601
- value: 0.5410978538558904
602
  name: Cosine Map@100
603
  ---
604
 
@@ -709,21 +709,21 @@ You can finetune this model on your own dataset.
709
 
710
  | Metric | mteb/AILA_casedocs | mteb/AILA_statutes | mteb/legalbench_consumer_contracts_qa | mteb/legalbench_corporate_lobbying | mteb/legal_summarization |
711
  |:--------------------|:-------------------|:-------------------|:--------------------------------------|:-----------------------------------|:-------------------------|
712
- | cosine_accuracy@1 | 0.22 | 0.26 | 0.4394 | 0.7118 | 0.4789 |
713
- | cosine_accuracy@3 | 0.32 | 0.52 | 0.6616 | 0.8706 | 0.6338 |
714
- | cosine_accuracy@5 | 0.38 | 0.56 | 0.7626 | 0.9088 | 0.6761 |
715
- | cosine_accuracy@10 | 0.52 | 0.78 | 0.8611 | 0.9471 | 0.7676 |
716
- | cosine_precision@1 | 0.22 | 0.26 | 0.4394 | 0.7118 | 0.4789 |
717
- | cosine_precision@3 | 0.18 | 0.2 | 0.2205 | 0.2902 | 0.2289 |
718
- | cosine_precision@5 | 0.14 | 0.152 | 0.1525 | 0.1818 | 0.1549 |
719
- | cosine_precision@10 | 0.102 | 0.132 | 0.0861 | 0.0947 | 0.0954 |
720
- | cosine_recall@1 | 0.0628 | 0.066 | 0.4394 | 0.7118 | 0.4258 |
721
- | cosine_recall@3 | 0.1623 | 0.151 | 0.6616 | 0.8706 | 0.5614 |
722
- | cosine_recall@5 | 0.1969 | 0.187 | 0.7626 | 0.9088 | 0.6039 |
723
- | cosine_recall@10 | 0.2872 | 0.3097 | 0.8611 | 0.9471 | 0.703 |
724
- | **cosine_ndcg@10** | **0.2402** | **0.2645** | **0.6447** | **0.8321** | **0.5814** |
725
- | cosine_mrr@10 | 0.2998 | 0.4064 | 0.576 | 0.7948 | 0.5694 |
726
- | cosine_map@100 | 0.215 | 0.2227 | 0.5823 | 0.7971 | 0.5411 |
727
 
728
  <!--
729
  ## Bias, Risks and Limitations
@@ -938,7 +938,7 @@ You can finetune this model on your own dataset.
938
 
939
  - `eval_strategy`: steps
940
  - `per_device_train_batch_size`: 64
941
- - `learning_rate`: 5e-06
942
  - `num_train_epochs`: 1
943
  - `warmup_ratio`: 0.1
944
  - `fp16`: True
@@ -958,7 +958,7 @@ You can finetune this model on your own dataset.
958
  - `gradient_accumulation_steps`: 1
959
  - `eval_accumulation_steps`: None
960
  - `torch_empty_cache_steps`: None
961
- - `learning_rate`: 5e-06
962
  - `weight_decay`: 0.0
963
  - `adam_beta1`: 0.9
964
  - `adam_beta2`: 0.999
@@ -1067,14 +1067,14 @@ You can finetune this model on your own dataset.
1067
  | Epoch | Step | Training Loss | mteb/AILA_casedocs_cosine_ndcg@10 | mteb/AILA_statutes_cosine_ndcg@10 | mteb/legalbench_consumer_contracts_qa_cosine_ndcg@10 | mteb/legalbench_corporate_lobbying_cosine_ndcg@10 | mteb/legal_summarization_cosine_ndcg@10 |
1068
  |:------:|:----:|:-------------:|:---------------------------------:|:---------------------------------:|:----------------------------------------------------:|:-------------------------------------------------:|:---------------------------------------:|
1069
  | 0 | 0 | - | 0.1972 | 0.2052 | 0.6560 | 0.8641 | 0.5900 |
1070
- | 0.1196 | 100 | - | 0.2523 | 0.2166 | 0.6632 | 0.8721 | 0.6012 |
1071
- | 0.2392 | 200 | - | 0.2656 | 0.2166 | 0.6526 | 0.8549 | 0.5988 |
1072
- | 0.3589 | 300 | - | 0.2647 | 0.2234 | 0.6434 | 0.8455 | 0.5856 |
1073
- | 0.4785 | 400 | - | 0.2533 | 0.2381 | 0.6429 | 0.8368 | 0.5885 |
1074
- | 0.5981 | 500 | 3.4941 | 0.2624 | 0.2515 | 0.6350 | 0.8225 | 0.5776 |
1075
- | 0.7177 | 600 | - | 0.2547 | 0.2667 | 0.6396 | 0.8218 | 0.5804 |
1076
- | 0.8373 | 700 | - | 0.2407 | 0.2646 | 0.6412 | 0.8253 | 0.5827 |
1077
- | 0.9569 | 800 | - | 0.2402 | 0.2645 | 0.6447 | 0.8321 | 0.5814 |
1078
 
