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# vit-base_rvl_cdip-N1K_AURC_32

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: 0.3439
- Accuracy: 0.8962
- Brier Loss: 0.1805
- Nll: 0.8184
- F1 Micro: 0.8962
- F1 Macro: 0.8963
- Ece: 0.0767
- Aurc: 0.0220

## 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: 32
- eval_batch_size: 32
- 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   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0301        | 1.0   | 500  | 0.1897          | 0.8808   | 0.1804     | 1.1636 | 0.8808   | 0.8807   | 0.0528 | 0.0227 |
| 0.0229        | 2.0   | 1000 | 0.2504          | 0.883    | 0.1834     | 1.1357 | 0.883    | 0.8832   | 0.0573 | 0.0248 |
| 0.0081        | 3.0   | 1500 | 0.2251          | 0.8858   | 0.1787     | 1.0242 | 0.8858   | 0.8858   | 0.0653 | 0.0221 |
| 0.004         | 4.0   | 2000 | 0.3075          | 0.886    | 0.1831     | 0.9279 | 0.886    | 0.8850   | 0.0744 | 0.0227 |
| 0.0023        | 5.0   | 2500 | 0.2491          | 0.8908   | 0.1791     | 0.9302 | 0.8907   | 0.8916   | 0.0728 | 0.0212 |
| 0.0014        | 6.0   | 3000 | 0.3067          | 0.8925   | 0.1795     | 0.8631 | 0.8925   | 0.8929   | 0.0752 | 0.0215 |
| 0.0012        | 7.0   | 3500 | 0.3277          | 0.8925   | 0.1812     | 0.8729 | 0.8925   | 0.8922   | 0.0764 | 0.0218 |
| 0.0009        | 8.0   | 4000 | 0.3386          | 0.895    | 0.1797     | 0.8406 | 0.895    | 0.8951   | 0.0760 | 0.0219 |
| 0.0007        | 9.0   | 4500 | 0.3383          | 0.8968   | 0.1808     | 0.8293 | 0.8968   | 0.8969   | 0.0747 | 0.0220 |
| 0.0006        | 10.0  | 5000 | 0.3439          | 0.8962   | 0.1805     | 0.8184 | 0.8962   | 0.8963   | 0.0767 | 0.0220 |


### Framework versions

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

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