20230824024310
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: 0.3005
- Accuracy: 0.7509
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 | 0.6323 | 0.5307 |
0.5669 | 2.0 | 624 | 0.4749 | 0.5415 |
0.5669 | 3.0 | 936 | 0.4812 | 0.5271 |
0.5542 | 4.0 | 1248 | 0.3917 | 0.5704 |
0.5146 | 5.0 | 1560 | 0.4706 | 0.5523 |
0.5146 | 6.0 | 1872 | 0.4418 | 0.6173 |
0.464 | 7.0 | 2184 | 0.3863 | 0.6462 |
0.464 | 8.0 | 2496 | 0.3326 | 0.6751 |
0.4357 | 9.0 | 2808 | 0.3896 | 0.6065 |
0.4268 | 10.0 | 3120 | 0.3329 | 0.6823 |
0.4268 | 11.0 | 3432 | 0.4012 | 0.6679 |
0.4077 | 12.0 | 3744 | 0.3661 | 0.7112 |
0.3832 | 13.0 | 4056 | 0.3640 | 0.7112 |
0.3832 | 14.0 | 4368 | 0.3328 | 0.7040 |
0.3918 | 15.0 | 4680 | 0.3398 | 0.7076 |
0.3918 | 16.0 | 4992 | 0.6806 | 0.6282 |
0.3741 | 17.0 | 5304 | 0.4620 | 0.6498 |
0.3627 | 18.0 | 5616 | 0.3085 | 0.7473 |
0.3627 | 19.0 | 5928 | 0.3018 | 0.7256 |
0.3392 | 20.0 | 6240 | 0.3790 | 0.6534 |
0.3074 | 21.0 | 6552 | 0.2964 | 0.7401 |
0.3074 | 22.0 | 6864 | 0.3124 | 0.7401 |
0.3076 | 23.0 | 7176 | 0.3907 | 0.6931 |
0.3076 | 24.0 | 7488 | 0.3046 | 0.7329 |
0.2868 | 25.0 | 7800 | 0.3494 | 0.7365 |
0.2757 | 26.0 | 8112 | 0.3811 | 0.7148 |
0.2757 | 27.0 | 8424 | 0.3061 | 0.7509 |
0.2688 | 28.0 | 8736 | 0.2989 | 0.7401 |
0.2638 | 29.0 | 9048 | 0.3090 | 0.7365 |
0.2638 | 30.0 | 9360 | 0.3295 | 0.7365 |
0.2554 | 31.0 | 9672 | 0.3185 | 0.7401 |
0.2554 | 32.0 | 9984 | 0.2872 | 0.7401 |
0.2538 | 33.0 | 10296 | 0.3178 | 0.7509 |
0.2404 | 34.0 | 10608 | 0.2920 | 0.7473 |
0.2404 | 35.0 | 10920 | 0.3001 | 0.7329 |
0.2342 | 36.0 | 11232 | 0.3155 | 0.7437 |
0.2258 | 37.0 | 11544 | 0.3324 | 0.7437 |
0.2258 | 38.0 | 11856 | 0.3179 | 0.7437 |
0.2247 | 39.0 | 12168 | 0.3276 | 0.7509 |
0.2247 | 40.0 | 12480 | 0.2988 | 0.7401 |
0.2184 | 41.0 | 12792 | 0.2916 | 0.7329 |
0.215 | 42.0 | 13104 | 0.3033 | 0.7401 |
0.215 | 43.0 | 13416 | 0.3209 | 0.7473 |
0.2117 | 44.0 | 13728 | 0.2994 | 0.7473 |
0.2035 | 45.0 | 14040 | 0.3093 | 0.7473 |
0.2035 | 46.0 | 14352 | 0.2984 | 0.7365 |
0.203 | 47.0 | 14664 | 0.2866 | 0.7401 |
0.203 | 48.0 | 14976 | 0.3140 | 0.7473 |
0.2019 | 49.0 | 15288 | 0.3158 | 0.7509 |
0.1937 | 50.0 | 15600 | 0.2996 | 0.7545 |
0.1937 | 51.0 | 15912 | 0.2814 | 0.7473 |
0.1988 | 52.0 | 16224 | 0.3050 | 0.7437 |
0.1965 | 53.0 | 16536 | 0.3073 | 0.7473 |
0.1965 | 54.0 | 16848 | 0.2994 | 0.7509 |
0.1918 | 55.0 | 17160 | 0.2985 | 0.7509 |
0.1918 | 56.0 | 17472 | 0.3046 | 0.7509 |
0.1902 | 57.0 | 17784 | 0.2991 | 0.7473 |
0.1879 | 58.0 | 18096 | 0.2942 | 0.7509 |
0.1879 | 59.0 | 18408 | 0.2976 | 0.7509 |
0.194 | 60.0 | 18720 | 0.3005 | 0.7509 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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