---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: profesor_MViT_B_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. -->

# profesor_MViT_B_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: 0.5811
- Accuracy: 0.8393
- F1: 0.8385
- Precision: 0.8464
- Recall: 0.8393
- Roc Auc: 0.9232

## 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: 22
- eval_batch_size: 22
- 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: 90
- training_steps: 900
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.6576        | 7.0067  | 90   | 0.6648          | 0.6786   | 0.6719 | 0.6944    | 0.6786 | 0.7857  |
| 0.5634        | 14.0133 | 180  | 0.5872          | 0.75     | 0.7497 | 0.7513    | 0.75   | 0.8533  |
| 0.4258        | 22.0067 | 270  | 0.4743          | 0.875    | 0.8750 | 0.8755    | 0.875  | 0.9133  |
| 0.3059        | 29.0133 | 360  | 0.4014          | 0.8393   | 0.8392 | 0.8397    | 0.8393 | 0.9298  |
| 0.2045        | 37.0067 | 450  | 0.3394          | 0.8571   | 0.8570 | 0.8590    | 0.8571 | 0.9401  |
| 0.1448        | 44.0133 | 540  | 0.3734          | 0.8393   | 0.8392 | 0.8397    | 0.8393 | 0.9279  |
| 0.1082        | 52.0067 | 630  | 0.3368          | 0.8929   | 0.8916 | 0.9118    | 0.8929 | 0.9515  |
| 0.0912        | 59.0133 | 720  | 0.3935          | 0.8571   | 0.8564 | 0.8646    | 0.8571 | 0.9388  |
| 0.0736        | 67.0067 | 810  | 0.3789          | 0.8929   | 0.8916 | 0.9118    | 0.8929 | 0.9522  |
| 0.0615        | 74.0133 | 900  | 0.3806          | 0.8929   | 0.8916 | 0.9118    | 0.8929 | 0.9579  |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.0.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.1