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# vit-base_rvl_cdip-N1K_AURC_256

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: 0.2459
- Accuracy: 0.8968
- Brier Loss: 0.1720
- Nll: 0.9246
- F1 Micro: 0.8968
- F1 Macro: 0.8967
- Ece: 0.0709
- Aurc: 0.0191

## 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: 256
- eval_batch_size: 256
- 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   | 63   | 0.1138          | 0.8922   | 0.1604     | 1.1695 | 0.8922   | 0.8926   | 0.0478 | 0.0170 |
| No log        | 2.0   | 126  | 0.1565          | 0.8952   | 0.1607     | 1.1000 | 0.8952   | 0.8952   | 0.0532 | 0.0176 |
| No log        | 3.0   | 189  | 0.1722          | 0.8972   | 0.1620     | 1.0250 | 0.8972   | 0.8973   | 0.0584 | 0.0175 |
| No log        | 4.0   | 252  | 0.2006          | 0.897    | 0.1642     | 0.9921 | 0.897    | 0.8969   | 0.0615 | 0.0181 |
| No log        | 5.0   | 315  | 0.2142          | 0.8988   | 0.1668     | 0.9670 | 0.8988   | 0.8986   | 0.0640 | 0.0183 |
| No log        | 6.0   | 378  | 0.2207          | 0.8975   | 0.1688     | 0.9482 | 0.8975   | 0.8975   | 0.0674 | 0.0186 |
| No log        | 7.0   | 441  | 0.2310          | 0.897    | 0.1700     | 0.9397 | 0.897    | 0.8969   | 0.0697 | 0.0188 |
| 0.008         | 8.0   | 504  | 0.2401          | 0.8968   | 0.1714     | 0.9268 | 0.8968   | 0.8966   | 0.0705 | 0.0190 |
| 0.008         | 9.0   | 567  | 0.2441          | 0.8975   | 0.1719     | 0.9262 | 0.8975   | 0.8974   | 0.0709 | 0.0191 |
| 0.008         | 10.0  | 630  | 0.2459          | 0.8968   | 0.1720     | 0.9246 | 0.8968   | 0.8967   | 0.0709 | 0.0191 |


### Framework versions

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

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