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bert-large-uncased_stereoset_classifieronly

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bert
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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.543171114599686
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the stereoset dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6847
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- - Accuracy: 0.5432
 
 
 
 
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  ## Model description
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@@ -57,106 +61,82 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 20
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.8199 | 0.21 | 10 | 0.7324 | 0.5416 |
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- | 0.7127 | 0.43 | 20 | 0.6844 | 0.5369 |
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- | 0.6952 | 0.64 | 30 | 0.6932 | 0.5353 |
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- | 0.6928 | 0.85 | 40 | 0.6842 | 0.5471 |
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- | 0.7036 | 1.06 | 50 | 0.6845 | 0.5440 |
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- | 0.6865 | 1.28 | 60 | 0.6873 | 0.5275 |
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- | 0.6941 | 1.49 | 70 | 0.6857 | 0.5400 |
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- | 0.7049 | 1.7 | 80 | 0.6850 | 0.5447 |
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- | 0.6964 | 1.91 | 90 | 0.6851 | 0.5455 |
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- | 0.6956 | 2.13 | 100 | 0.6857 | 0.5345 |
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- | 0.6967 | 2.34 | 110 | 0.6847 | 0.5440 |
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- | 0.7033 | 2.55 | 120 | 0.6851 | 0.5440 |
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- | 0.6888 | 2.77 | 130 | 0.6847 | 0.5400 |
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- | 0.6925 | 2.98 | 140 | 0.6847 | 0.5440 |
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- | 0.6868 | 3.19 | 150 | 0.6847 | 0.5400 |
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- | 0.7036 | 3.4 | 160 | 0.6844 | 0.5463 |
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- | 0.6945 | 3.62 | 170 | 0.6843 | 0.5440 |
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- | 0.6929 | 3.83 | 180 | 0.6845 | 0.5487 |
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- | 0.6905 | 4.04 | 190 | 0.6846 | 0.5463 |
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- | 0.693 | 4.26 | 200 | 0.6851 | 0.5424 |
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- | 0.6958 | 4.47 | 210 | 0.6855 | 0.5463 |
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- | 0.6973 | 4.68 | 220 | 0.6849 | 0.5455 |
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- | 0.6836 | 4.89 | 230 | 0.6854 | 0.5400 |
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- | 0.6921 | 5.11 | 240 | 0.6878 | 0.5283 |
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- | 0.7023 | 5.32 | 250 | 0.6851 | 0.5440 |
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- | 0.6952 | 5.53 | 260 | 0.6849 | 0.5440 |
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- | 0.705 | 5.74 | 270 | 0.6843 | 0.5471 |
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- | 0.694 | 5.96 | 280 | 0.6846 | 0.5424 |
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- | 0.6932 | 6.17 | 290 | 0.6850 | 0.5447 |
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- | 0.6903 | 6.38 | 300 | 0.6848 | 0.5432 |
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- | 0.6893 | 6.6 | 310 | 0.6844 | 0.5455 |
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- | 0.6934 | 6.81 | 320 | 0.6845 | 0.5495 |
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- | 0.6996 | 7.02 | 330 | 0.6847 | 0.5502 |
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- | 0.6819 | 7.23 | 340 | 0.6848 | 0.5447 |
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- | 0.6927 | 7.45 | 350 | 0.6851 | 0.5432 |
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- | 0.703 | 7.66 | 360 | 0.6849 | 0.5479 |
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- | 0.6922 | 7.87 | 370 | 0.6848 | 0.5463 |
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- | 0.7008 | 8.09 | 380 | 0.6846 | 0.5440 |
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- | 0.7052 | 8.3 | 390 | 0.6844 | 0.5487 |
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- | 0.701 | 8.51 | 400 | 0.6841 | 0.5447 |
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- | 0.7164 | 8.72 | 410 | 0.6851 | 0.5447 |
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- | 0.6947 | 8.94 | 420 | 0.6849 | 0.5424 |
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- | 0.6904 | 9.15 | 430 | 0.6840 | 0.5463 |
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- | 0.6874 | 9.36 | 440 | 0.6842 | 0.5455 |
