20230824022326
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: 1.2256
- 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: 0.003
- 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 | 1.2170 | 0.5307 |
0.9844 | 2.0 | 624 | 0.7365 | 0.5090 |
0.9844 | 3.0 | 936 | 0.6978 | 0.5632 |
0.8956 | 4.0 | 1248 | 0.8855 | 0.4765 |
0.8957 | 5.0 | 1560 | 1.0223 | 0.5379 |
0.8957 | 6.0 | 1872 | 0.6873 | 0.6137 |
0.7665 | 7.0 | 2184 | 0.8629 | 0.6173 |
0.7665 | 8.0 | 2496 | 0.6861 | 0.6570 |
0.734 | 9.0 | 2808 | 0.6714 | 0.7076 |
0.7238 | 10.0 | 3120 | 0.6298 | 0.7184 |
0.7238 | 11.0 | 3432 | 0.5975 | 0.7184 |
0.6786 | 12.0 | 3744 | 0.8311 | 0.6968 |
0.6396 | 13.0 | 4056 | 0.7136 | 0.6751 |
0.6396 | 14.0 | 4368 | 0.7183 | 0.6859 |
0.6481 | 15.0 | 4680 | 0.6652 | 0.7076 |
0.6481 | 16.0 | 4992 | 1.0367 | 0.6823 |
0.6106 | 17.0 | 5304 | 0.7197 | 0.6895 |
0.6011 | 18.0 | 5616 | 0.6058 | 0.7292 |
0.6011 | 19.0 | 5928 | 0.7227 | 0.7112 |
0.5978 | 20.0 | 6240 | 1.1472 | 0.6570 |
0.5309 | 21.0 | 6552 | 0.6741 | 0.7256 |
0.5309 | 22.0 | 6864 | 0.9335 | 0.6787 |
0.5392 | 23.0 | 7176 | 0.8296 | 0.7365 |
0.5392 | 24.0 | 7488 | 0.9097 | 0.7040 |
0.5058 | 25.0 | 7800 | 0.8278 | 0.7292 |
0.4669 | 26.0 | 8112 | 1.0859 | 0.6498 |
0.4669 | 27.0 | 8424 | 0.9387 | 0.7184 |
0.462 | 28.0 | 8736 | 1.0893 | 0.7365 |
0.4757 | 29.0 | 9048 | 1.3568 | 0.6859 |
0.4757 | 30.0 | 9360 | 1.0252 | 0.7040 |
0.4237 | 31.0 | 9672 | 1.0489 | 0.7329 |
0.4237 | 32.0 | 9984 | 0.8661 | 0.7292 |
0.4275 | 33.0 | 10296 | 0.9781 | 0.7437 |
0.3722 | 34.0 | 10608 | 0.8879 | 0.7329 |
0.3722 | 35.0 | 10920 | 0.9932 | 0.7292 |
0.3741 | 36.0 | 11232 | 1.0509 | 0.7365 |
0.3358 | 37.0 | 11544 | 1.3875 | 0.7329 |
0.3358 | 38.0 | 11856 | 1.2366 | 0.7220 |
0.3415 | 39.0 | 12168 | 1.0563 | 0.7329 |
0.3415 | 40.0 | 12480 | 0.9688 | 0.7401 |
0.3357 | 41.0 | 12792 | 0.8598 | 0.7329 |
0.3094 | 42.0 | 13104 | 1.0506 | 0.7329 |
0.3094 | 43.0 | 13416 | 1.3257 | 0.7365 |
0.2947 | 44.0 | 13728 | 1.1759 | 0.7365 |
0.2832 | 45.0 | 14040 | 1.1699 | 0.7329 |
0.2832 | 46.0 | 14352 | 1.1070 | 0.7401 |
0.2808 | 47.0 | 14664 | 1.1519 | 0.7473 |
0.2808 | 48.0 | 14976 | 1.0674 | 0.7401 |
0.2715 | 49.0 | 15288 | 1.1491 | 0.7401 |
0.252 | 50.0 | 15600 | 1.0819 | 0.7473 |
0.252 | 51.0 | 15912 | 0.9650 | 0.7473 |
0.2577 | 52.0 | 16224 | 1.0753 | 0.7437 |
0.2579 | 53.0 | 16536 | 1.0896 | 0.7473 |
0.2579 | 54.0 | 16848 | 1.0579 | 0.7401 |
0.2395 | 55.0 | 17160 | 1.1172 | 0.7509 |
0.2395 | 56.0 | 17472 | 1.1540 | 0.7509 |
0.2392 | 57.0 | 17784 | 1.2162 | 0.7509 |
0.22 | 58.0 | 18096 | 1.1978 | 0.7509 |
0.22 | 59.0 | 18408 | 1.2381 | 0.7473 |
0.2242 | 60.0 | 18720 | 1.2256 | 0.7473 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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