20230824002458
This model is a fine-tuned version of bert-large-cased on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.0768
- Accuracy: 0.7112
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: 0.003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.4042 | 1.0 | 623 | 0.3862 | 0.5271 |
0.3203 | 2.0 | 1246 | 1.0958 | 0.4729 |
0.3087 | 3.0 | 1869 | 0.5979 | 0.4729 |
0.2723 | 4.0 | 2492 | 0.1618 | 0.5271 |
0.2635 | 5.0 | 3115 | 0.2704 | 0.5343 |
0.2826 | 6.0 | 3738 | 0.3245 | 0.4729 |
0.2663 | 7.0 | 4361 | 0.2230 | 0.5957 |
0.2562 | 8.0 | 4984 | 0.1453 | 0.6390 |
0.2259 | 9.0 | 5607 | 0.1312 | 0.6282 |
0.1806 | 10.0 | 6230 | 0.1118 | 0.7148 |
0.1525 | 11.0 | 6853 | 0.1076 | 0.6787 |
0.1509 | 12.0 | 7476 | 0.1241 | 0.6643 |
0.149 | 13.0 | 8099 | 0.1158 | 0.6931 |
0.1509 | 14.0 | 8722 | 0.1154 | 0.7040 |
0.1397 | 15.0 | 9345 | 0.1096 | 0.6823 |
0.1311 | 16.0 | 9968 | 0.0999 | 0.6751 |
0.13 | 17.0 | 10591 | 0.0986 | 0.6968 |
0.1244 | 18.0 | 11214 | 0.1063 | 0.6895 |
0.1278 | 19.0 | 11837 | 0.1229 | 0.6931 |
0.1228 | 20.0 | 12460 | 0.0905 | 0.7112 |
0.1153 | 21.0 | 13083 | 0.0916 | 0.7004 |
0.1171 | 22.0 | 13706 | 0.1085 | 0.7148 |
0.1179 | 23.0 | 14329 | 0.1101 | 0.7256 |
0.1069 | 24.0 | 14952 | 0.0917 | 0.6895 |
0.1019 | 25.0 | 15575 | 0.0837 | 0.7112 |
0.1017 | 26.0 | 16198 | 0.0832 | 0.7148 |
0.1034 | 27.0 | 16821 | 0.0847 | 0.7220 |
0.0989 | 28.0 | 17444 | 0.0830 | 0.7256 |
0.0969 | 29.0 | 18067 | 0.0817 | 0.7148 |
0.0964 | 30.0 | 18690 | 0.0835 | 0.7112 |
0.0957 | 31.0 | 19313 | 0.0846 | 0.7148 |
0.0937 | 32.0 | 19936 | 0.0827 | 0.7112 |
0.0895 | 33.0 | 20559 | 0.0860 | 0.7220 |
0.0905 | 34.0 | 21182 | 0.0830 | 0.7220 |
0.0875 | 35.0 | 21805 | 0.0796 | 0.7184 |
0.0895 | 36.0 | 22428 | 0.0811 | 0.7076 |
0.0861 | 37.0 | 23051 | 0.0805 | 0.7112 |
0.0868 | 38.0 | 23674 | 0.0786 | 0.7040 |
0.0798 | 39.0 | 24297 | 0.0787 | 0.7148 |
0.0827 | 40.0 | 24920 | 0.0815 | 0.7112 |
0.0798 | 41.0 | 25543 | 0.0790 | 0.7184 |
0.079 | 42.0 | 26166 | 0.0813 | 0.7220 |
0.0794 | 43.0 | 26789 | 0.0802 | 0.7112 |
0.0766 | 44.0 | 27412 | 0.0796 | 0.7076 |
0.0766 | 45.0 | 28035 | 0.0813 | 0.7329 |
0.0765 | 46.0 | 28658 | 0.0810 | 0.7112 |
0.0744 | 47.0 | 29281 | 0.0781 | 0.7148 |
0.076 | 48.0 | 29904 | 0.0794 | 0.7148 |
0.0728 | 49.0 | 30527 | 0.0780 | 0.7112 |
0.0745 | 50.0 | 31150 | 0.0767 | 0.7256 |
0.0711 | 51.0 | 31773 | 0.0771 | 0.7220 |
0.0726 | 52.0 | 32396 | 0.0772 | 0.7256 |
0.0747 | 53.0 | 33019 | 0.0772 | 0.7184 |
0.0711 | 54.0 | 33642 | 0.0772 | 0.7256 |
0.0676 | 55.0 | 34265 | 0.0767 | 0.7329 |
0.0697 | 56.0 | 34888 | 0.0783 | 0.7220 |
0.0692 | 57.0 | 35511 | 0.0766 | 0.7184 |
0.067 | 58.0 | 36134 | 0.0773 | 0.7148 |
0.0676 | 59.0 | 36757 | 0.0774 | 0.7112 |
0.0678 | 60.0 | 37380 | 0.0768 | 0.7112 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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