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# vit-base_rvl_cdip-N1K_aAURC_4

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: 0.5297
- Accuracy: 0.874
- Brier Loss: 0.2289
- Nll: 0.9943
- F1 Micro: 0.874
- F1 Macro: 0.8744
- Ece: 0.1117
- Aurc: 0.0291

## 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: 4
- eval_batch_size: 4
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.2467        | 1.0   | 4000  | 0.3863          | 0.8403   | 0.2519     | 1.2558 | 0.8403   | 0.8407   | 0.0998 | 0.0394 |
| 0.1931        | 2.0   | 8000  | 0.4295          | 0.8482   | 0.2575     | 1.2140 | 0.8482   | 0.8486   | 0.1120 | 0.0362 |
| 0.1278        | 3.0   | 12000 | 0.4308          | 0.86     | 0.2406     | 1.1212 | 0.8600   | 0.8601   | 0.1063 | 0.0332 |
| 0.0798        | 4.0   | 16000 | 0.5079          | 0.853    | 0.2588     | 1.2528 | 0.853    | 0.8523   | 0.1221 | 0.0348 |
| 0.0422        | 5.0   | 20000 | 0.5064          | 0.8638   | 0.2443     | 1.1013 | 0.8638   | 0.8635   | 0.1165 | 0.0315 |
| 0.0123        | 6.0   | 24000 | 0.5186          | 0.8672   | 0.2378     | 1.0551 | 0.8672   | 0.8668   | 0.1155 | 0.0328 |
| 0.0048        | 7.0   | 28000 | 0.5372          | 0.8752   | 0.2306     | 1.1080 | 0.8752   | 0.8756   | 0.1101 | 0.0310 |
| 0.0098        | 8.0   | 32000 | 0.5395          | 0.8732   | 0.2325     | 1.0344 | 0.8732   | 0.8732   | 0.1135 | 0.0306 |
| 0.0019        | 9.0   | 36000 | 0.5249          | 0.875    | 0.2283     | 1.0203 | 0.875    | 0.8751   | 0.1099 | 0.0290 |
| 0.002         | 10.0  | 40000 | 0.5297          | 0.874    | 0.2289     | 0.9943 | 0.874    | 0.8744   | 0.1117 | 0.0291 |


### Framework versions

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

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