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20230825183837
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: 0.5509
- 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: 0.005
- 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 | 1.0350 | 0.5307 |
No log | 2.0 | 312 | 0.7083 | 0.5199 |
No log | 3.0 | 468 | 0.8268 | 0.4801 |
0.9653 | 4.0 | 624 | 0.7385 | 0.5199 |
0.9653 | 5.0 | 780 | 0.6701 | 0.5271 |
0.9653 | 6.0 | 936 | 0.6090 | 0.6029 |
0.8296 | 7.0 | 1092 | 0.5400 | 0.6282 |
0.8296 | 8.0 | 1248 | 0.5084 | 0.6715 |
0.8296 | 9.0 | 1404 | 0.5534 | 0.6606 |
0.7744 | 10.0 | 1560 | 0.4802 | 0.6895 |
0.7744 | 11.0 | 1716 | 0.5757 | 0.6715 |
0.7744 | 12.0 | 1872 | 0.5599 | 0.6787 |
0.6735 | 13.0 | 2028 | 0.4614 | 0.7220 |
0.6735 | 14.0 | 2184 | 0.4656 | 0.7004 |
0.6735 | 15.0 | 2340 | 0.5463 | 0.6859 |
0.6735 | 16.0 | 2496 | 0.5148 | 0.6968 |
0.642 | 17.0 | 2652 | 0.4414 | 0.7292 |
0.642 | 18.0 | 2808 | 0.6131 | 0.6931 |
0.642 | 19.0 | 2964 | 0.4674 | 0.7184 |
0.6495 | 20.0 | 3120 | 0.5114 | 0.7004 |
0.6495 | 21.0 | 3276 | 0.4827 | 0.7365 |
0.6495 | 22.0 | 3432 | 0.7846 | 0.6245 |
0.5629 | 23.0 | 3588 | 0.4956 | 0.7148 |
0.5629 | 24.0 | 3744 | 0.4705 | 0.7617 |
0.5629 | 25.0 | 3900 | 0.4782 | 0.7220 |
0.5208 | 26.0 | 4056 | 0.4177 | 0.7365 |
0.5208 | 27.0 | 4212 | 0.6597 | 0.6931 |
0.5208 | 28.0 | 4368 | 0.5945 | 0.6931 |
0.5051 | 29.0 | 4524 | 0.5733 | 0.7184 |
0.5051 | 30.0 | 4680 | 0.4994 | 0.7437 |
0.5051 | 31.0 | 4836 | 0.5630 | 0.6895 |
0.5051 | 32.0 | 4992 | 0.5061 | 0.7437 |
0.4822 | 33.0 | 5148 | 0.5961 | 0.6968 |
0.4822 | 34.0 | 5304 | 0.5072 | 0.7329 |
0.4822 | 35.0 | 5460 | 0.5716 | 0.7473 |
0.4437 | 36.0 | 5616 | 0.5670 | 0.7076 |
0.4437 | 37.0 | 5772 | 0.5414 | 0.7112 |
0.4437 | 38.0 | 5928 | 0.5748 | 0.6931 |
0.436 | 39.0 | 6084 | 0.5068 | 0.7545 |
0.436 | 40.0 | 6240 | 0.5532 | 0.7076 |
0.436 | 41.0 | 6396 | 0.5705 | 0.7545 |
0.3882 | 42.0 | 6552 | 0.5622 | 0.7545 |
0.3882 | 43.0 | 6708 | 0.5511 | 0.7112 |
0.3882 | 44.0 | 6864 | 0.5306 | 0.7473 |
0.3639 | 45.0 | 7020 | 0.5418 | 0.7148 |
0.3639 | 46.0 | 7176 | 0.5856 | 0.7256 |
0.3639 | 47.0 | 7332 | 0.5920 | 0.7581 |
0.3639 | 48.0 | 7488 | 0.6323 | 0.7112 |
0.3344 | 49.0 | 7644 | 0.5837 | 0.7256 |
0.3344 | 50.0 | 7800 | 0.5591 | 0.7329 |
0.3344 | 51.0 | 7956 | 0.6241 | 0.7401 |
0.3131 | 52.0 | 8112 | 0.5855 | 0.7365 |
0.3131 | 53.0 | 8268 | 0.5593 | 0.7401 |
0.3131 | 54.0 | 8424 | 0.5920 | 0.7401 |
0.319 | 55.0 | 8580 | 0.5000 | 0.7401 |
0.319 | 56.0 | 8736 | 0.6601 | 0.7004 |
0.319 | 57.0 | 8892 | 0.7536 | 0.7076 |
0.2995 | 58.0 | 9048 | 0.5308 | 0.7256 |
0.2995 | 59.0 | 9204 | 0.7136 | 0.7365 |
0.2995 | 60.0 | 9360 | 0.5192 | 0.7581 |
0.2865 | 61.0 | 9516 | 0.5491 | 0.7365 |
0.2865 | 62.0 | 9672 | 0.5884 | 0.7292 |
0.2865 | 63.0 | 9828 | 0.5730 | 0.7329 |
0.2865 | 64.0 | 9984 | 0.5539 | 0.7365 |
0.2779 | 65.0 | 10140 | 0.5626 | 0.7401 |
0.2779 | 66.0 | 10296 | 0.5826 | 0.7545 |
0.2779 | 67.0 | 10452 | 0.6070 | 0.7473 |
0.2621 | 68.0 | 10608 | 0.5399 | 0.7509 |
0.2621 | 69.0 | 10764 | 0.5598 | 0.7437 |
0.2621 | 70.0 | 10920 | 0.5688 | 0.7401 |
0.2549 | 71.0 | 11076 | 0.5407 | 0.7437 |
0.2549 | 72.0 | 11232 | 0.5516 | 0.7473 |
0.2549 | 73.0 | 11388 | 0.5699 | 0.7148 |
0.2453 | 74.0 | 11544 | 0.5284 | 0.7437 |
0.2453 | 75.0 | 11700 | 0.5615 | 0.7401 |
0.2453 | 76.0 | 11856 | 0.5336 | 0.7365 |
0.2478 | 77.0 | 12012 | 0.5502 | 0.7401 |
0.2478 | 78.0 | 12168 | 0.5507 | 0.7401 |
0.2478 | 79.0 | 12324 | 0.5451 | 0.7401 |
0.2478 | 80.0 | 12480 | 0.5509 | 0.7401 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3