| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: training_outputs |
| | 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. --> |
| |
|
| | # training_outputs |
| | |
| | This model was trained from scratch on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0394 |
| | - Accuracy: 0.993 |
| | - Precision: 0.9913 |
| | - Recall: 0.9884 |
| | - F1: 0.9899 |
| | - Roc Auc: 0.9988 |
| | |
| | ## 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: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 1 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
| | | 0.045 | 0.1105 | 1000 | 0.0609 | 0.987 | 0.9798 | 0.9827 | 0.9812 | 0.9990 | |
| | | 0.0539 | 0.2210 | 2000 | 0.0471 | 0.988 | 0.9883 | 0.9769 | 0.9826 | 0.9985 | |
| | | 0.0467 | 0.3316 | 3000 | 0.0546 | 0.989 | 0.9855 | 0.9827 | 0.9841 | 0.9989 | |
| | | 0.0439 | 0.4421 | 4000 | 0.0416 | 0.99 | 0.9884 | 0.9827 | 0.9855 | 0.9990 | |
| | | 0.0419 | 0.5526 | 5000 | 0.0470 | 0.99 | 0.9855 | 0.9855 | 0.9855 | 0.9991 | |
| | | 0.0395 | 0.6631 | 6000 | 0.0396 | 0.992 | 0.9884 | 0.9884 | 0.9884 | 0.9970 | |
| | | 0.0329 | 0.7737 | 7000 | 0.0427 | 0.993 | 0.9885 | 0.9913 | 0.9899 | 0.9986 | |
| | | 0.0373 | 0.8842 | 8000 | 0.0408 | 0.992 | 0.9884 | 0.9884 | 0.9884 | 0.9988 | |
| | | 0.031 | 0.9947 | 9000 | 0.0394 | 0.993 | 0.9913 | 0.9884 | 0.9899 | 0.9988 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.41.2 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| | |
