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20230823213639
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: 1.3551
- Accuracy: 0.7545
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 | 1.1031 | 0.5307 |
| 0.9187 | 2.0 | 624 | 0.7935 | 0.4874 |
| 0.9187 | 3.0 | 936 | 0.7082 | 0.5704 |
| 0.8508 | 4.0 | 1248 | 0.6713 | 0.6065 |
| 0.8272 | 5.0 | 1560 | 0.6997 | 0.6390 |
| 0.8272 | 6.0 | 1872 | 0.8815 | 0.6426 |
| 0.722 | 7.0 | 2184 | 1.0092 | 0.6318 |
| 0.722 | 8.0 | 2496 | 0.7370 | 0.6751 |
| 0.7377 | 9.0 | 2808 | 0.6362 | 0.7076 |
| 0.6952 | 10.0 | 3120 | 0.9842 | 0.6570 |
| 0.6952 | 11.0 | 3432 | 0.7133 | 0.7040 |
| 0.672 | 12.0 | 3744 | 0.7288 | 0.6823 |
| 0.6344 | 13.0 | 4056 | 0.7260 | 0.7220 |
| 0.6344 | 14.0 | 4368 | 0.6437 | 0.7112 |
| 0.6039 | 15.0 | 4680 | 0.7529 | 0.7184 |
| 0.6039 | 16.0 | 4992 | 1.0284 | 0.6787 |
| 0.5952 | 17.0 | 5304 | 0.8757 | 0.7256 |
| 0.5371 | 18.0 | 5616 | 0.6932 | 0.7329 |
| 0.5371 | 19.0 | 5928 | 0.7127 | 0.7148 |
| 0.5411 | 20.0 | 6240 | 1.0835 | 0.6823 |
| 0.4985 | 21.0 | 6552 | 0.9109 | 0.7292 |
| 0.4985 | 22.0 | 6864 | 1.4054 | 0.6643 |
| 0.4897 | 23.0 | 7176 | 1.0748 | 0.7112 |
| 0.4897 | 24.0 | 7488 | 1.1041 | 0.7256 |
| 0.4498 | 25.0 | 7800 | 1.0205 | 0.7040 |
| 0.4208 | 26.0 | 8112 | 1.0637 | 0.7148 |
| 0.4208 | 27.0 | 8424 | 0.8231 | 0.7329 |
| 0.4024 | 28.0 | 8736 | 0.7506 | 0.7401 |
| 0.4083 | 29.0 | 9048 | 1.1923 | 0.7184 |
| 0.4083 | 30.0 | 9360 | 1.2166 | 0.7184 |
| 0.3497 | 31.0 | 9672 | 1.2273 | 0.7220 |
| 0.3497 | 32.0 | 9984 | 0.9219 | 0.7437 |
| 0.3188 | 33.0 | 10296 | 1.1009 | 0.7401 |
| 0.2923 | 34.0 | 10608 | 0.8986 | 0.7545 |
| 0.2923 | 35.0 | 10920 | 1.2732 | 0.7509 |
| 0.2876 | 36.0 | 11232 | 1.0246 | 0.7437 |
| 0.2751 | 37.0 | 11544 | 1.0842 | 0.7545 |
| 0.2751 | 38.0 | 11856 | 1.3797 | 0.7401 |
| 0.2807 | 39.0 | 12168 | 1.2845 | 0.7401 |
| 0.2807 | 40.0 | 12480 | 1.0588 | 0.7473 |
| 0.2524 | 41.0 | 12792 | 1.3290 | 0.7365 |
| 0.2353 | 42.0 | 13104 | 1.1838 | 0.7509 |
| 0.2353 | 43.0 | 13416 | 1.6934 | 0.7292 |
| 0.2221 | 44.0 | 13728 | 1.4884 | 0.7437 |
| 0.222 | 45.0 | 14040 | 1.4472 | 0.7292 |
| 0.222 | 46.0 | 14352 | 1.6685 | 0.7365 |
| 0.2124 | 47.0 | 14664 | 1.2194 | 0.7545 |
| 0.2124 | 48.0 | 14976 | 1.4803 | 0.7437 |
| 0.1923 | 49.0 | 15288 | 1.3954 | 0.7509 |
| 0.1717 | 50.0 | 15600 | 1.4008 | 0.7401 |
| 0.1717 | 51.0 | 15912 | 1.2478 | 0.7545 |
| 0.1775 | 52.0 | 16224 | 1.2562 | 0.7545 |
| 0.1599 | 53.0 | 16536 | 1.4865 | 0.7545 |
| 0.1599 | 54.0 | 16848 | 1.3985 | 0.7473 |
| 0.1518 | 55.0 | 17160 | 1.3492 | 0.7437 |
| 0.1518 | 56.0 | 17472 | 1.3659 | 0.7437 |
| 0.1481 | 57.0 | 17784 | 1.2743 | 0.7545 |
| 0.1461 | 58.0 | 18096 | 1.3666 | 0.7509 |
| 0.1461 | 59.0 | 18408 | 1.3473 | 0.7509 |
| 0.1449 | 60.0 | 18720 | 1.3551 | 0.7545 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