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- | 0.709 | 9.57 | 450 | 0.6846 | 0.5455 |
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- | 0.7024 | 9.79 | 460 | 0.6845 | 0.5502 |
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- | 0.6916 | 10.0 | 470 | 0.6847 | 0.5440 |
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- | 0.6971 | 10.21 | 480 | 0.6844 | 0.5471 |
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- | 0.6903 | 10.43 | 490 | 0.6845 | 0.5463 |
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- | 0.6923 | 10.64 | 500 | 0.6850 | 0.5440 |
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- | 0.6948 | 10.85 | 510 | 0.6854 | 0.5424 |
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- | 0.6914 | 11.06 | 520 | 0.6862 | 0.5330 |
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- | 0.6915 | 11.28 | 530 | 0.6860 | 0.5353 |
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- | 0.6918 | 11.49 | 540 | 0.6847 | 0.5471 |
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- | 0.6936 | 11.7 | 550 | 0.6850 | 0.5455 |
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- | 0.6993 | 11.91 | 560 | 0.6847 | 0.5447 |
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- | 0.704 | 12.13 | 570 | 0.6852 | 0.5440 |
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- | 0.6934 | 12.34 | 580 | 0.6848 | 0.5455 |
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- | 0.6969 | 12.55 | 590 | 0.6849 | 0.5455 |
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- | 0.695 | 12.77 | 600 | 0.6850 | 0.5495 |
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- | 0.7044 | 12.98 | 610 | 0.6849 | 0.5463 |
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- | 0.7066 | 13.19 | 620 | 0.6863 | 0.5322 |
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- | 0.6799 | 13.4 | 630 | 0.6860 | 0.5338 |
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- | 0.6886 | 13.62 | 640 | 0.6849 | 0.5479 |
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- | 0.697 | 13.83 | 650 | 0.6847 | 0.5432 |
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- | 0.6849 | 14.04 | 660 | 0.6847 | 0.5416 |
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- | 0.7028 | 14.26 | 670 | 0.6847 | 0.5432 |
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- | 0.6992 | 14.47 | 680 | 0.6849 | 0.5471 |
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- | 0.7016 | 14.68 | 690 | 0.6854 | 0.5416 |
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- | 0.6918 | 14.89 | 700 | 0.6846 | 0.5471 |
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- | 0.6899 | 15.11 | 710 | 0.6846 | 0.5440 |
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- | 0.6933 | 15.32 | 720 | 0.6846 | 0.5440 |
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- | 0.6841 | 15.53 | 730 | 0.6846 | 0.5416 |
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- | 0.6891 | 15.74 | 740 | 0.6846 | 0.5424 |
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- | 0.6935 | 15.96 | 750 | 0.6846 | 0.5424 |
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- | 0.6868 | 16.17 | 760 | 0.6847 | 0.5440 |
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- | 0.6973 | 16.38 | 770 | 0.6850 | 0.5471 |
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- | 0.6792 | 16.6 | 780 | 0.6850 | 0.5471 |
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- | 0.6787 | 16.81 | 790 | 0.6849 | 0.5440 |
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- | 0.6976 | 17.02 | 800 | 0.6847 | 0.5463 |
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- | 0.6841 | 17.23 | 810 | 0.6848 | 0.5455 |
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- | 0.6883 | 17.45 | 820 | 0.6848 | 0.5479 |
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- | 0.6899 | 17.66 | 830 | 0.6847 | 0.5432 |
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- | 0.6987 | 17.87 | 840 | 0.6847 | 0.5455 |
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- | 0.6956 | 18.09 | 850 | 0.6847 | 0.5455 |
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- | 0.6843 | 18.3 | 860 | 0.6847 | 0.5455 |
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- | 0.6781 | 18.51 | 870 | 0.6847 | 0.5455 |
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- | 0.6837 | 18.72 | 880 | 0.6847 | 0.5432 |
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- | 0.7108 | 18.94 | 890 | 0.6847 | 0.5432 |
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- | 0.7048 | 19.15 | 900 | 0.6847 | 0.5432 |
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- | 0.6912 | 19.36 | 910 | 0.6847 | 0.5432 |
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- | 0.707 | 19.57 | 920 | 0.6847 | 0.5424 |
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- | 0.697 | 19.79 | 930 | 0.6847 | 0.5424 |
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- | 0.6922 | 20.0 | 940 | 0.6847 | 0.5432 |
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  ### Framework versions
 
