---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: estudiante_Swin3D_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. -->

# estudiante_Swin3D_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: 0.5108
- Accuracy: 0.8036
- F1: 0.8030
- Precision: 0.8071
- Recall: 0.8036
- Roc Auc: 0.8383

## 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: 20
- eval_batch_size: 20
- 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: 44
- training_steps: 440
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.6623        | 1.0227  | 22   | 0.6737          | 0.625    | 0.5960 | 0.6753    | 0.625  | 0.6186  |
| 0.6271        | 3.0182  | 44   | 0.6519          | 0.6786   | 0.6719 | 0.6944    | 0.6786 | 0.7066  |
| 0.5442        | 5.0136  | 66   | 0.6355          | 0.6964   | 0.6940 | 0.7029    | 0.6964 | 0.7309  |
| 0.5011        | 7.0091  | 88   | 0.5895          | 0.6607   | 0.6606 | 0.6609    | 0.6607 | 0.7577  |
| 0.4201        | 9.0045  | 110  | 0.5643          | 0.75     | 0.7487 | 0.7552    | 0.75   | 0.7806  |
| 0.3943        | 10.0273 | 132  | 0.5755          | 0.8036   | 0.8035 | 0.8040    | 0.8036 | 0.7857  |
| 0.3258        | 12.0227 | 154  | 0.6106          | 0.7679   | 0.7678 | 0.7682    | 0.7679 | 0.7870  |
| 0.2769        | 14.0182 | 176  | 0.5971          | 0.8036   | 0.8035 | 0.8040    | 0.8036 | 0.7959  |
| 0.2305        | 16.0136 | 198  | 0.5782          | 0.8036   | 0.8035 | 0.8040    | 0.8036 | 0.7997  |
| 0.2703        | 18.0091 | 220  | 0.6228          | 0.8036   | 0.8035 | 0.8040    | 0.8036 | 0.8099  |
| 0.1854        | 20.0045 | 242  | 0.7158          | 0.7679   | 0.7672 | 0.7710    | 0.7679 | 0.8278  |


### Framework versions

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