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# vit-base_rvl_cdip-N1K_aAURC_16

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: 0.5629
- Accuracy: 0.8892
- Brier Loss: 0.1995
- Nll: 0.8643
- F1 Micro: 0.8892
- F1 Macro: 0.8898
- Ece: 0.0923
- Aurc: 0.0215

## 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: 16
- eval_batch_size: 16
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1794        | 1.0   | 1000  | 0.3827          | 0.8815   | 0.1829     | 1.1942 | 0.8815   | 0.8822   | 0.0573 | 0.0226 |
| 0.1415        | 2.0   | 2000  | 0.4705          | 0.8698   | 0.2118     | 1.1615 | 0.8698   | 0.8686   | 0.0859 | 0.0259 |
| 0.0725        | 3.0   | 3000  | 0.4582          | 0.8768   | 0.1996     | 1.0476 | 0.8768   | 0.8771   | 0.0845 | 0.0234 |
| 0.0388        | 4.0   | 4000  | 0.4958          | 0.879    | 0.2024     | 1.0000 | 0.879    | 0.8798   | 0.0877 | 0.0259 |
| 0.0153        | 5.0   | 5000  | 0.5171          | 0.8815   | 0.2047     | 0.9580 | 0.8815   | 0.8815   | 0.0942 | 0.0229 |
| 0.0069        | 6.0   | 6000  | 0.5334          | 0.8845   | 0.2021     | 0.9350 | 0.8845   | 0.8854   | 0.0922 | 0.0230 |
| 0.005         | 7.0   | 7000  | 0.5412          | 0.8905   | 0.1964     | 0.9179 | 0.8905   | 0.8907   | 0.0886 | 0.0218 |
| 0.0043        | 8.0   | 8000  | 0.5497          | 0.8892   | 0.1985     | 0.8970 | 0.8892   | 0.8900   | 0.0901 | 0.0225 |
| 0.0023        | 9.0   | 9000  | 0.5610          | 0.8878   | 0.1994     | 0.8679 | 0.8878   | 0.8883   | 0.0932 | 0.0220 |
| 0.0024        | 10.0  | 10000 | 0.5629          | 0.8892   | 0.1995     | 0.8643 | 0.8892   | 0.8898   | 0.0923 | 0.0215 |


### Framework versions

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

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