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  metrics:
22
  - name: Accuracy
23
  type: accuracy
24
+ value: 0.5478806907378336
25
  ---
26
 
27
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
31
 
32
  This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the stereoset dataset.
33
  It achieves the following results on the evaluation set:
34
+ - Loss: 0.6856
35
+ - Accuracy: 0.5479
36
+ - Tp: 0.3579
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+ - Tn: 0.1900
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+ - Fp: 0.3242
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+ - Fn: 0.1279
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41
  ## Model description
42
 
 
61
  - seed: 42
62
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
63
  - lr_scheduler_type: linear
64
+ - num_epochs: 30
65
 
66
  ### Training results
67
 
68
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
69
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
70
+ | 0.6938 | 0.43 | 20 | 0.6891 | 0.5573 | 0.2630 | 0.2943 | 0.2198 | 0.2229 |
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+ | 0.703 | 0.85 | 40 | 0.6942 | 0.5455 | 0.3901 | 0.1554 | 0.3587 | 0.0958 |
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+ | 0.6929 | 1.28 | 60 | 0.6882 | 0.5510 | 0.3069 | 0.2441 | 0.2700 | 0.1790 |
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+ | 0.7044 | 1.7 | 80 | 0.6895 | 0.5471 | 0.3603 | 0.1868 | 0.3273 | 0.1256 |
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+ | 0.6947 | 2.13 | 100 | 0.6874 | 0.5463 | 0.3006 | 0.2457 | 0.2684 | 0.1852 |
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+ | 0.7115 | 2.55 | 120 | 0.6910 | 0.5479 | 0.3768 | 0.1711 | 0.3430 | 0.1091 |
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+ | 0.7016 | 2.98 | 140 | 0.6879 | 0.5518 | 0.3430 | 0.2088 | 0.3053 | 0.1429 |
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+ | 0.6952 | 3.4 | 160 | 0.6918 | 0.5361 | 0.3980 | 0.1381 | 0.3760 | 0.0879 |
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+ | 0.6959 | 3.83 | 180 | 0.6910 | 0.5463 | 0.3878 | 0.1586 | 0.3556 | 0.0981 |
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+ | 0.6924 | 4.26 | 200 | 0.6906 | 0.5471 | 0.3830 | 0.1641 | 0.3501 | 0.1028 |
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+ | 0.6943 | 4.68 | 220 | 0.6877 | 0.5487 | 0.3195 | 0.2292 | 0.2849 | 0.1664 |
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+ | 0.7052 | 5.11 | 240 | 0.6879 | 0.5644 | 0.2473 | 0.3171 | 0.1970 | 0.2386 |
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+ | 0.6881 | 5.53 | 260 | 0.6889 | 0.5479 | 0.3791 | 0.1688 | 0.3454 | 0.1068 |
83
+ | 0.6971 | 5.96 | 280 | 0.6882 | 0.5463 | 0.3752 | 0.1711 | 0.3430 | 0.1107 |
