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20230823213605
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: 0.6579
- Accuracy: 0.7365
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 | 1.6256 | 0.5307 |
| 0.8748 | 2.0 | 624 | 0.7617 | 0.5523 |
| 0.8748 | 3.0 | 936 | 0.6603 | 0.5271 |
| 0.7596 | 4.0 | 1248 | 0.6103 | 0.6101 |
| 0.7685 | 5.0 | 1560 | 0.9349 | 0.5668 |
| 0.7685 | 6.0 | 1872 | 0.8351 | 0.6101 |
| 0.6585 | 7.0 | 2184 | 0.5995 | 0.6823 |
| 0.6585 | 8.0 | 2496 | 0.5553 | 0.7076 |
| 0.651 | 9.0 | 2808 | 0.5718 | 0.7040 |
| 0.629 | 10.0 | 3120 | 0.5922 | 0.7040 |
| 0.629 | 11.0 | 3432 | 0.5775 | 0.7148 |
| 0.6145 | 12.0 | 3744 | 0.5886 | 0.7292 |
| 0.595 | 13.0 | 4056 | 0.5959 | 0.7076 |
| 0.595 | 14.0 | 4368 | 0.5683 | 0.7040 |
| 0.5501 | 15.0 | 4680 | 0.5633 | 0.7329 |
| 0.5501 | 16.0 | 4992 | 0.6229 | 0.7184 |
| 0.5382 | 17.0 | 5304 | 0.8960 | 0.6643 |
| 0.4987 | 18.0 | 5616 | 0.5098 | 0.7076 |
| 0.4987 | 19.0 | 5928 | 0.6151 | 0.7184 |
| 0.5146 | 20.0 | 6240 | 0.6031 | 0.7329 |
| 0.4536 | 21.0 | 6552 | 0.7180 | 0.7329 |
| 0.4536 | 22.0 | 6864 | 0.7608 | 0.7184 |
| 0.45 | 23.0 | 7176 | 0.7551 | 0.7112 |
| 0.45 | 24.0 | 7488 | 0.7242 | 0.7148 |
| 0.4336 | 25.0 | 7800 | 0.7373 | 0.7292 |
| 0.396 | 26.0 | 8112 | 0.7001 | 0.7220 |
| 0.396 | 27.0 | 8424 | 0.6008 | 0.7365 |
| 0.3851 | 28.0 | 8736 | 0.5931 | 0.7148 |
| 0.3699 | 29.0 | 9048 | 0.6664 | 0.7329 |
| 0.3699 | 30.0 | 9360 | 0.6632 | 0.7473 |
| 0.3451 | 31.0 | 9672 | 0.6476 | 0.7437 |
| 0.3451 | 32.0 | 9984 | 0.5929 | 0.7292 |
| 0.3273 | 33.0 | 10296 | 0.7271 | 0.7292 |
| 0.3025 | 34.0 | 10608 | 0.6819 | 0.7292 |
| 0.3025 | 35.0 | 10920 | 0.5734 | 0.7329 |
| 0.2981 | 36.0 | 11232 | 0.7307 | 0.7256 |
| 0.2829 | 37.0 | 11544 | 0.8025 | 0.7329 |
| 0.2829 | 38.0 | 11856 | 0.5696 | 0.7545 |
| 0.2724 | 39.0 | 12168 | 0.6290 | 0.7401 |
| 0.2724 | 40.0 | 12480 | 0.6417 | 0.7292 |
| 0.2604 | 41.0 | 12792 | 0.5523 | 0.7401 |
| 0.253 | 42.0 | 13104 | 0.7210 | 0.7365 |
| 0.253 | 43.0 | 13416 | 0.6005 | 0.7365 |
| 0.2469 | 44.0 | 13728 | 0.6808 | 0.7473 |
| 0.2492 | 45.0 | 14040 | 0.6506 | 0.7509 |
| 0.2492 | 46.0 | 14352 | 0.6687 | 0.7437 |
| 0.2413 | 47.0 | 14664 | 0.6401 | 0.7329 |
| 0.2413 | 48.0 | 14976 | 0.6588 | 0.7329 |
| 0.2356 | 49.0 | 15288 | 0.6625 | 0.7401 |
| 0.2251 | 50.0 | 15600 | 0.6472 | 0.7292 |
| 0.2251 | 51.0 | 15912 | 0.6800 | 0.7401 |
| 0.2207 | 52.0 | 16224 | 0.6191 | 0.7473 |
| 0.2127 | 53.0 | 16536 | 0.6478 | 0.7365 |
| 0.2127 | 54.0 | 16848 | 0.6509 | 0.7329 |
| 0.2217 | 55.0 | 17160 | 0.6644 | 0.7365 |
| 0.2217 | 56.0 | 17472 | 0.6360 | 0.7365 |
| 0.2094 | 57.0 | 17784 | 0.6509 | 0.7365 |
| 0.2045 | 58.0 | 18096 | 0.6445 | 0.7365 |
| 0.2045 | 59.0 | 18408 | 0.6659 | 0.7365 |
| 0.2072 | 60.0 | 18720 | 0.6579 | 0.7365 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
