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update model card README.md
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metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - crows_pairs
metrics:
  - accuracy
model-index:
  - name: gpt2_crows_pairs_finetuned
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: crows_pairs
          type: crows_pairs
          config: crows_pairs
          split: test
          args: crows_pairs
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7781456953642384

gpt2_crows_pairs_finetuned

This model is a fine-tuned version of gpt2 on the crows_pairs dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0946
  • Accuracy: 0.7781
  • Tp: 0.3444
  • Tn: 0.4338
  • Fp: 0.1159
  • Fn: 0.1060

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: 5e-05
  • 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
0.7371 1.05 20 0.7345 0.4669 0.4305 0.0364 0.5132 0.0199
0.6794 2.11 40 0.6829 0.5397 0.3013 0.2384 0.3113 0.1490
0.5972 3.16 60 0.6602 0.6291 0.3411 0.2881 0.2616 0.1093
0.4691 4.21 80 0.6568 0.6788 0.3742 0.3046 0.2450 0.0762
0.3645 5.26 100 0.5872 0.7252 0.2815 0.4437 0.1060 0.1689
0.2645 6.32 120 0.6835 0.7185 0.2318 0.4868 0.0629 0.2185
0.1698 7.37 140 0.7757 0.7483 0.2914 0.4570 0.0927 0.1589
0.1386 8.42 160 0.7445 0.7417 0.2881 0.4536 0.0960 0.1623
0.077 9.47 180 1.0591 0.7252 0.3642 0.3609 0.1887 0.0861
0.0836 10.53 200 1.0908 0.7185 0.2649 0.4536 0.0960 0.1854
0.0485 11.58 220 1.2155 0.7450 0.3709 0.3742 0.1755 0.0795
0.0298 12.63 240 1.1973 0.7417 0.3245 0.4172 0.1325 0.1258
0.0444 13.68 260 1.4213 0.7384 0.3675 0.3709 0.1788 0.0828
0.0215 14.74 280 1.4907 0.7450 0.3278 0.4172 0.1325 0.1225
0.0483 15.79 300 1.5485 0.7583 0.2781 0.4801 0.0695 0.1722
0.0129 16.84 320 1.7145 0.7550 0.2748 0.4801 0.0695 0.1755
0.0525 17.89 340 1.7827 0.7550 0.3642 0.3907 0.1589 0.0861
0.0074 18.95 360 1.6230 0.7682 0.2980 0.4702 0.0795 0.1523
0.004 20.0 380 1.8522 0.7384 0.3444 0.3940 0.1556 0.1060
0.0067 21.05 400 1.8479 0.7616 0.3046 0.4570 0.0927 0.1457
0.001 22.11 420 1.9830 0.7682 0.2947 0.4735 0.0762 0.1556
0.01 23.16 440 1.9412 0.7715 0.3113 0.4603 0.0894 0.1391
0.0048 24.21 460 2.0075 0.7649 0.3510 0.4139 0.1358 0.0993
0.0025 25.26 480 2.0912 0.7649 0.2980 0.4669 0.0828 0.1523
0.0013 26.32 500 2.1548 0.7715 0.3444 0.4272 0.1225 0.1060
0.0041 27.37 520 2.1337 0.7682 0.3543 0.4139 0.1358 0.0960
0.0005 28.42 540 2.1242 0.7550 0.3576 0.3974 0.1523 0.0927
0.0124 29.47 560 2.1297 0.7583 0.3642 0.3940 0.1556 0.0861
0.0104 30.53 580 2.0057 0.7583 0.3179 0.4404 0.1093 0.1325
0.0156 31.58 600 2.0365 0.7483 0.2881 0.4603 0.0894 0.1623
0.0003 32.63 620 1.9614 0.7649 0.3212 0.4437 0.1060 0.1291
0.0029 33.68 640 1.9658 0.7682 0.3245 0.4437 0.1060 0.1258
0.0001 34.74 660 1.9913 0.7649 0.3013 0.4636 0.0861 0.1490
0.0001 35.79 680 2.0039 0.7649 0.3013 0.4636 0.0861 0.1490
0.0004 36.84 700 1.9657 0.7715 0.3146 0.4570 0.0927 0.1358
0.0003 37.89 720 1.9787 0.7748 0.3245 0.4503 0.0993 0.1258
0.0007 38.95 740 1.9888 0.7781 0.3377 0.4404 0.1093 0.1126
0.0002 40.0 760 2.0293 0.7682 0.3477 0.4205 0.1291 0.1026
0.0002 41.05 780 1.9914 0.7781 0.3245 0.4536 0.0960 0.1258
0.0003 42.11 800 2.0444 0.7583 0.2914 0.4669 0.0828 0.1589
0.0072 43.16 820 2.0247 0.7649 0.3278 0.4371 0.1126 0.1225
0.0001 44.21 840 2.0398 0.7682 0.3278 0.4404 0.1093 0.1225
0.0001 45.26 860 2.0358 0.7682 0.3278 0.4404 0.1093 0.1225
0.0011 46.32 880 2.0432 0.7682 0.3278 0.4404 0.1093 0.1225
0.0001 47.37 900 2.0923 0.7781 0.3444 0.4338 0.1159 0.1060
0.0 48.42 920 2.0975 0.7781 0.3444 0.4338 0.1159 0.1060
0.0002 49.47 940 2.0946 0.7781 0.3444 0.4338 0.1159 0.1060

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1
  • Datasets 2.10.1
  • Tokenizers 0.13.2