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+ | 0.6781 | 6.38 | 300 | 0.6930 | 0.5330 | 0.4035 | 0.1295 | 0.3846 | 0.0824 |
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+ | 0.6992 | 6.81 | 320 | 0.6875 | 0.5495 | 0.3579 | 0.1915 | 0.3226 | 0.1279 |
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+ | 0.6954 | 7.23 | 340 | 0.6868 | 0.5557 | 0.3195 | 0.2363 | 0.2779 | 0.1664 |
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+ | 0.6949 | 7.66 | 360 | 0.6877 | 0.5479 | 0.3556 | 0.1923 | 0.3218 | 0.1303 |
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+ | 0.6946 | 8.09 | 380 | 0.6899 | 0.5471 | 0.3878 | 0.1593 | 0.3548 | 0.0981 |
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+ | 0.6877 | 8.51 | 400 | 0.6862 | 0.5542 | 0.3218 | 0.2323 | 0.2818 | 0.1641 |
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+ | 0.6994 | 8.94 | 420 | 0.6890 | 0.5479 | 0.3823 | 0.1656 | 0.3485 | 0.1036 |
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+ | 0.7061 | 9.36 | 440 | 0.6867 | 0.5620 | 0.2347 | 0.3273 | 0.1868 | 0.2512 |
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+ | 0.6945 | 9.79 | 460 | 0.6893 | 0.5479 | 0.3878 | 0.1601 | 0.3540 | 0.0981 |
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+ | 0.7078 | 10.21 | 480 | 0.6908 | 0.5353 | 0.3972 | 0.1381 | 0.3760 | 0.0887 |
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+ | 0.6911 | 10.64 | 500 | 0.6858 | 0.5502 | 0.3108 | 0.2394 | 0.2747 | 0.1750 |
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+ | 0.684 | 11.06 | 520 | 0.6875 | 0.5502 | 0.3768 | 0.1735 | 0.3407 | 0.1091 |
96
+ | 0.6925 | 11.49 | 540 | 0.6906 | 0.5369 | 0.3972 | 0.1397 | 0.3744 | 0.0887 |
97
+ | 0.7104 | 11.91 | 560 | 0.6856 | 0.5597 | 0.2527 | 0.3069 | 0.2072 | 0.2331 |
98
+ | 0.6919 | 12.34 | 580 | 0.6857 | 0.5479 | 0.3391 | 0.2088 | 0.3053 | 0.1468 |
99
+ | 0.6873 | 12.77 | 600 | 0.6903 | 0.5338 | 0.3987 | 0.1350 | 0.3791 | 0.0871 |
100
+ | 0.6915 | 13.19 | 620 | 0.6862 | 0.5471 | 0.3540 | 0.1931 | 0.3210 | 0.1319 |
101
+ | 0.6921 | 13.62 | 640 | 0.6859 | 0.5518 | 0.3485 | 0.2033 | 0.3108 | 0.1374 |
102
+ | 0.7092 | 14.04 | 660 | 0.6888 | 0.5479 | 0.3807 | 0.1672 | 0.3469 | 0.1052 |
103
+ | 0.6874 | 14.47 | 680 | 0.6851 | 0.5518 | 0.3210 | 0.2308 | 0.2834 | 0.1648 |
104
+ | 0.682 | 14.89 | 700 | 0.6877 | 0.5510 | 0.3744 | 0.1766 | 0.3375 | 0.1115 |
105
+ | 0.6953 | 15.32 | 720 | 0.6853 | 0.5526 | 0.3273 | 0.2253 | 0.2889 | 0.1586 |
106
+ | 0.7056 | 15.74 | 740 | 0.6882 | 0.5487 | 0.3885 | 0.1601 | 0.3540 | 0.0973 |
107
+ | 0.6776 | 16.17 | 760 | 0.6875 | 0.5471 | 0.3783 | 0.1688 | 0.3454 | 0.1075 |
108
+ | 0.6862 | 16.6 | 780 | 0.6863 | 0.5510 | 0.3642 | 0.1868 | 0.3273 | 0.1217 |
109
+ | 0.6827 | 17.02 | 800 | 0.6868 | 0.5510 | 0.3705 | 0.1805 | 0.3336 | 0.1154 |
