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20230824023516
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: 0.7658
- Accuracy: 0.7401
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 | 1.0164 | 0.5307 |
| 0.9117 | 2.0 | 624 | 0.7035 | 0.5090 |
| 0.9117 | 3.0 | 936 | 0.6456 | 0.5307 |
| 0.771 | 4.0 | 1248 | 0.6625 | 0.5487 |
| 0.7935 | 5.0 | 1560 | 0.9135 | 0.5487 |
| 0.7935 | 6.0 | 1872 | 0.7048 | 0.6426 |
| 0.7247 | 7.0 | 2184 | 0.7188 | 0.6570 |
| 0.7247 | 8.0 | 2496 | 0.7428 | 0.6570 |
| 0.6659 | 9.0 | 2808 | 0.5639 | 0.7076 |
| 0.6647 | 10.0 | 3120 | 0.8170 | 0.6426 |
| 0.6647 | 11.0 | 3432 | 0.5627 | 0.7076 |
| 0.6248 | 12.0 | 3744 | 0.7036 | 0.7040 |
| 0.5859 | 13.0 | 4056 | 0.5674 | 0.7112 |
| 0.5859 | 14.0 | 4368 | 0.6351 | 0.7112 |
| 0.599 | 15.0 | 4680 | 0.5921 | 0.7112 |
| 0.599 | 16.0 | 4992 | 0.9538 | 0.6643 |
| 0.5515 | 17.0 | 5304 | 0.6401 | 0.7004 |
| 0.5423 | 18.0 | 5616 | 0.5545 | 0.7256 |
| 0.5423 | 19.0 | 5928 | 0.5583 | 0.7365 |
| 0.5248 | 20.0 | 6240 | 0.8808 | 0.6534 |
| 0.4795 | 21.0 | 6552 | 0.5670 | 0.7292 |
| 0.4795 | 22.0 | 6864 | 0.6174 | 0.6968 |
| 0.4853 | 23.0 | 7176 | 0.8153 | 0.7112 |
| 0.4853 | 24.0 | 7488 | 0.6551 | 0.7256 |
| 0.4379 | 25.0 | 7800 | 0.7501 | 0.7292 |
| 0.4365 | 26.0 | 8112 | 0.8488 | 0.6895 |
| 0.4365 | 27.0 | 8424 | 0.7814 | 0.7112 |
| 0.4204 | 28.0 | 8736 | 0.7393 | 0.7220 |
| 0.434 | 29.0 | 9048 | 0.9116 | 0.6859 |
| 0.434 | 30.0 | 9360 | 0.8298 | 0.7076 |
| 0.4064 | 31.0 | 9672 | 0.7928 | 0.6968 |
| 0.4064 | 32.0 | 9984 | 0.6150 | 0.7329 |
| 0.3869 | 33.0 | 10296 | 0.8984 | 0.7256 |
| 0.3459 | 34.0 | 10608 | 0.6598 | 0.7401 |
| 0.3459 | 35.0 | 10920 | 0.6022 | 0.7401 |
| 0.352 | 36.0 | 11232 | 0.8833 | 0.7112 |
| 0.3268 | 37.0 | 11544 | 0.9331 | 0.7220 |
| 0.3268 | 38.0 | 11856 | 0.8233 | 0.7401 |
| 0.3108 | 39.0 | 12168 | 0.8361 | 0.7329 |
| 0.3108 | 40.0 | 12480 | 0.6123 | 0.7292 |
| 0.3038 | 41.0 | 12792 | 0.6187 | 0.7292 |
| 0.287 | 42.0 | 13104 | 0.7216 | 0.7401 |
| 0.287 | 43.0 | 13416 | 0.9118 | 0.7148 |
| 0.2802 | 44.0 | 13728 | 0.8249 | 0.7329 |
| 0.2756 | 45.0 | 14040 | 0.7843 | 0.7437 |
| 0.2756 | 46.0 | 14352 | 0.7272 | 0.7365 |
| 0.2735 | 47.0 | 14664 | 0.7253 | 0.7292 |
| 0.2735 | 48.0 | 14976 | 0.7766 | 0.7365 |
| 0.2552 | 49.0 | 15288 | 0.7906 | 0.7401 |
| 0.2449 | 50.0 | 15600 | 0.6664 | 0.7329 |
| 0.2449 | 51.0 | 15912 | 0.6854 | 0.7220 |
| 0.248 | 52.0 | 16224 | 0.7260 | 0.7256 |
| 0.2533 | 53.0 | 16536 | 0.7750 | 0.7329 |
| 0.2533 | 54.0 | 16848 | 0.7146 | 0.7401 |
| 0.238 | 55.0 | 17160 | 0.7802 | 0.7365 |
| 0.238 | 56.0 | 17472 | 0.7462 | 0.7365 |
| 0.2412 | 57.0 | 17784 | 0.7619 | 0.7473 |
| 0.2241 | 58.0 | 18096 | 0.6815 | 0.7437 |
| 0.2241 | 59.0 | 18408 | 0.7661 | 0.7401 |
| 0.2293 | 60.0 | 18720 | 0.7658 | 0.7401 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
