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# vit-base_rvl_cdip-N1K_AURC_8

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: 0.3096
- Accuracy: 0.883
- Brier Loss: 0.2014
- Nll: 0.9150
- F1 Micro: 0.883
- F1 Macro: 0.8832
- Ece: 0.0891
- Aurc: 0.0256

## 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: 8
- eval_batch_size: 8
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0736        | 1.0   | 2000  | 0.2385          | 0.8592   | 0.2172     | 1.2230 | 0.8592   | 0.8608   | 0.0734 | 0.0313 |
| 0.0594        | 2.0   | 4000  | 0.2561          | 0.8712   | 0.2047     | 1.2297 | 0.8713   | 0.8716   | 0.0678 | 0.0283 |
| 0.0421        | 3.0   | 6000  | 0.2432          | 0.867    | 0.2104     | 1.1813 | 0.867    | 0.8679   | 0.0749 | 0.0303 |
| 0.0256        | 4.0   | 8000  | 0.2882          | 0.8632   | 0.2199     | 1.1103 | 0.8632   | 0.8635   | 0.0847 | 0.0310 |
| 0.0147        | 5.0   | 10000 | 0.4246          | 0.8515   | 0.2466     | 1.1118 | 0.8515   | 0.8489   | 0.1059 | 0.0360 |
| 0.0105        | 6.0   | 12000 | 0.2747          | 0.8668   | 0.2220     | 1.0335 | 0.8668   | 0.8691   | 0.0986 | 0.0278 |
| 0.004         | 7.0   | 14000 | 0.2954          | 0.878    | 0.2034     | 0.9467 | 0.878    | 0.8783   | 0.0865 | 0.0264 |
| 0.0034        | 8.0   | 16000 | 0.3339          | 0.8708   | 0.2185     | 0.9551 | 0.8708   | 0.8713   | 0.0969 | 0.0286 |
| 0.0017        | 9.0   | 18000 | 0.3125          | 0.8748   | 0.2099     | 0.9454 | 0.8748   | 0.8761   | 0.0953 | 0.0265 |
| 0.0009        | 10.0  | 20000 | 0.3096          | 0.883    | 0.2014     | 0.9150 | 0.883    | 0.8832   | 0.0891 | 0.0256 |


### Framework versions

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

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