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3e-2_10_0.1
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: 0.6258
- Accuracy: 0.5379
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: 0.03
- 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 | 1.7487 | 0.5271 |
3.9657 | 2.0 | 624 | 1.6713 | 0.4729 |
3.9657 | 3.0 | 936 | 1.7039 | 0.4729 |
2.3312 | 4.0 | 1248 | 4.3023 | 0.5271 |
2.1711 | 5.0 | 1560 | 0.8582 | 0.4729 |
2.1711 | 6.0 | 1872 | 0.6298 | 0.4982 |
1.7918 | 7.0 | 2184 | 1.4449 | 0.5271 |
1.7918 | 8.0 | 2496 | 0.6374 | 0.5271 |
1.4918 | 9.0 | 2808 | 1.6588 | 0.4729 |
1.5706 | 10.0 | 3120 | 0.6965 | 0.5090 |
1.5706 | 11.0 | 3432 | 1.0698 | 0.5271 |
1.4388 | 12.0 | 3744 | 0.8561 | 0.4729 |
1.2519 | 13.0 | 4056 | 0.6604 | 0.5271 |
1.2519 | 14.0 | 4368 | 1.1529 | 0.5271 |
1.1804 | 15.0 | 4680 | 0.7657 | 0.4729 |
1.1804 | 16.0 | 4992 | 0.6331 | 0.4838 |
1.1249 | 17.0 | 5304 | 1.2513 | 0.5271 |
1.161 | 18.0 | 5616 | 1.5477 | 0.5271 |
1.161 | 19.0 | 5928 | 0.6309 | 0.5126 |
1.1646 | 20.0 | 6240 | 0.6461 | 0.5235 |
1.0512 | 21.0 | 6552 | 1.0072 | 0.5271 |
1.0512 | 22.0 | 6864 | 0.7228 | 0.5271 |
1.0792 | 23.0 | 7176 | 1.2781 | 0.4729 |
1.0792 | 24.0 | 7488 | 0.8418 | 0.4729 |
1.0817 | 25.0 | 7800 | 1.0903 | 0.5271 |
1.0233 | 26.0 | 8112 | 0.9363 | 0.5271 |
1.0233 | 27.0 | 8424 | 0.8552 | 0.4729 |
0.982 | 28.0 | 8736 | 0.7299 | 0.4729 |
0.926 | 29.0 | 9048 | 0.6380 | 0.4440 |
0.926 | 30.0 | 9360 | 1.5393 | 0.5271 |
0.9613 | 31.0 | 9672 | 0.7258 | 0.4729 |
0.9613 | 32.0 | 9984 | 0.8471 | 0.5271 |
0.8893 | 33.0 | 10296 | 0.6271 | 0.5271 |
0.904 | 34.0 | 10608 | 0.6718 | 0.5271 |
0.904 | 35.0 | 10920 | 0.6358 | 0.4874 |
0.9034 | 36.0 | 11232 | 0.9034 | 0.4729 |
0.887 | 37.0 | 11544 | 0.7764 | 0.5271 |
0.887 | 38.0 | 11856 | 0.6706 | 0.4729 |
0.8477 | 39.0 | 12168 | 0.6326 | 0.5271 |
0.8477 | 40.0 | 12480 | 0.6265 | 0.5054 |
0.8539 | 41.0 | 12792 | 0.6624 | 0.5271 |
0.8147 | 42.0 | 13104 | 0.6563 | 0.5271 |
0.8147 | 43.0 | 13416 | 0.6304 | 0.4729 |
0.8202 | 44.0 | 13728 | 0.6489 | 0.4729 |
0.7907 | 45.0 | 14040 | 0.7081 | 0.5271 |
0.7907 | 46.0 | 14352 | 0.6311 | 0.4368 |
0.7947 | 47.0 | 14664 | 0.6740 | 0.4729 |
0.7947 | 48.0 | 14976 | 0.6262 | 0.5379 |
0.7523 | 49.0 | 15288 | 0.6370 | 0.4729 |
0.7378 | 50.0 | 15600 | 0.6247 | 0.5271 |
0.7378 | 51.0 | 15912 | 0.6253 | 0.5162 |
0.7219 | 52.0 | 16224 | 0.7281 | 0.4729 |
0.7043 | 53.0 | 16536 | 0.6248 | 0.5271 |
0.7043 | 54.0 | 16848 | 0.6247 | 0.5271 |
0.6898 | 55.0 | 17160 | 0.6630 | 0.4729 |
0.6898 | 56.0 | 17472 | 0.6596 | 0.5271 |
0.6822 | 57.0 | 17784 | 0.6302 | 0.5271 |
0.6656 | 58.0 | 18096 | 0.6270 | 0.4910 |
0.6656 | 59.0 | 18408 | 0.6256 | 0.5271 |
0.6559 | 60.0 | 18720 | 0.6258 | 0.5379 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3