variant-tapt_base-LR_2e-05
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on the Mardiyyah/TAPT_Variant_FT dataset. It achieves the following results on the evaluation set:
- Loss: 1.4987
- Accuracy: 0.7109
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: 16
- eval_batch_size: 16
- seed: 3407
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7258 | 1.0 | 19 | 1.7873 | 0.6920 |
| 1.708 | 2.0 | 38 | 1.7398 | 0.6962 |
| 1.6506 | 3.0 | 57 | 1.6802 | 0.6973 |
| 1.5883 | 4.0 | 76 | 1.6648 | 0.7041 |
| 1.567 | 5.0 | 95 | 1.6342 | 0.7030 |
| 1.5485 | 6.0 | 114 | 1.5430 | 0.7146 |
| 1.5105 | 7.0 | 133 | 1.5296 | 0.7113 |
| 1.4635 | 8.0 | 152 | 1.6214 | 0.7078 |
| 1.4841 | 9.0 | 171 | 1.5212 | 0.7120 |
| 1.4663 | 10.0 | 190 | 1.5628 | 0.7035 |
| 1.4282 | 11.0 | 209 | 1.5351 | 0.7165 |
| 1.4511 | 12.0 | 228 | 1.5300 | 0.7095 |
| 1.4318 | 13.0 | 247 | 1.5256 | 0.7148 |
| 1.4241 | 14.0 | 266 | 1.4872 | 0.7146 |
| 1.4235 | 15.0 | 285 | 1.5431 | 0.7088 |
| 1.3905 | 16.0 | 304 | 1.5831 | 0.7096 |
| 1.3526 | 17.0 | 323 | 1.4920 | 0.7175 |
| 1.3733 | 18.0 | 342 | 1.5018 | 0.7104 |
| 1.3673 | 19.0 | 361 | 1.4766 | 0.7180 |
| 1.3631 | 20.0 | 380 | 1.4878 | 0.7142 |
| 1.3709 | 21.0 | 399 | 1.5422 | 0.7039 |
| 1.3408 | 22.0 | 418 | 1.4855 | 0.7206 |
| 1.3311 | 23.0 | 437 | 1.5095 | 0.7157 |
| 1.3144 | 24.0 | 456 | 1.5173 | 0.7157 |
| 1.297 | 25.0 | 475 | 1.4743 | 0.7215 |
| 1.3343 | 26.0 | 494 | 1.5012 | 0.7113 |
| 1.2949 | 27.0 | 513 | 1.4988 | 0.7146 |
| 1.3182 | 28.0 | 532 | 1.4198 | 0.7242 |
| 1.3005 | 29.0 | 551 | 1.4724 | 0.7161 |
| 1.2821 | 30.0 | 570 | 1.4705 | 0.7205 |
| 1.278 | 31.0 | 589 | 1.4780 | 0.7201 |
| 1.274 | 32.0 | 608 | 1.5008 | 0.7129 |
| 1.2849 | 33.0 | 627 | 1.4571 | 0.7200 |
| 1.2607 | 34.0 | 646 | 1.4253 | 0.7247 |
| 1.2673 | 35.0 | 665 | 1.5112 | 0.7101 |
| 1.259 | 36.0 | 684 | 1.5094 | 0.7149 |
| 1.2348 | 37.0 | 703 | 1.4844 | 0.7216 |
| 1.2561 | 38.0 | 722 | 1.4628 | 0.7171 |
| 1.2464 | 39.0 | 741 | 1.4711 | 0.7183 |
| 1.2483 | 40.0 | 760 | 1.4617 | 0.7228 |
| 1.2392 | 41.0 | 779 | 1.4650 | 0.7165 |
| 1.2306 | 42.0 | 798 | 1.4046 | 0.7259 |
| 1.2328 | 43.0 | 817 | 1.4773 | 0.7141 |
| 1.2493 | 44.0 | 836 | 1.4506 | 0.7229 |
| 1.2349 | 45.0 | 855 | 1.5113 | 0.7073 |
| 1.2352 | 46.0 | 874 | 1.4787 | 0.7155 |
| 1.2469 | 47.0 | 893 | 1.4405 | 0.7175 |
| 1.2215 | 48.0 | 912 | 1.4719 | 0.7176 |
| 1.2238 | 49.0 | 931 | 1.4799 | 0.7195 |
| 1.2371 | 50.0 | 950 | 1.4882 | 0.7123 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.21.0
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