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# vit-base_rvl_cdip-N1K_aAURC_8

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: 0.5639
- Accuracy: 0.8882
- Brier Loss: 0.2015
- Nll: 0.8676
- F1 Micro: 0.8882
- F1 Macro: 0.8882
- Ece: 0.0953
- Aurc: 0.0259

## 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: 8
- eval_batch_size: 8
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.2121        | 1.0   | 2000  | 0.4148          | 0.862    | 0.2185     | 1.2055 | 0.8620   | 0.8637   | 0.0821 | 0.0286 |
| 0.1797        | 2.0   | 4000  | 0.4259          | 0.8738   | 0.2073     | 1.1106 | 0.8738   | 0.8729   | 0.0842 | 0.0269 |
| 0.0999        | 3.0   | 6000  | 0.4691          | 0.874    | 0.2161     | 1.0691 | 0.874    | 0.8739   | 0.0941 | 0.0287 |
| 0.0532        | 4.0   | 8000  | 0.5251          | 0.872    | 0.2218     | 1.1401 | 0.872    | 0.8726   | 0.0995 | 0.0287 |
| 0.0197        | 5.0   | 10000 | 0.5723          | 0.871    | 0.2303     | 1.0391 | 0.871    | 0.8710   | 0.1085 | 0.0297 |
| 0.0118        | 6.0   | 12000 | 0.5253          | 0.8845   | 0.2070     | 0.9140 | 0.8845   | 0.8847   | 0.0953 | 0.0246 |
| 0.0095        | 7.0   | 14000 | 0.5969          | 0.8718   | 0.2284     | 0.9640 | 0.8718   | 0.8717   | 0.1094 | 0.0249 |
| 0.0063        | 8.0   | 16000 | 0.5702          | 0.8848   | 0.2087     | 0.8868 | 0.8848   | 0.8846   | 0.1003 | 0.0258 |
| 0.0014        | 9.0   | 18000 | 0.5810          | 0.8825   | 0.2115     | 0.8615 | 0.8825   | 0.8828   | 0.0998 | 0.0271 |
| 0.0025        | 10.0  | 20000 | 0.5639          | 0.8882   | 0.2015     | 0.8676 | 0.8882   | 0.8882   | 0.0953 | 0.0259 |


### Framework versions

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

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