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20230825183835
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: 0.3648
- Accuracy: 0.7473
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: 0.005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 156 | 0.8052 | 0.5307 |
No log | 2.0 | 312 | 0.6957 | 0.4801 |
No log | 3.0 | 468 | 0.9722 | 0.4801 |
0.8916 | 4.0 | 624 | 0.7219 | 0.5560 |
0.8916 | 5.0 | 780 | 0.5572 | 0.5921 |
0.8916 | 6.0 | 936 | 0.4803 | 0.6534 |
0.8141 | 7.0 | 1092 | 0.6885 | 0.6318 |
0.8141 | 8.0 | 1248 | 0.4588 | 0.6895 |
0.8141 | 9.0 | 1404 | 1.0159 | 0.4729 |
0.7176 | 10.0 | 1560 | 0.4835 | 0.6823 |
0.7176 | 11.0 | 1716 | 0.5513 | 0.6823 |
0.7176 | 12.0 | 1872 | 0.4150 | 0.7184 |
0.6445 | 13.0 | 2028 | 0.4789 | 0.7148 |
0.6445 | 14.0 | 2184 | 0.4414 | 0.7220 |
0.6445 | 15.0 | 2340 | 0.3778 | 0.6968 |
0.6445 | 16.0 | 2496 | 0.5422 | 0.6823 |
0.6267 | 17.0 | 2652 | 0.3654 | 0.7220 |
0.6267 | 18.0 | 2808 | 0.7434 | 0.6390 |
0.6267 | 19.0 | 2964 | 0.3713 | 0.7112 |
0.5715 | 20.0 | 3120 | 0.3942 | 0.6931 |
0.5715 | 21.0 | 3276 | 0.3785 | 0.7112 |
0.5715 | 22.0 | 3432 | 0.5429 | 0.6570 |
0.5015 | 23.0 | 3588 | 0.3600 | 0.7365 |
0.5015 | 24.0 | 3744 | 0.4567 | 0.7473 |
0.5015 | 25.0 | 3900 | 0.3680 | 0.7148 |
0.4739 | 26.0 | 4056 | 0.3348 | 0.7292 |
0.4739 | 27.0 | 4212 | 0.4191 | 0.7437 |
0.4739 | 28.0 | 4368 | 0.4034 | 0.7401 |
0.4139 | 29.0 | 4524 | 0.3887 | 0.7112 |
0.4139 | 30.0 | 4680 | 0.4222 | 0.7004 |
0.4139 | 31.0 | 4836 | 0.3804 | 0.7220 |
0.4139 | 32.0 | 4992 | 0.3842 | 0.7256 |
0.3958 | 33.0 | 5148 | 0.3851 | 0.7365 |
0.3958 | 34.0 | 5304 | 0.4758 | 0.7040 |
0.3958 | 35.0 | 5460 | 0.3569 | 0.7473 |
0.3561 | 36.0 | 5616 | 0.3971 | 0.7256 |
0.3561 | 37.0 | 5772 | 0.4006 | 0.7545 |
0.3561 | 38.0 | 5928 | 0.5292 | 0.7220 |
0.3349 | 39.0 | 6084 | 0.4014 | 0.7329 |
0.3349 | 40.0 | 6240 | 0.3285 | 0.7473 |
0.3349 | 41.0 | 6396 | 0.3665 | 0.7581 |
0.2946 | 42.0 | 6552 | 0.3843 | 0.7690 |
0.2946 | 43.0 | 6708 | 0.3634 | 0.7509 |
0.2946 | 44.0 | 6864 | 0.3518 | 0.7437 |
0.2813 | 45.0 | 7020 | 0.4009 | 0.7473 |
0.2813 | 46.0 | 7176 | 0.4073 | 0.7653 |
0.2813 | 47.0 | 7332 | 0.3974 | 0.7473 |
0.2813 | 48.0 | 7488 | 0.4134 | 0.7437 |
0.2601 | 49.0 | 7644 | 0.3661 | 0.7437 |
0.2601 | 50.0 | 7800 | 0.3733 | 0.7437 |
0.2601 | 51.0 | 7956 | 0.3425 | 0.7509 |
0.242 | 52.0 | 8112 | 0.4186 | 0.7473 |
0.242 | 53.0 | 8268 | 0.4262 | 0.7401 |
0.242 | 54.0 | 8424 | 0.3627 | 0.7437 |
0.2356 | 55.0 | 8580 | 0.3966 | 0.7473 |
0.2356 | 56.0 | 8736 | 0.3819 | 0.7509 |
0.2356 | 57.0 | 8892 | 0.4087 | 0.7473 |
0.2198 | 58.0 | 9048 | 0.3691 | 0.7365 |
0.2198 | 59.0 | 9204 | 0.4938 | 0.7437 |
0.2198 | 60.0 | 9360 | 0.4097 | 0.7581 |
0.1995 | 61.0 | 9516 | 0.3870 | 0.7509 |
0.1995 | 62.0 | 9672 | 0.4417 | 0.7473 |
0.1995 | 63.0 | 9828 | 0.3596 | 0.7509 |
0.1995 | 64.0 | 9984 | 0.3483 | 0.7473 |
0.1933 | 65.0 | 10140 | 0.4424 | 0.7545 |
0.1933 | 66.0 | 10296 | 0.3443 | 0.7437 |
0.1933 | 67.0 | 10452 | 0.3820 | 0.7437 |
0.1898 | 68.0 | 10608 | 0.3889 | 0.7473 |
0.1898 | 69.0 | 10764 | 0.3841 | 0.7437 |
0.1898 | 70.0 | 10920 | 0.4081 | 0.7581 |
0.1813 | 71.0 | 11076 | 0.3680 | 0.7473 |
0.1813 | 72.0 | 11232 | 0.3775 | 0.7473 |
0.1813 | 73.0 | 11388 | 0.3713 | 0.7473 |
0.1688 | 74.0 | 11544 | 0.3765 | 0.7473 |
0.1688 | 75.0 | 11700 | 0.3580 | 0.7509 |
0.1688 | 76.0 | 11856 | 0.3485 | 0.7437 |
0.1663 | 77.0 | 12012 | 0.3601 | 0.7509 |
0.1663 | 78.0 | 12168 | 0.3721 | 0.7509 |
0.1663 | 79.0 | 12324 | 0.3633 | 0.7473 |
0.1663 | 80.0 | 12480 | 0.3648 | 0.7473 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3