---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: profesor_MViT_N_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_N_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: 0.4752
- Accuracy: 0.8929
- F1: 0.8927
- Precision: 0.8949
- Recall: 0.8929
- Roc Auc: 0.9283

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.6465        | 7.0067  | 90   | 0.6553          | 0.7679   | 0.7672 | 0.7710    | 0.7679 | 0.8010  |
| 0.5617        | 14.0133 | 180  | 0.5770          | 0.8036   | 0.8030 | 0.8071    | 0.8036 | 0.8673  |
| 0.4403        | 22.0067 | 270  | 0.4678          | 0.8929   | 0.8927 | 0.8949    | 0.8929 | 0.9031  |
| 0.3043        | 29.0133 | 360  | 0.4475          | 0.8393   | 0.8380 | 0.8505    | 0.8393 | 0.9069  |
| 0.196         | 37.0067 | 450  | 0.4182          | 0.8393   | 0.8380 | 0.8505    | 0.8393 | 0.9247  |
| 0.131         | 44.0133 | 540  | 0.3250          | 0.875    | 0.8746 | 0.8794    | 0.875  | 0.9515  |
| 0.0945        | 52.0067 | 630  | 0.3444          | 0.8929   | 0.8927 | 0.8949    | 0.8929 | 0.9605  |
| 0.0736        | 59.0133 | 720  | 0.3662          | 0.875    | 0.8746 | 0.8794    | 0.875  | 0.9554  |
| 0.0535        | 67.0067 | 810  | 0.4566          | 0.8571   | 0.8564 | 0.8646    | 0.8571 | 0.9681  |
| 0.0513        | 74.0133 | 900  | 0.4785          | 0.875    | 0.8746 | 0.8794    | 0.875  | 0.9554  |


### Framework versions

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