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# vit-base_rvl_cdip-N1K_aAURC_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: 0.5215
- Accuracy: 0.888
- Brier Loss: 0.1918
- Nll: 0.9026
- F1 Micro: 0.888
- F1 Macro: 0.8883
- Ece: 0.0880
- Aurc: 0.0205

## 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   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1629        | 1.0   | 500  | 0.3779          | 0.8875   | 0.1721     | 1.1899 | 0.8875   | 0.8877   | 0.0531 | 0.0201 |
| 0.1234        | 2.0   | 1000 | 0.4074          | 0.8868   | 0.1790     | 1.1333 | 0.8868   | 0.8874   | 0.0647 | 0.0213 |
| 0.0616        | 3.0   | 1500 | 0.4257          | 0.888    | 0.1813     | 1.0677 | 0.888    | 0.8879   | 0.0695 | 0.0201 |
| 0.0303        | 4.0   | 2000 | 0.4595          | 0.885    | 0.1869     | 1.0256 | 0.885    | 0.8856   | 0.0776 | 0.0222 |
| 0.0133        | 5.0   | 2500 | 0.4902          | 0.8848   | 0.1922     | 0.9983 | 0.8848   | 0.8849   | 0.0831 | 0.0228 |
| 0.0083        | 6.0   | 3000 | 0.4941          | 0.8862   | 0.1903     | 0.9464 | 0.8862   | 0.8868   | 0.0850 | 0.0211 |
| 0.0051        | 7.0   | 3500 | 0.5116          | 0.8875   | 0.1928     | 0.9118 | 0.8875   | 0.8873   | 0.0875 | 0.0207 |
| 0.0043        | 8.0   | 4000 | 0.5154          | 0.8882   | 0.1910     | 0.9138 | 0.8882   | 0.8887   | 0.0864 | 0.0205 |
| 0.0041        | 9.0   | 4500 | 0.5221          | 0.8865   | 0.1924     | 0.9101 | 0.8865   | 0.8868   | 0.0896 | 0.0206 |
| 0.0037        | 10.0  | 5000 | 0.5215          | 0.888    | 0.1918     | 0.9026 | 0.888    | 0.8883   | 0.0880 | 0.0205 |


### Framework versions

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

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