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# vit-base_rvl_cdip-N1K_AURC_64

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: 0.3118
- Accuracy: 0.8952
- Brier Loss: 0.1766
- Nll: 0.8835
- F1 Micro: 0.8952
- F1 Macro: 0.8951
- Ece: 0.0747
- Aurc: 0.0206

## 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: 64
- eval_batch_size: 64
- 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   | 250  | 0.1770          | 0.8875   | 0.1709     | 1.2031 | 0.8875   | 0.8885   | 0.0519 | 0.0208 |
| 0.0228        | 2.0   | 500  | 0.2135          | 0.8852   | 0.1813     | 1.1542 | 0.8852   | 0.8853   | 0.0557 | 0.0228 |
| 0.0228        | 3.0   | 750  | 0.1750          | 0.8918   | 0.1729     | 1.0088 | 0.8918   | 0.8917   | 0.0628 | 0.0192 |
| 0.0066        | 4.0   | 1000 | 0.2117          | 0.8955   | 0.1697     | 0.9611 | 0.8955   | 0.8954   | 0.0655 | 0.0189 |
| 0.0066        | 5.0   | 1250 | 0.2578          | 0.8958   | 0.1714     | 0.9234 | 0.8958   | 0.8958   | 0.0690 | 0.0194 |
| 0.0021        | 6.0   | 1500 | 0.2752          | 0.8962   | 0.1730     | 0.9093 | 0.8962   | 0.8964   | 0.0709 | 0.0197 |
| 0.0021        | 7.0   | 1750 | 0.2949          | 0.8972   | 0.1748     | 0.8841 | 0.8972   | 0.8972   | 0.0708 | 0.0200 |
| 0.0014        | 8.0   | 2000 | 0.3037          | 0.8955   | 0.1755     | 0.8842 | 0.8955   | 0.8954   | 0.0739 | 0.0204 |
| 0.0014        | 9.0   | 2250 | 0.3045          | 0.8952   | 0.1764     | 0.8839 | 0.8952   | 0.8951   | 0.0741 | 0.0206 |
| 0.0013        | 10.0  | 2500 | 0.3118          | 0.8952   | 0.1766     | 0.8835 | 0.8952   | 0.8951   | 0.0747 | 0.0206 |


### Framework versions

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

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