1079
 
1080
  ### Framework Versions
 
357
  value: 0.38
358
  name: Cosine Accuracy@5
359
  - type: cosine_accuracy@10
360
+ value: 0.56
361
  name: Cosine Accuracy@10
362
  - type: cosine_precision@1
363
  value: 0.22
364
  name: Cosine Precision@1
365
  - type: cosine_precision@3
366
+ value: 0.1733333333333333
367
  name: Cosine Precision@3
368
  - type: cosine_precision@5
369
+ value: 0.136
370
  name: Cosine Precision@5
371
  - type: cosine_precision@10
372
+ value: 0.10399999999999998
373
  name: Cosine Precision@10
374
  - type: cosine_recall@1
375
+ value: 0.05253846153846153
376
  name: Cosine Recall@1
377
  - type: cosine_recall@3
378
+ value: 0.15198601398601397
379
  name: Cosine Recall@3
380
  - type: cosine_recall@5
381
+ value: 0.18272843822843826
382
  name: Cosine Recall@5
383
  - type: cosine_recall@10
384
+ value: 0.3166759906759907
385
  name: Cosine Recall@10
386
  - type: cosine_ndcg@10
387
+ value: 0.23898276498956456
388
  name: Cosine Ndcg@10
389
  - type: cosine_mrr@10
390
+ value: 0.3051904761904762
391
  name: Cosine Mrr@10
392
  - type: cosine_map@100
393
+ value: 0.1932052448643373
394
  name: Cosine Map@100
395
  - task:
396
  type: information-retrieval
 
400
  type: mteb/AILA_statutes
401
  metrics:
402
  - type: cosine_accuracy@1
403
+ value: 0.2
404
  name: Cosine Accuracy@1
405
  - type: cosine_accuracy@3
406
+ value: 0.38
407
  name: Cosine Accuracy@3
408
  - type: cosine_accuracy@5
409
+ value: 0.5
410
  name: Cosine Accuracy@5
411
  - type: cosine_accuracy@10
412
+ value: 0.68
413
  name: Cosine Accuracy@10
414
  - type: cosine_precision@1
415
+ value: 0.2
416
  name: Cosine Precision@1
417
  - type: cosine_precision@3
418
+ value: 0.14666666666666664
419
  name: Cosine Precision@3
420
  - type: cosine_precision@5
421
+ value: 0.124
422
  name: Cosine Precision@5
423
  - type: cosine_precision@10
424
+ value: 0.10399999999999998
425
  name: Cosine Precision@10
426
  - type: cosine_recall@1
427
+ value: 0.051
428
  name: Cosine Recall@1
429
  - type: cosine_recall@3
430
+ value: 0.109
431
  name: Cosine Recall@3
432
  - type: cosine_recall@5
433
+ value: 0.15600000000000003
434
  name: Cosine Recall@5
435
  - type: cosine_recall@10
436
+ value: 0.25233333333333335
437
  name: Cosine Recall@10
438
  - type: cosine_ndcg@10
439
+ value: 0.2090082947698036
440
  name: Cosine Ndcg@10
441
  - type: cosine_mrr@10
442
+ value: 0.3234603174603174
443
  name: Cosine Mrr@10
444
  - type: cosine_map@100
445
+ value: 0.1807255759521389
446
  name: Cosine Map@100
447
  - task:
448
  type: information-retrieval
 
452
  type: mteb/legalbench_consumer_contracts_qa
453
  metrics:
454
  - type: cosine_accuracy@1
455
+ value: 0.4494949494949495
456
  name: Cosine Accuracy@1
457
  - type: cosine_accuracy@3
458
+ value: 0.6893939393939394
459
  name: Cosine Accuracy@3
460
  - type: cosine_accuracy@5
461
+ value: 0.7929292929292929
462
  name: Cosine Accuracy@5
463
  - type: cosine_accuracy@10
464
+ value: 0.8762626262626263
465
  name: Cosine Accuracy@10
466
  - type: cosine_precision@1
467
+ value: 0.4494949494949495
468
  name: Cosine Precision@1
469
  - type: cosine_precision@3
470
+ value: 0.22979797979797975
471
  name: Cosine Precision@3
472
  - type: cosine_precision@5
473
+ value: 0.15858585858585855
474
  name: Cosine Precision@5
475
  - type: cosine_precision@10
476
+ value: 0.08762626262626264
477
  name: Cosine Precision@10
478
  - type: cosine_recall@1
479
+ value: 0.4494949494949495
480
  name: Cosine Recall@1
481
  - type: cosine_recall@3
482
+ value: 0.6893939393939394
483
  name: Cosine Recall@3
484
  - type: cosine_recall@5
485
+ value: 0.7929292929292929
486
  name: Cosine Recall@5
487
  - type: cosine_recall@10
488
+ value: 0.8762626262626263
489
  name: Cosine Recall@10
490
  - type: cosine_ndcg@10
491
+ value: 0.6582590833247183
492
  name: Cosine Ndcg@10
493
  - type: cosine_mrr@10
494
+ value: 0.5888117283950618
495
  name: Cosine Mrr@10
496
  - type: cosine_map@100
497
+ value: 0.5945634002427188
498
  name: Cosine Map@100
499
  - task:
500
  type: information-retrieval
 
