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# vit-base_rvl_cdip-N1K_aAURC_64

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: 0.4857
- Accuracy: 0.8892
- Brier Loss: 0.1843
- Nll: 0.9506
- F1 Micro: 0.8892
- F1 Macro: 0.8895
- Ece: 0.0837
- Aurc: 0.0193

## 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
- 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   | 250  | 0.3824          | 0.888    | 0.1700     | 1.1756 | 0.888    | 0.8884   | 0.0548 | 0.0185 |
| 0.1403        | 2.0   | 500  | 0.3988          | 0.8925   | 0.1681     | 1.1230 | 0.8925   | 0.8936   | 0.0549 | 0.0199 |
| 0.1403        | 3.0   | 750  | 0.4099          | 0.8865   | 0.1756     | 1.0948 | 0.8865   | 0.8868   | 0.0672 | 0.0187 |
| 0.0442        | 4.0   | 1000 | 0.4297          | 0.8925   | 0.1747     | 1.0568 | 0.8925   | 0.8931   | 0.0685 | 0.0191 |
| 0.0442        | 5.0   | 1250 | 0.4467          | 0.8925   | 0.1775     | 1.0202 | 0.8925   | 0.8928   | 0.0734 | 0.0194 |
| 0.0119        | 6.0   | 1500 | 0.4612          | 0.8908   | 0.1808     | 0.9834 | 0.8907   | 0.8914   | 0.0772 | 0.0191 |
| 0.0119        | 7.0   | 1750 | 0.4762          | 0.8882   | 0.1845     | 0.9761 | 0.8882   | 0.8885   | 0.0827 | 0.0197 |
| 0.0062        | 8.0   | 2000 | 0.4763          | 0.892    | 0.1824     | 0.9652 | 0.892    | 0.8923   | 0.0789 | 0.0192 |
| 0.0062        | 9.0   | 2250 | 0.4854          | 0.8892   | 0.1844     | 0.9509 | 0.8892   | 0.8895   | 0.0834 | 0.0193 |
| 0.0051        | 10.0  | 2500 | 0.4857          | 0.8892   | 0.1843     | 0.9506 | 0.8892   | 0.8895   | 0.0837 | 0.0193 |


### Framework versions

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

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