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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: profesor_MViT_B_VIOPERU |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# profesor_MViT_B_VIOPERU |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5811 |
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- Accuracy: 0.8393 |
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- F1: 0.8385 |
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- Precision: 0.8464 |
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- Recall: 0.8393 |
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- Roc Auc: 0.9232 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 22 |
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- eval_batch_size: 22 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 90 |
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- training_steps: 900 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:| |
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| 0.6576 | 7.0067 | 90 | 0.6648 | 0.6786 | 0.6719 | 0.6944 | 0.6786 | 0.7857 | |
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| 0.5634 | 14.0133 | 180 | 0.5872 | 0.75 | 0.7497 | 0.7513 | 0.75 | 0.8533 | |
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| 0.4258 | 22.0067 | 270 | 0.4743 | 0.875 | 0.8750 | 0.8755 | 0.875 | 0.9133 | |
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| 0.3059 | 29.0133 | 360 | 0.4014 | 0.8393 | 0.8392 | 0.8397 | 0.8393 | 0.9298 | |
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| 0.2045 | 37.0067 | 450 | 0.3394 | 0.8571 | 0.8570 | 0.8590 | 0.8571 | 0.9401 | |
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| 0.1448 | 44.0133 | 540 | 0.3734 | 0.8393 | 0.8392 | 0.8397 | 0.8393 | 0.9279 | |
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| 0.1082 | 52.0067 | 630 | 0.3368 | 0.8929 | 0.8916 | 0.9118 | 0.8929 | 0.9515 | |
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| 0.0912 | 59.0133 | 720 | 0.3935 | 0.8571 | 0.8564 | 0.8646 | 0.8571 | 0.9388 | |
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| 0.0736 | 67.0067 | 810 | 0.3789 | 0.8929 | 0.8916 | 0.9118 | 0.8929 | 0.9522 | |
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| 0.0615 | 74.0133 | 900 | 0.3806 | 0.8929 | 0.8916 | 0.9118 | 0.8929 | 0.9579 | |
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### Framework versions |
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- Transformers 4.46.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.1 |
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