---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: estudiante_MC318_profesor_MViT_akl_RLVS
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# estudiante_MC318_profesor_MViT_akl_RLVS

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: 0.0597
- Accuracy: 0.9803
- F1: 0.9803
- Precision: 0.9803
- Recall: 0.9803
- Roc Auc: 0.9979

## 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: 30
- eval_batch_size: 30
- 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: 159
- training_steps: 1590
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 22.7945       | 1.0145  | 79   | 0.4204          | 0.8984   | 0.8984 | 0.8985    | 0.8984 | 0.9730  |
| 14.8284       | 2.0289  | 158  | 0.2329          | 0.9251   | 0.9251 | 0.9259    | 0.9251 | 0.9836  |
| 10.0047       | 4.0082  | 237  | 0.1401          | 0.9492   | 0.9492 | 0.9492    | 0.9492 | 0.9905  |
| 7.3642        | 5.0226  | 316  | 0.1023          | 0.9733   | 0.9733 | 0.9733    | 0.9733 | 0.9932  |
| 5.6128        | 7.0019  | 395  | 0.1193          | 0.9733   | 0.9733 | 0.9733    | 0.9733 | 0.9947  |
| 4.5908        | 8.0164  | 474  | 0.0743          | 0.9733   | 0.9733 | 0.9735    | 0.9733 | 0.9958  |
| 3.4695        | 9.0308  | 553  | 0.0811          | 0.9759   | 0.9759 | 0.9760    | 0.9759 | 0.9968  |
| 3.3759        | 11.0101 | 632  | 0.0575          | 0.9759   | 0.9759 | 0.9761    | 0.9759 | 0.9975  |
| 3.4995        | 12.0245 | 711  | 0.0492          | 0.9840   | 0.9840 | 0.9840    | 0.9840 | 0.9982  |
| 2.5838        | 14.0038 | 790  | 0.0470          | 0.9840   | 0.9840 | 0.9840    | 0.9840 | 0.9980  |
| 2.8276        | 15.0182 | 869  | 0.0544          | 0.9759   | 0.9759 | 0.9761    | 0.9759 | 0.9986  |
| 2.3636        | 16.0327 | 948  | 0.0441          | 0.9786   | 0.9786 | 0.9791    | 0.9786 | 0.9989  |
| 2.5376        | 18.0119 | 1027 | 0.0431          | 0.9840   | 0.9840 | 0.9842    | 0.9840 | 0.9988  |
| 2.3264        | 19.0264 | 1106 | 0.0620          | 0.9706   | 0.9706 | 0.9717    | 0.9706 | 0.9989  |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.0.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3