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# vit-base_rvl_cdip-N1K_aAURC_128

This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4634
- Accuracy: 0.8915
- Brier Loss: 0.1791
- Nll: 0.9824
- F1 Micro: 0.8915
- F1 Macro: 0.8918
- Ece: 0.0767
- Aurc: 0.0184

## 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: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log        | 1.0   | 125  | 0.3790          | 0.8935   | 0.1649     | 1.1886 | 0.8935   | 0.8937   | 0.0488 | 0.0175 |
| No log        | 2.0   | 250  | 0.3783          | 0.8958   | 0.1605     | 1.1495 | 0.8958   | 0.8959   | 0.0497 | 0.0178 |
| No log        | 3.0   | 375  | 0.4065          | 0.8915   | 0.1700     | 1.0956 | 0.8915   | 0.8918   | 0.0617 | 0.0183 |
| 0.0928        | 4.0   | 500  | 0.4158          | 0.8932   | 0.1705     | 1.0843 | 0.8932   | 0.8936   | 0.0635 | 0.0183 |
| 0.0928        | 5.0   | 625  | 0.4328          | 0.8932   | 0.1721     | 1.0369 | 0.8932   | 0.8935   | 0.0673 | 0.0186 |
| 0.0928        | 6.0   | 750  | 0.4442          | 0.891    | 0.1764     | 1.0214 | 0.891    | 0.8913   | 0.0737 | 0.0183 |
| 0.0928        | 7.0   | 875  | 0.4542          | 0.8935   | 0.1770     | 1.0053 | 0.8935   | 0.8938   | 0.0722 | 0.0187 |
| 0.0125        | 8.0   | 1000 | 0.4587          | 0.891    | 0.1790     | 0.9941 | 0.891    | 0.8913   | 0.0767 | 0.0183 |
| 0.0125        | 9.0   | 1125 | 0.4616          | 0.891    | 0.1786     | 0.9847 | 0.891    | 0.8912   | 0.0767 | 0.0185 |
| 0.0125        | 10.0  | 1250 | 0.4634          | 0.8915   | 0.1791     | 0.9824 | 0.8915   | 0.8918   | 0.0767 | 0.0184 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3

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