110
+ | 0.7161 | 17.45 | 820 | 0.6878 | 0.5502 | 0.3791 | 0.1711 | 0.3430 | 0.1068 |
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+ | 0.6991 | 17.87 | 840 | 0.6852 | 0.5487 | 0.3359 | 0.2127 | 0.3014 | 0.1499 |
112
+ | 0.6836 | 18.3 | 860 | 0.6876 | 0.5487 | 0.3830 | 0.1656 | 0.3485 | 0.1028 |
113
+ | 0.7023 | 18.72 | 880 | 0.6862 | 0.5487 | 0.3595 | 0.1892 | 0.3250 | 0.1264 |
114
+ | 0.6939 | 19.15 | 900 | 0.6854 | 0.5495 | 0.3485 | 0.2009 | 0.3132 | 0.1374 |
115
+ | 0.6883 | 19.57 | 920 | 0.6860 | 0.5479 | 0.3587 | 0.1892 | 0.3250 | 0.1272 |
116
+ | 0.6872 | 20.0 | 940 | 0.6866 | 0.5518 | 0.3697 | 0.1821 | 0.3320 | 0.1162 |
117
+ | 0.685 | 20.43 | 960 | 0.6861 | 0.5487 | 0.3595 | 0.1892 | 0.3250 | 0.1264 |
118
+ | 0.6771 | 20.85 | 980 | 0.6853 | 0.5510 | 0.3477 | 0.2033 | 0.3108 | 0.1381 |
119
+ | 0.6904 | 21.28 | 1000 | 0.6859 | 0.5487 | 0.3564 | 0.1923 | 0.3218 | 0.1295 |
120
+ | 0.6925 | 21.7 | 1020 | 0.6848 | 0.5518 | 0.3132 | 0.2386 | 0.2755 | 0.1727 |
121
+ | 0.6982 | 22.13 | 1040 | 0.6856 | 0.5463 | 0.3532 | 0.1931 | 0.3210 | 0.1327 |
122
+ | 0.7015 | 22.55 | 1060 | 0.6859 | 0.5479 | 0.3587 | 0.1892 | 0.3250 | 0.1272 |
123
+ | 0.6851 | 22.98 | 1080 | 0.6860 | 0.5518 | 0.3650 | 0.1868 | 0.3273 | 0.1209 |
124
+ | 0.6875 | 23.4 | 1100 | 0.6856 | 0.5463 | 0.3532 | 0.1931 | 0.3210 | 0.1327 |
125
+ | 0.7035 | 23.83 | 1120 | 0.6851 | 0.5510 | 0.3454 | 0.2057 | 0.3085 | 0.1405 |
126
+ | 0.699 | 24.26 | 1140 | 0.6846 | 0.5534 | 0.3281 | 0.2253 | 0.2889 | 0.1578 |
127
+ | 0.6954 | 24.68 | 1160 | 0.6851 | 0.5495 | 0.3485 | 0.2009 | 0.3132 | 0.1374 |
128
+ | 0.6881 | 25.11 | 1180 | 0.6851 | 0.5510 | 0.3485 | 0.2025 | 0.3116 | 0.1374 |
129
+ | 0.6931 | 25.53 | 1200 | 0.6862 | 0.5487 | 0.3666 | 0.1821 | 0.3320 | 0.1193 |
130
+ | 0.6967 | 25.96 | 1220 | 0.6868 | 0.5487 | 0.3752 | 0.1735 | 0.3407 | 0.1107 |
131
+ | 0.6826 | 26.38 | 1240 | 0.6863 | 0.5502 | 0.3689 | 0.1813 | 0.3328 | 0.1170 |
132
+ | 0.6927 | 26.81 | 1260 | 0.6857 | 0.5487 | 0.3587 | 0.1900 | 0.3242 | 0.1272 |
133
+ | 0.692 | 27.23 | 1280 | 0.6853 | 0.5471 | 0.3524 | 0.1947 | 0.3195 | 0.1334 |
134
+ | 0.6936 | 27.66 | 1300 | 0.6856 | 0.5479 | 0.3579 | 0.1900 | 0.3242 | 0.1279 |
135
+ | 0.6871 | 28.09 | 1320 | 0.6856 | 0.5487 | 0.3579 | 0.1907 | 0.3234 | 0.1279 |
136
+ | 0.6956 | 28.51 | 1340 | 0.6857 | 0.5487 | 0.3595 | 0.1892 | 0.3250 | 0.1264 |
137
+ | 0.6788 | 28.94 | 1360 | 0.6859 | 0.5479 | 0.3611 | 0.1868 | 0.3273 | 0.1248 |
138
+ | 0.6933 | 29.36 | 1380 | 0.6856 | 0.5479 | 0.3579 | 0.1900 | 0.3242 | 0.1279 |
139
+ | 0.6909 | 29.79 | 1400 | 0.6856 | 0.5479 | 0.3579 | 0.1900 | 0.3242 | 0.1279 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
140
 
141
 
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  ### Framework versions