<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
securebert-finetuned-autoisac
This model is a fine-tuned version of ehsanaghaei/SecureBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5774
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.4541 | 1.0 | 2 | 2.1295 |
2.3899 | 2.0 | 4 | 3.1051 |
2.384 | 3.0 | 6 | 2.3916 |
2.461 | 4.0 | 8 | 2.5481 |
2.3104 | 5.0 | 10 | 1.9451 |
2.3225 | 6.0 | 12 | 2.4900 |
2.1623 | 7.0 | 14 | 2.1504 |
2.2753 | 8.0 | 16 | 2.2117 |
2.1934 | 9.0 | 18 | 2.2114 |
2.2003 | 10.0 | 20 | 2.5221 |
2.1598 | 11.0 | 22 | 2.0404 |
2.1319 | 12.0 | 24 | 1.9068 |
2.1139 | 13.0 | 26 | 1.8526 |
1.9242 | 14.0 | 28 | 1.6899 |
1.8706 | 15.0 | 30 | 2.2340 |
1.9503 | 16.0 | 32 | 2.1700 |
1.939 | 17.0 | 34 | 1.7180 |
1.998 | 18.0 | 36 | 1.9487 |
1.9129 | 19.0 | 38 | 2.3239 |
1.8028 | 20.0 | 40 | 2.4939 |
2.0098 | 21.0 | 42 | 2.1276 |
1.8822 | 22.0 | 44 | 1.5615 |
1.8569 | 23.0 | 46 | 2.2414 |
1.7875 | 24.0 | 48 | 1.7774 |
1.8278 | 25.0 | 50 | 2.5106 |
1.8141 | 26.0 | 52 | 1.9493 |
1.8379 | 27.0 | 54 | 1.9589 |
1.8965 | 28.0 | 56 | 2.2619 |
1.8251 | 29.0 | 58 | 1.7368 |
1.6857 | 30.0 | 60 | 1.7609 |
1.7867 | 31.0 | 62 | 2.1918 |
1.7636 | 32.0 | 64 | 2.2292 |
1.632 | 33.0 | 66 | 1.9211 |
1.6702 | 34.0 | 68 | 2.3036 |
1.6825 | 35.0 | 70 | 2.3332 |
1.6613 | 36.0 | 72 | 1.9210 |
1.5195 | 37.0 | 74 | 1.7967 |
1.6362 | 38.0 | 76 | 1.8938 |
1.652 | 39.0 | 78 | 1.8180 |
1.7578 | 40.0 | 80 | 2.0958 |
1.7971 | 41.0 | 82 | 2.3873 |
1.5767 | 42.0 | 84 | 1.4808 |
1.6922 | 43.0 | 86 | 2.1077 |
1.5517 | 44.0 | 88 | 1.6335 |
1.6198 | 45.0 | 90 | 1.7669 |
1.5966 | 46.0 | 92 | 2.0056 |
1.588 | 47.0 | 94 | 1.8835 |
1.5696 | 48.0 | 96 | 2.1344 |
1.5497 | 49.0 | 98 | 1.9380 |
1.5754 | 50.0 | 100 | 1.9710 |
1.5357 | 51.0 | 102 | 1.9916 |
1.5488 | 52.0 | 104 | 1.9536 |
1.5625 | 53.0 | 106 | 2.0705 |
1.5039 | 54.0 | 108 | 2.0675 |
1.5423 | 55.0 | 110 | 2.0393 |
1.5478 | 56.0 | 112 | 1.9174 |
1.571 | 57.0 | 114 | 1.6184 |
1.506 | 58.0 | 116 | 2.0959 |
1.4856 | 59.0 | 118 | 2.2757 |
1.5077 | 60.0 | 120 | 2.2091 |
1.607 | 61.0 | 122 | 2.1535 |
1.558 | 62.0 | 124 | 1.7893 |
1.5304 | 63.0 | 126 | 2.4471 |
1.533 | 64.0 | 128 | 1.7384 |
1.424 | 65.0 | 130 | 1.7157 |
1.5778 | 66.0 | 132 | 1.9103 |
1.4301 | 67.0 | 134 | 1.6906 |
1.5053 | 68.0 | 136 | 1.6810 |
1.4954 | 69.0 | 138 | 1.8924 |
1.5213 | 70.0 | 140 | 1.5374 |
1.4771 | 71.0 | 142 | 1.6301 |
1.3914 | 72.0 | 144 | 1.9411 |
1.466 | 73.0 | 146 | 1.6775 |
1.4342 | 74.0 | 148 | 1.5887 |
1.4158 | 75.0 | 150 | 1.9451 |
1.4845 | 76.0 | 152 | 1.7925 |
1.447 | 77.0 | 154 | 1.6508 |
1.3285 | 78.0 | 156 | 2.3469 |
1.4416 | 79.0 | 158 | 1.9387 |
1.3357 | 80.0 | 160 | 1.9829 |
1.4197 | 81.0 | 162 | 2.1912 |
1.4183 | 82.0 | 164 | 1.7065 |
1.5176 | 83.0 | 166 | 1.8547 |
1.4922 | 84.0 | 168 | 1.7672 |
1.4131 | 85.0 | 170 | 1.8707 |
1.4281 | 86.0 | 172 | 1.9953 |
1.439 | 87.0 | 174 | 1.7536 |
1.4848 | 88.0 | 176 | 1.9255 |
1.4845 | 89.0 | 178 | 1.5462 |
1.4587 | 90.0 | 180 | 1.3696 |
1.366 | 91.0 | 182 | 2.1685 |
1.5134 | 92.0 | 184 | 2.1314 |
1.4547 | 93.0 | 186 | 2.1088 |
1.3936 | 94.0 | 188 | 1.8491 |
1.4802 | 95.0 | 190 | 1.8716 |
1.3974 | 96.0 | 192 | 2.1149 |
1.4762 | 97.0 | 194 | 1.9697 |
1.4287 | 98.0 | 196 | 1.6517 |
1.5177 | 99.0 | 198 | 2.0683 |
1.3889 | 100.0 | 200 | 1.5774 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3