| | --- |
| | library_name: transformers |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: 3class_EfficientFormer30M_ForTesting |
| | 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. --> |
| |
|
| | # 3class_EfficientFormer30M_ForTesting |
| |
|
| | This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0636 |
| | - Precision: 0.9798 |
| | - Recall: 0.9764 |
| | - Accuracy: 0.9818 |
| | - F1: 0.9781 |
| | - Roc Auc: 0.9983 |
| |
|
| | ## 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: 0.0001 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - num_epochs: 4 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Roc Auc | |
| | |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------:| |
| | | 0.1199 | 0.3436 | 200 | 0.0955 | 0.9604 | 0.9551 | 0.9650 | 0.9576 | 0.9958 | |
| | | 0.0656 | 0.6873 | 400 | 0.0722 | 0.9754 | 0.9699 | 0.9774 | 0.9725 | 0.9972 | |
| | | 0.0418 | 1.0309 | 600 | 0.0797 | 0.9758 | 0.9740 | 0.9793 | 0.9749 | 0.9969 | |
| | | 0.0744 | 1.3746 | 800 | 0.0636 | 0.9798 | 0.9764 | 0.9818 | 0.9781 | 0.9983 | |
| | | 0.0044 | 1.7182 | 1000 | 0.0659 | 0.9793 | 0.9756 | 0.9814 | 0.9774 | 0.9983 | |
| | | 0.0412 | 2.0619 | 1200 | 0.0690 | 0.9782 | 0.9779 | 0.9818 | 0.9780 | 0.9983 | |
| | | 0.0029 | 2.4055 | 1400 | 0.0744 | 0.9808 | 0.9780 | 0.9830 | 0.9794 | 0.9984 | |
| | | 0.0245 | 2.7491 | 1600 | 0.0872 | 0.9813 | 0.9755 | 0.9821 | 0.9782 | 0.9981 | |
| | | 0.0006 | 3.0928 | 1800 | 0.0753 | 0.9811 | 0.9794 | 0.9837 | 0.9803 | 0.9985 | |
| | | 0.0049 | 3.4364 | 2000 | 0.0844 | 0.9799 | 0.9773 | 0.9823 | 0.9785 | 0.9984 | |
| | | 0.0011 | 3.7801 | 2200 | 0.0827 | 0.9806 | 0.9778 | 0.9828 | 0.9792 | 0.9984 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 5.3.0 |
| | - Pytorch 2.10.0+cu128 |
| | - Datasets 4.7.0 |
| | - Tokenizers 0.22.2 |
| | |
