| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: estudiante_Swin3D_VIOPERU |
| 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. --> |
|
|
| # estudiante_Swin3D_VIOPERU |
|
|
| 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: 0.5108 |
| - Accuracy: 0.8036 |
| - F1: 0.8030 |
| - Precision: 0.8071 |
| - Recall: 0.8036 |
| - Roc Auc: 0.8383 |
|
|
| ## 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: 1e-05 |
| - train_batch_size: 20 |
| - eval_batch_size: 20 |
| - seed: 42 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 44 |
| - training_steps: 440 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc | |
| |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:| |
| | 0.6623 | 1.0227 | 22 | 0.6737 | 0.625 | 0.5960 | 0.6753 | 0.625 | 0.6186 | |
| | 0.6271 | 3.0182 | 44 | 0.6519 | 0.6786 | 0.6719 | 0.6944 | 0.6786 | 0.7066 | |
| | 0.5442 | 5.0136 | 66 | 0.6355 | 0.6964 | 0.6940 | 0.7029 | 0.6964 | 0.7309 | |
| | 0.5011 | 7.0091 | 88 | 0.5895 | 0.6607 | 0.6606 | 0.6609 | 0.6607 | 0.7577 | |
| | 0.4201 | 9.0045 | 110 | 0.5643 | 0.75 | 0.7487 | 0.7552 | 0.75 | 0.7806 | |
| | 0.3943 | 10.0273 | 132 | 0.5755 | 0.8036 | 0.8035 | 0.8040 | 0.8036 | 0.7857 | |
| | 0.3258 | 12.0227 | 154 | 0.6106 | 0.7679 | 0.7678 | 0.7682 | 0.7679 | 0.7870 | |
| | 0.2769 | 14.0182 | 176 | 0.5971 | 0.8036 | 0.8035 | 0.8040 | 0.8036 | 0.7959 | |
| | 0.2305 | 16.0136 | 198 | 0.5782 | 0.8036 | 0.8035 | 0.8040 | 0.8036 | 0.7997 | |
| | 0.2703 | 18.0091 | 220 | 0.6228 | 0.8036 | 0.8035 | 0.8040 | 0.8036 | 0.8099 | |
| | 0.1854 | 20.0045 | 242 | 0.7158 | 0.7679 | 0.7672 | 0.7710 | 0.7679 | 0.8278 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.46.1 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.1 |
| |
