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20230824104100
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: 0.0729
- Accuracy: 0.7473
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: 8
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 312 | 0.2294 | 0.5307 |
| 0.3686 | 2.0 | 624 | 0.5346 | 0.4729 |
| 0.3686 | 3.0 | 936 | 0.2223 | 0.5235 |
| 0.2907 | 4.0 | 1248 | 0.1895 | 0.4729 |
| 0.2686 | 5.0 | 1560 | 0.1783 | 0.5018 |
| 0.2686 | 6.0 | 1872 | 0.1995 | 0.5884 |
| 0.2686 | 7.0 | 2184 | 0.3037 | 0.5740 |
| 0.2686 | 8.0 | 2496 | 0.1386 | 0.6715 |
| 0.266 | 9.0 | 2808 | 0.1311 | 0.7076 |
| 0.2363 | 10.0 | 3120 | 0.1403 | 0.6968 |
| 0.2363 | 11.0 | 3432 | 0.2988 | 0.5957 |
| 0.215 | 12.0 | 3744 | 0.1119 | 0.6968 |
| 0.198 | 13.0 | 4056 | 0.1238 | 0.6859 |
| 0.198 | 14.0 | 4368 | 0.1107 | 0.7040 |
| 0.1845 | 15.0 | 4680 | 0.1604 | 0.6570 |
| 0.1845 | 16.0 | 4992 | 0.1143 | 0.7004 |
| 0.1664 | 17.0 | 5304 | 0.1197 | 0.7148 |
| 0.159 | 18.0 | 5616 | 0.1122 | 0.7329 |
| 0.159 | 19.0 | 5928 | 0.1038 | 0.7184 |
| 0.145 | 20.0 | 6240 | 0.0973 | 0.7040 |
| 0.1304 | 21.0 | 6552 | 0.0996 | 0.7292 |
| 0.1304 | 22.0 | 6864 | 0.0938 | 0.7473 |
| 0.1264 | 23.0 | 7176 | 0.1212 | 0.7437 |
| 0.1264 | 24.0 | 7488 | 0.0953 | 0.7256 |
| 0.1212 | 25.0 | 7800 | 0.0899 | 0.7329 |
| 0.1172 | 26.0 | 8112 | 0.1037 | 0.7365 |
| 0.1172 | 27.0 | 8424 | 0.0844 | 0.7292 |
| 0.1122 | 28.0 | 8736 | 0.0850 | 0.7365 |
| 0.1131 | 29.0 | 9048 | 0.0875 | 0.7220 |
| 0.1131 | 30.0 | 9360 | 0.0904 | 0.7437 |
| 0.1082 | 31.0 | 9672 | 0.0883 | 0.7184 |
| 0.1082 | 32.0 | 9984 | 0.0800 | 0.7509 |
| 0.1086 | 33.0 | 10296 | 0.0897 | 0.7509 |
| 0.1015 | 34.0 | 10608 | 0.0837 | 0.7473 |
| 0.1015 | 35.0 | 10920 | 0.0820 | 0.7329 |
| 0.099 | 36.0 | 11232 | 0.0819 | 0.7365 |
| 0.0942 | 37.0 | 11544 | 0.0858 | 0.7509 |
| 0.0942 | 38.0 | 11856 | 0.0793 | 0.7437 |
| 0.0956 | 39.0 | 12168 | 0.0823 | 0.7581 |
| 0.0956 | 40.0 | 12480 | 0.0860 | 0.7256 |
| 0.0921 | 41.0 | 12792 | 0.0753 | 0.7545 |
| 0.0911 | 42.0 | 13104 | 0.0838 | 0.7473 |
| 0.0911 | 43.0 | 13416 | 0.0763 | 0.7545 |
| 0.0894 | 44.0 | 13728 | 0.0761 | 0.7473 |
| 0.0886 | 45.0 | 14040 | 0.0752 | 0.7581 |
| 0.0886 | 46.0 | 14352 | 0.0743 | 0.7437 |
| 0.0855 | 47.0 | 14664 | 0.0759 | 0.7581 |
| 0.0855 | 48.0 | 14976 | 0.0801 | 0.7437 |
| 0.0837 | 49.0 | 15288 | 0.0797 | 0.7473 |
| 0.083 | 50.0 | 15600 | 0.0734 | 0.7509 |
| 0.083 | 51.0 | 15912 | 0.0756 | 0.7545 |
| 0.0845 | 52.0 | 16224 | 0.0744 | 0.7401 |
| 0.084 | 53.0 | 16536 | 0.0731 | 0.7545 |
| 0.084 | 54.0 | 16848 | 0.0736 | 0.7473 |
| 0.0797 | 55.0 | 17160 | 0.0734 | 0.7653 |
| 0.0797 | 56.0 | 17472 | 0.0735 | 0.7545 |
| 0.0803 | 57.0 | 17784 | 0.0737 | 0.7545 |
| 0.0792 | 58.0 | 18096 | 0.0735 | 0.7581 |
| 0.0792 | 59.0 | 18408 | 0.0732 | 0.7581 |
| 0.0815 | 60.0 | 18720 | 0.0729 | 0.7473 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
