henryscheible / bert-base-uncased_crows_pairs_finetuned

huggingface.co
Total runs: 9
24-hour runs: 0
7-day runs: 1
30-day runs: -1
Model's Last Updated: March 12 2023
text-classification

Introduction of bert-base-uncased_crows_pairs_finetuned

Model Details of bert-base-uncased_crows_pairs_finetuned

bert-base-uncased_crows_pairs_finetuned

This model is a fine-tuned version of bert-base-uncased on the crows_pairs dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1731
  • Accuracy: 0.7649
  • Tp: 0.3344
  • Tn: 0.4305
  • Fp: 0.1126
  • Fn: 0.1225
Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure
Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
Training results
Training Loss Epoch Step Validation Loss Accuracy Tp Tn Fp Fn
0.703 1.05 20 0.6889 0.5430 0.0 0.5430 0.0 0.4570
0.6884 2.11 40 0.6886 0.5 0.3013 0.1987 0.3444 0.1556
0.5388 3.16 60 0.6347 0.7053 0.1821 0.5232 0.0199 0.2748
0.2228 4.21 80 0.9802 0.6987 0.1887 0.5099 0.0331 0.2682
0.1644 5.26 100 0.7523 0.7583 0.3675 0.3907 0.1523 0.0894
0.0478 6.32 120 1.5712 0.6954 0.2053 0.4901 0.0530 0.2517
0.0465 7.37 140 1.2587 0.7351 0.2781 0.4570 0.0861 0.1788
0.0313 8.42 160 1.5825 0.7450 0.3775 0.3675 0.1755 0.0795
0.0137 9.47 180 1.3570 0.7318 0.2815 0.4503 0.0927 0.1755
0.04 10.53 200 2.1377 0.6921 0.1887 0.5033 0.0397 0.2682
0.0041 11.58 220 1.6776 0.7351 0.3278 0.4073 0.1358 0.1291
0.0042 12.63 240 1.8873 0.7086 0.2980 0.4106 0.1325 0.1589
0.0009 13.68 260 2.2464 0.6987 0.3543 0.3444 0.1987 0.1026
0.014 14.74 280 1.9753 0.7252 0.3245 0.4007 0.1424 0.1325
0.0026 15.79 300 1.8852 0.7417 0.2914 0.4503 0.0927 0.1656
0.0147 16.84 320 2.0273 0.7351 0.3113 0.4238 0.1192 0.1457
0.0009 17.89 340 1.7328 0.7483 0.3278 0.4205 0.1225 0.1291
0.0085 18.95 360 2.0146 0.7450 0.2815 0.4636 0.0795 0.1755
0.0001 20.0 380 2.0808 0.7450 0.3113 0.4338 0.1093 0.1457
0.0001 21.05 400 2.2655 0.7417 0.3609 0.3808 0.1623 0.0960
0.0034 22.11 420 2.0298 0.7583 0.3079 0.4503 0.0927 0.1490
0.0082 23.16 440 2.0650 0.7550 0.3344 0.4205 0.1225 0.1225
0.0001 24.21 460 2.2472 0.7450 0.2748 0.4702 0.0728 0.1821
0.0001 25.26 480 2.3655 0.7351 0.3709 0.3642 0.1788 0.0861
0.0004 26.32 500 2.1407 0.7550 0.3510 0.4040 0.1391 0.1060
0.0001 27.37 520 2.1168 0.7450 0.3642 0.3808 0.1623 0.0927
0.0002 28.42 540 2.2050 0.7517 0.3775 0.3742 0.1689 0.0795
0.0 29.47 560 2.0560 0.7682 0.3212 0.4470 0.0960 0.1358
0.0 30.53 580 2.0859 0.7715 0.3179 0.4536 0.0894 0.1391
0.0 31.58 600 2.0958 0.7715 0.3179 0.4536 0.0894 0.1391
0.0 32.63 620 2.1039 0.7715 0.3179 0.4536 0.0894 0.1391
0.0 33.68 640 2.1113 0.7715 0.3179 0.4536 0.0894 0.1391
0.0 34.74 660 2.1180 0.7715 0.3179 0.4536 0.0894 0.1391
0.0 35.79 680 2.1127 0.7715 0.3278 0.4437 0.0993 0.1291
0.0 36.84 700 2.1376 0.7682 0.3377 0.4305 0.1126 0.1192
0.0 37.89 720 2.1460 0.7616 0.3377 0.4238 0.1192 0.1192
0.0 38.95 740 2.1507 0.7649 0.3377 0.4272 0.1159 0.1192
0.0 40.0 760 2.1548 0.7682 0.3377 0.4305 0.1126 0.1192
0.0 41.05 780 2.1586 0.7682 0.3377 0.4305 0.1126 0.1192
0.0 42.11 800 2.1620 0.7682 0.3377 0.4305 0.1126 0.1192
0.0 43.16 820 2.1649 0.7682 0.3377 0.4305 0.1126 0.1192
0.0 44.21 840 2.1674 0.7682 0.3377 0.4305 0.1126 0.1192
0.0 45.26 860 2.1690 0.7682 0.3377 0.4305 0.1126 0.1192
0.0 46.32 880 2.1705 0.7682 0.3377 0.4305 0.1126 0.1192
0.0 47.37 900 2.1717 0.7649 0.3344 0.4305 0.1126 0.1225
0.0 48.42 920 2.1726 0.7649 0.3344 0.4305 0.1126 0.1225
0.0 49.47 940 2.1731 0.7649 0.3344 0.4305 0.1126 0.1225
Framework versions
  • Transformers 4.26.1
  • Pytorch 1.13.1
  • Datasets 2.10.1
  • Tokenizers 0.13.2

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