| | --- |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: DNADebertaK6_Worm |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # DNADebertaK6_Worm |
| | |
| | This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.6161 |
| | |
| | ## 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: 5e-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 |
| | - training_steps: 600001 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:------:|:------:|:---------------:| |
| | | 4.5653 | 7.26 | 20000 | 1.8704 | |
| | | 1.8664 | 14.53 | 40000 | 1.7762 | |
| | | 1.7803 | 21.79 | 60000 | 1.7429 | |
| | | 1.7502 | 29.06 | 80000 | 1.7305 | |
| | | 1.7329 | 36.32 | 100000 | 1.7185 | |
| | | 1.7191 | 43.59 | 120000 | 1.7073 | |
| | | 1.7065 | 50.85 | 140000 | 1.6925 | |
| | | 1.6945 | 58.12 | 160000 | 1.6877 | |
| | | 1.6862 | 65.38 | 180000 | 1.6792 | |
| | | 1.6788 | 72.65 | 200000 | 1.6712 | |
| | | 1.6729 | 79.91 | 220000 | 1.6621 | |
| | | 1.6679 | 87.18 | 240000 | 1.6608 | |
| | | 1.6632 | 94.44 | 260000 | 1.6586 | |
| | | 1.6582 | 101.71 | 280000 | 1.6585 | |
| | | 1.6551 | 108.97 | 300000 | 1.6564 | |
| | | 1.6507 | 116.24 | 320000 | 1.6449 | |
| | | 1.6481 | 123.5 | 340000 | 1.6460 | |
| | | 1.6448 | 130.77 | 360000 | 1.6411 | |
| | | 1.6425 | 138.03 | 380000 | 1.6408 | |
| | | 1.6387 | 145.3 | 400000 | 1.6358 | |
| | | 1.6369 | 152.56 | 420000 | 1.6373 | |
| | | 1.6337 | 159.83 | 440000 | 1.6364 | |
| | | 1.6312 | 167.09 | 460000 | 1.6303 | |
| | | 1.6298 | 174.36 | 480000 | 1.6346 | |
| | | 1.6273 | 181.62 | 500000 | 1.6272 | |
| | | 1.6244 | 188.88 | 520000 | 1.6268 | |
| | | 1.6225 | 196.15 | 540000 | 1.6295 | |
| | | 1.6207 | 203.41 | 560000 | 1.6206 | |
| | | 1.6186 | 210.68 | 580000 | 1.6277 | |
| | | 1.6171 | 217.94 | 600000 | 1.6161 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.19.2 |
| | - Pytorch 1.11.0 |
| | - Datasets 2.2.2 |
| | - Tokenizers 0.12.1 |
| | |
