---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: profesor_MViT_S_RWF2000
  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_RWF2000

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: 0.2287
- Accuracy: 0.9287
- F1: 0.9287
- Precision: 0.9288
- Recall: 0.9287
- Roc Auc: 0.9790

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.4986        | 2.0333  | 240  | 0.3695          | 0.9075   | 0.9075 | 0.9080    | 0.9075 | 0.9649  |
| 0.2786        | 5.0333  | 480  | 0.2486          | 0.915    | 0.9150 | 0.9152    | 0.915  | 0.9791  |
| 0.1896        | 8.0333  | 720  | 0.2303          | 0.92     | 0.9200 | 0.9204    | 0.92   | 0.9804  |
| 0.1882        | 11.0333 | 960  | 0.2339          | 0.9175   | 0.9175 | 0.9178    | 0.9175 | 0.9801  |
| 0.1381        | 14.0333 | 1200 | 0.2263          | 0.93     | 0.9300 | 0.9304    | 0.93   | 0.9807  |
| 0.1401        | 17.0333 | 1440 | 0.2477          | 0.9275   | 0.9275 | 0.9280    | 0.9275 | 0.9795  |
| 0.1016        | 20.0333 | 1680 | 0.2504          | 0.925    | 0.9250 | 0.9257    | 0.925  | 0.9819  |
| 0.1106        | 23.0333 | 1920 | 0.2722          | 0.93     | 0.9300 | 0.9304    | 0.93   | 0.9812  |


### Framework versions

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