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# vit-base_rvl-cdip_r2_32

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: 0.6372
- Accuracy: 0.8985
- Brier Loss: 0.1792
- Nll: 1.1736
- F1 Micro: 0.8985
- F1 Macro: 0.8987
- Ece: 0.0847
- Aurc: 0.0201

## 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: 96
- eval_batch_size: 96
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1647        | 1.0   | 3334  | 0.4024          | 0.8887   | 0.1682     | 1.2086 | 0.8887   | 0.8891   | 0.0457 | 0.0178 |
| 0.1418        | 2.0   | 6668  | 0.4075          | 0.8941   | 0.1646     | 1.2066 | 0.8941   | 0.8942   | 0.0522 | 0.0177 |
| 0.0989        | 3.0   | 10002 | 0.4409          | 0.8932   | 0.1690     | 1.1966 | 0.8932   | 0.8932   | 0.0647 | 0.0175 |
| 0.0614        | 4.0   | 13336 | 0.4781          | 0.8944   | 0.1730     | 1.2083 | 0.8944   | 0.8951   | 0.0694 | 0.0181 |
| 0.0392        | 5.0   | 16670 | 0.5329          | 0.8959   | 0.1761     | 1.1777 | 0.8959   | 0.8958   | 0.0776 | 0.0187 |
| 0.0231        | 6.0   | 20004 | 0.5714          | 0.8957   | 0.1799     | 1.2083 | 0.8957   | 0.8958   | 0.0813 | 0.0198 |
| 0.0126        | 7.0   | 23338 | 0.6002          | 0.8966   | 0.1802     | 1.1732 | 0.8966   | 0.8972   | 0.0839 | 0.0197 |
| 0.0079        | 8.0   | 26672 | 0.6193          | 0.8984   | 0.1789     | 1.1849 | 0.8984   | 0.8985   | 0.0833 | 0.0200 |
| 0.0049        | 9.0   | 30006 | 0.6333          | 0.8976   | 0.1798     | 1.1906 | 0.8976   | 0.8978   | 0.0851 | 0.0205 |
| 0.0034        | 10.0  | 33340 | 0.6372          | 0.8985   | 0.1792     | 1.1736 | 0.8985   | 0.8987   | 0.0847 | 0.0201 |


### Framework versions

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

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