---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: profesor_MViT_S_RLVS
  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_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: 0.0191
- Accuracy: 0.9947
- F1: 0.9947
- Precision: 0.9947
- Recall: 0.9947
- Roc Auc: 0.9999

## 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: 240
- training_steps: 2400
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.4099        | 2.0333  | 240  | 0.2282          | 0.9634   | 0.9634 | 0.9639    | 0.9634 | 0.9974  |
| 0.1424        | 5.0333  | 480  | 0.1349          | 0.9661   | 0.9660 | 0.9676    | 0.9661 | 0.9955  |
| 0.0659        | 8.0333  | 720  | 0.0890          | 0.9765   | 0.9765 | 0.9768    | 0.9765 | 0.9989  |
| 0.0582        | 11.0333 | 960  | 0.1153          | 0.9687   | 0.9687 | 0.9700    | 0.9687 | 0.9987  |
| 0.0319        | 14.0333 | 1200 | 0.0400          | 0.9896   | 0.9896 | 0.9896    | 0.9896 | 0.9994  |
| 0.0346        | 17.0333 | 1440 | 0.0408          | 0.9896   | 0.9896 | 0.9896    | 0.9896 | 0.9993  |
| 0.0145        | 20.0333 | 1680 | 0.0314          | 0.9922   | 0.9922 | 0.9922    | 0.9922 | 0.9997  |
| 0.0302        | 23.0333 | 1920 | 0.0395          | 0.9896   | 0.9896 | 0.9896    | 0.9896 | 0.9996  |
| 0.0244        | 26.0333 | 2160 | 0.0423          | 0.9896   | 0.9896 | 0.9896    | 0.9896 | 0.9998  |
| 0.0285        | 29.0333 | 2400 | 0.2273          | 0.9608   | 0.9608 | 0.9637    | 0.9608 | 0.9993  |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.0.1+cu118
- Datasets 3.0.2
- Tokenizers 0.20.1