504
  type: mteb/legalbench_corporate_lobbying
505
  metrics:
506
  - type: cosine_accuracy@1
507
+ value: 0.7470588235294118
508
  name: Cosine Accuracy@1
509
  - type: cosine_accuracy@3
510
+ value: 0.9
511
  name: Cosine Accuracy@3
512
  - type: cosine_accuracy@5
513
+ value: 0.9235294117647059
514
  name: Cosine Accuracy@5
515
  - type: cosine_accuracy@10
516
+ value: 0.961764705882353
517
  name: Cosine Accuracy@10
518
  - type: cosine_precision@1
519
+ value: 0.7470588235294118
520
  name: Cosine Precision@1
521
  - type: cosine_precision@3
522
+ value: 0.3
523
  name: Cosine Precision@3
524
  - type: cosine_precision@5
525
+ value: 0.18470588235294116
526
  name: Cosine Precision@5
527
  - type: cosine_precision@10
528
+ value: 0.09617647058823528
529
  name: Cosine Precision@10
530
  - type: cosine_recall@1
531
+ value: 0.7470588235294118
532
  name: Cosine Recall@1
533
  - type: cosine_recall@3
534
+ value: 0.9
535
  name: Cosine Recall@3
536
  - type: cosine_recall@5
537
+ value: 0.9235294117647059
538
  name: Cosine Recall@5
539
  - type: cosine_recall@10
540
+ value: 0.961764705882353
541
  name: Cosine Recall@10
542
  - type: cosine_ndcg@10
543
+ value: 0.8615838412286293
544
  name: Cosine Ndcg@10
545
  - type: cosine_mrr@10
546
+ value: 0.828830532212885
547
  name: Cosine Mrr@10
548
  - type: cosine_map@100
549
+ value: 0.830269992938399
550
  name: Cosine Map@100
551
  - task:
552
  type: information-retrieval
 
556
  type: mteb/legal_summarization
557
  metrics:
558
  - type: cosine_accuracy@1
559
+ value: 0.4894366197183099
560
  name: Cosine Accuracy@1
561
  - type: cosine_accuracy@3
562
+ value: 0.6584507042253521
563
  name: Cosine Accuracy@3
564
  - type: cosine_accuracy@5
565
+ value: 0.721830985915493
566
  name: Cosine Accuracy@5
567
  - type: cosine_accuracy@10
568
+ value: 0.8063380281690141
569
  name: Cosine Accuracy@10
570
  - type: cosine_precision@1
571
+ value: 0.4894366197183099
572
  name: Cosine Precision@1
573
  - type: cosine_precision@3
574
+ value: 0.2417840375586854
575
  name: Cosine Precision@3
576
  - type: cosine_precision@5
577
+ value: 0.1676056338028169
578
  name: Cosine Precision@5
579
  - type: cosine_precision@10
580
+ value: 0.09964788732394367
581
  name: Cosine Precision@10
582
  - type: cosine_recall@1
583
+ value: 0.432778870190842
584
  name: Cosine Recall@1
585
  - type: cosine_recall@3
586
+ value: 0.5777433540637766
587
  name: Cosine Recall@3
588
  - type: cosine_recall@5
589
+ value: 0.6435118285470398
590
  name: Cosine Recall@5
591
  - type: cosine_recall@10
592
+ value: 0.7322247881226754
593
  name: Cosine Recall@10
594
  - type: cosine_ndcg@10
595
+ value: 0.6015252034422203
596
  name: Cosine Ndcg@10
597
  - type: cosine_mrr@10
598
+ value: 0.5894757433489828
599
  name: Cosine Mrr@10
600
  - type: cosine_map@100
601
+ value: 0.5580777128150884
602
  name: Cosine Map@100
603
  ---
604
 
