---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: profesor_MViT_S_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_S_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: 0.2991
- Accuracy: 0.9107
- F1: 0.9107
- Precision: 0.9112
- Recall: 0.9107
- Roc Auc: 0.9576

## 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: 23
- eval_batch_size: 23
- 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: 81
- training_steps: 810
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.6473        | 6.0111  | 81   | 0.6350          | 0.75     | 0.7497 | 0.7513    | 0.75   | 0.8418  |
| 0.5438        | 13.0074 | 162  | 0.5559          | 0.7857   | 0.7857 | 0.7857    | 0.7857 | 0.8980  |
| 0.4124        | 20.0037 | 243  | 0.4445          | 0.8571   | 0.8564 | 0.8646    | 0.8571 | 0.9439  |
| 0.2958        | 26.0148 | 324  | 0.3501          | 0.8929   | 0.8927 | 0.8949    | 0.8929 | 0.9745  |
| 0.2126        | 33.0111 | 405  | 0.2827          | 0.8929   | 0.8927 | 0.8949    | 0.8929 | 0.9745  |
| 0.1469        | 40.0074 | 486  | 0.3615          | 0.875    | 0.8746 | 0.8794    | 0.875  | 0.9732  |
| 0.1063        | 47.0037 | 567  | 0.3208          | 0.8929   | 0.8927 | 0.8949    | 0.8929 | 0.9783  |
| 0.0883        | 53.0148 | 648  | 0.4270          | 0.875    | 0.8746 | 0.8794    | 0.875  | 0.9745  |
| 0.0631        | 60.0111 | 729  | 0.4191          | 0.8929   | 0.8927 | 0.8949    | 0.8929 | 0.9783  |


### Framework versions

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