---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: estudiante_MC318_profesor_MViT_kl_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_kl_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: 0.0617
- Accuracy: 0.9829
- F1: 0.9829
- Precision: 0.9831
- Recall: 0.9829
- 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: 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 1.1707        | 1.0145  | 79   | 0.3903          | 0.9091   | 0.9091 | 0.9091    | 0.9091 | 0.9760  |
| 0.7543        | 2.0289  | 158  | 0.2225          | 0.9358   | 0.9358 | 0.9359    | 0.9358 | 0.9882  |
| 0.5018        | 4.0082  | 237  | 0.1346          | 0.9572   | 0.9572 | 0.9577    | 0.9572 | 0.9928  |
| 0.3639        | 5.0226  | 316  | 0.0938          | 0.9706   | 0.9706 | 0.9706    | 0.9706 | 0.9945  |
| 0.2751        | 7.0019  | 395  | 0.1042          | 0.9706   | 0.9706 | 0.9707    | 0.9706 | 0.9963  |
| 0.2243        | 8.0164  | 474  | 0.0650          | 0.9733   | 0.9733 | 0.9735    | 0.9733 | 0.9971  |
| 0.1755        | 9.0308  | 553  | 0.0690          | 0.9813   | 0.9813 | 0.9813    | 0.9813 | 0.9980  |
| 0.1681        | 11.0101 | 632  | 0.0532          | 0.9866   | 0.9866 | 0.9866    | 0.9866 | 0.9978  |
| 0.1581        | 12.0245 | 711  | 0.0432          | 0.9813   | 0.9813 | 0.9813    | 0.9813 | 0.9991  |
| 0.1245        | 14.0038 | 790  | 0.0446          | 0.9893   | 0.9893 | 0.9894    | 0.9893 | 0.9986  |
| 0.1291        | 15.0182 | 869  | 0.0467          | 0.9866   | 0.9866 | 0.9868    | 0.9866 | 0.9992  |
| 0.1057        | 16.0327 | 948  | 0.0427          | 0.9840   | 0.9840 | 0.9842    | 0.9840 | 0.9990  |
| 0.1109        | 18.0119 | 1027 | 0.0403          | 0.9840   | 0.9840 | 0.9842    | 0.9840 | 0.9991  |
| 0.1039        | 19.0264 | 1106 | 0.0762          | 0.9679   | 0.9679 | 0.9693    | 0.9679 | 0.9989  |
| 0.0978        | 21.0057 | 1185 | 0.0393          | 0.9813   | 0.9813 | 0.9813    | 0.9813 | 0.9991  |


### Framework versions

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