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# vit-base_rvl_cdip-N1K_AURC_4

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: 0.2768
- Accuracy: 0.8738
- Brier Loss: 0.2167
- Nll: 0.9821
- F1 Micro: 0.8738
- F1 Macro: 0.8749
- Ece: 0.0970
- Aurc: 0.0292

## 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: 4
- eval_batch_size: 4
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1764        | 1.0   | 4000  | 0.3808          | 0.8217   | 0.2750     | 1.2675 | 0.8217   | 0.8194   | 0.1016 | 0.0461 |
| 0.1131        | 2.0   | 8000  | 0.3321          | 0.8413   | 0.2583     | 1.3120 | 0.8413   | 0.8421   | 0.0949 | 0.0418 |
| 0.113         | 3.0   | 12000 | 0.3781          | 0.8207   | 0.2910     | 1.4889 | 0.8207   | 0.8213   | 0.1162 | 0.0496 |
| 0.0814        | 4.0   | 16000 | 0.4793          | 0.8157   | 0.3036     | 1.4208 | 0.8157   | 0.8151   | 0.1302 | 0.0552 |
| 0.0542        | 5.0   | 20000 | 0.2914          | 0.8658   | 0.2279     | 1.1541 | 0.8658   | 0.8657   | 0.0955 | 0.0320 |
| 0.0238        | 6.0   | 24000 | 0.3059          | 0.8568   | 0.2401     | 1.1686 | 0.8568   | 0.8581   | 0.1012 | 0.0354 |
| 0.0197        | 7.0   | 28000 | 0.3077          | 0.8545   | 0.2390     | 1.1659 | 0.8545   | 0.8553   | 0.1059 | 0.0354 |
| 0.0116        | 8.0   | 32000 | 0.3169          | 0.8705   | 0.2172     | 1.0323 | 0.8705   | 0.8704   | 0.0918 | 0.0314 |
| 0.0054        | 9.0   | 36000 | 0.2850          | 0.8738   | 0.2199     | 1.0171 | 0.8738   | 0.8747   | 0.0960 | 0.0302 |
| 0.0128        | 10.0  | 40000 | 0.2768          | 0.8738   | 0.2167     | 0.9821 | 0.8738   | 0.8749   | 0.0970 | 0.0292 |


### Framework versions

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

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