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# vit-base_rvl_cdip_symce

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: 0.6253
- Accuracy: 0.8982
- Brier Loss: 0.1796
- Nll: 1.1468
- F1 Micro: 0.8982
- F1 Macro: 0.8984
- Ece: 0.0846
- Aurc: 0.0197

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1665        | 1.0   | 2500  | 0.3898          | 0.8939   | 0.1621     | 1.1704 | 0.8939   | 0.8938   | 0.0463 | 0.0167 |
| 0.1439        | 2.0   | 5000  | 0.3927          | 0.8949   | 0.1602     | 1.1860 | 0.8949   | 0.8954   | 0.0506 | 0.0165 |
| 0.0889        | 3.0   | 7500  | 0.4389          | 0.8941   | 0.1684     | 1.1449 | 0.8941   | 0.8946   | 0.0637 | 0.0172 |
| 0.0574        | 4.0   | 10000 | 0.4870          | 0.8953   | 0.1741     | 1.1605 | 0.8953   | 0.8952   | 0.0719 | 0.0179 |
| 0.0372        | 5.0   | 12500 | 0.5259          | 0.8929   | 0.1792     | 1.1860 | 0.8929   | 0.8935   | 0.0775 | 0.0185 |
| 0.0225        | 6.0   | 15000 | 0.5579          | 0.8959   | 0.1784     | 1.1504 | 0.8959   | 0.8963   | 0.0799 | 0.0196 |
| 0.0126        | 7.0   | 17500 | 0.5905          | 0.8949   | 0.1811     | 1.1714 | 0.8949   | 0.8950   | 0.0836 | 0.0197 |
| 0.0081        | 8.0   | 20000 | 0.6011          | 0.8973   | 0.1791     | 1.1720 | 0.8973   | 0.8975   | 0.0828 | 0.0198 |
| 0.0048        | 9.0   | 22500 | 0.6198          | 0.8975   | 0.1800     | 1.1518 | 0.8975   | 0.8977   | 0.0847 | 0.0198 |
| 0.0038        | 10.0  | 25000 | 0.6253          | 0.8982   | 0.1796     | 1.1468 | 0.8982   | 0.8984   | 0.0846 | 0.0197 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2

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