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# vit-base_rvl_cdip-N1K_AURC_16

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: 0.2895
- Accuracy: 0.8925
- Brier Loss: 0.1833
- Nll: 0.8632
- F1 Micro: 0.8925
- F1 Macro: 0.8927
- Ece: 0.0768
- Aurc: 0.0218

## 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: 16
- eval_batch_size: 16
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0448        | 1.0   | 1000  | 0.1956          | 0.8758   | 0.1900     | 1.1701 | 0.8758   | 0.8769   | 0.0566 | 0.0252 |
| 0.0381        | 2.0   | 2000  | 0.2463          | 0.8715   | 0.1989     | 1.1688 | 0.8715   | 0.8716   | 0.0715 | 0.0261 |
| 0.0136        | 3.0   | 3000  | 0.2947          | 0.87     | 0.2081     | 1.0890 | 0.87     | 0.8693   | 0.0752 | 0.0271 |
| 0.0092        | 4.0   | 4000  | 0.2718          | 0.881    | 0.1901     | 1.0230 | 0.881    | 0.8811   | 0.0759 | 0.0253 |
| 0.0048        | 5.0   | 5000  | 0.2823          | 0.8812   | 0.1934     | 0.9914 | 0.8812   | 0.8814   | 0.0777 | 0.0238 |
| 0.0045        | 6.0   | 6000  | 0.2555          | 0.8855   | 0.1889     | 0.9305 | 0.8855   | 0.8861   | 0.0768 | 0.0223 |
| 0.0022        | 7.0   | 7000  | 0.2754          | 0.886    | 0.1873     | 0.8958 | 0.886    | 0.8860   | 0.0804 | 0.0221 |
| 0.0019        | 8.0   | 8000  | 0.2784          | 0.8858   | 0.1914     | 0.9248 | 0.8858   | 0.8866   | 0.0796 | 0.0229 |
| 0.0008        | 9.0   | 9000  | 0.2855          | 0.8878   | 0.1885     | 0.8671 | 0.8878   | 0.8876   | 0.0809 | 0.0226 |
| 0.0005        | 10.0  | 10000 | 0.2895          | 0.8925   | 0.1833     | 0.8632 | 0.8925   | 0.8927   | 0.0768 | 0.0218 |


### Framework versions

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

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