 
709
 
710
  | Metric | mteb/AILA_casedocs | mteb/AILA_statutes | mteb/legalbench_consumer_contracts_qa | mteb/legalbench_corporate_lobbying | mteb/legal_summarization |
711
  |:--------------------|:-------------------|:-------------------|:--------------------------------------|:-----------------------------------|:-------------------------|
712
+ | cosine_accuracy@1 | 0.22 | 0.2 | 0.4495 | 0.7471 | 0.4894 |
713
+ | cosine_accuracy@3 | 0.32 | 0.38 | 0.6894 | 0.9 | 0.6585 |
714
+ | cosine_accuracy@5 | 0.38 | 0.5 | 0.7929 | 0.9235 | 0.7218 |
715
+ | cosine_accuracy@10 | 0.56 | 0.68 | 0.8763 | 0.9618 | 0.8063 |
716
+ | cosine_precision@1 | 0.22 | 0.2 | 0.4495 | 0.7471 | 0.4894 |
717
+ | cosine_precision@3 | 0.1733 | 0.1467 | 0.2298 | 0.3 | 0.2418 |
718
+ | cosine_precision@5 | 0.136 | 0.124 | 0.1586 | 0.1847 | 0.1676 |
719
+ | cosine_precision@10 | 0.104 | 0.104 | 0.0876 | 0.0962 | 0.0996 |
720
+ | cosine_recall@1 | 0.0525 | 0.051 | 0.4495 | 0.7471 | 0.4328 |
721
+ | cosine_recall@3 | 0.152 | 0.109 | 0.6894 | 0.9 | 0.5777 |
722
+ | cosine_recall@5 | 0.1827 | 0.156 | 0.7929 | 0.9235 | 0.6435 |
723
+ | cosine_recall@10 | 0.3167 | 0.2523 | 0.8763 | 0.9618 | 0.7322 |
724
+ | **cosine_ndcg@10** | **0.239** | **0.209** | **0.6583** | **0.8616** | **0.6015** |
725
+ | cosine_mrr@10 | 0.3052 | 0.3235 | 0.5888 | 0.8288 | 0.5895 |
726
+ | cosine_map@100 | 0.1932 | 0.1807 | 0.5946 | 0.8303 | 0.5581 |
727
 
728
  <!--
729
  ## Bias, Risks and Limitations
 
938
 
939
  - `eval_strategy`: steps
940
  - `per_device_train_batch_size`: 64
941
+ - `learning_rate`: 1e-06
942
  - `num_train_epochs`: 1
943
  - `warmup_ratio`: 0.1
944
  - `fp16`: True
 
958
  - `gradient_accumulation_steps`: 1
959
  - `eval_accumulation_steps`: None
960
  - `torch_empty_cache_steps`: None
961
+ - `learning_rate`: 1e-06
962
  - `weight_decay`: 0.0
963
  - `adam_beta1`: 0.9
964
  - `adam_beta2`: 0.999
 
1067
  | Epoch | Step | Training Loss | mteb/AILA_casedocs_cosine_ndcg@10 | mteb/AILA_statutes_cosine_ndcg@10 | mteb/legalbench_consumer_contracts_qa_cosine_ndcg@10 | mteb/legalbench_corporate_lobbying_cosine_ndcg@10 | mteb/legal_summarization_cosine_ndcg@10 |
1068
  |:------:|:----:|:-------------:|:---------------------------------:|:---------------------------------:|:----------------------------------------------------:|:-------------------------------------------------:|:---------------------------------------:|
1069
  | 0 | 0 | - | 0.1972 | 0.2052 | 0.6560 | 0.8641 | 0.5900 |
1070
+ | 0.1196 | 100 | - | 0.2011 | 0.2127 | 0.6534 | 0.8652 | 0.5978 |
1071
+ | 0.2392 | 200 | - | 0.2121 | 0.2171 | 0.6609 | 0.8649 | 0.6017 |
1072
+ | 0.3589 | 300 | - | 0.2160 | 0.2154 | 0.6630 | 0.8668 | 0.6050 |
1073
+ | 0.4785 | 400 | - | 0.2228 | 0.2083 | 0.6600 | 0.8637 | 0.6067 |
1074
+ | 0.5981 | 500 | 4.7925 | 0.2256 | 0.2050 | 0.6578 | 0.8624 | 0.6053 |
1075
+ | 0.7177 | 600 | - | 0.2305 | 0.2049 | 0.6608 | 0.8624 | 0.6032 |
1076
+ | 0.8373 | 700 | - | 0.2377 | 0.2088 | 0.6583 | 0.8616 | 0.6013 |
1077
+ | 0.9569 | 800 | - | 0.2390 | 0.2090 | 0.6583 | 0.8616 | 0.6015 |
1078
 
1079
 
1080
  ### Framework Versions
model.safetensors CHANGED
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  size 90864192
 
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  size 90864192