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---
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
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