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# vit-base_rvl_cdip-N1K_ce_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: 0.5671
- Accuracy: 0.8915
- Brier Loss: 0.1895
- Nll: 0.9175
- F1 Micro: 0.8915
- F1 Macro: 0.8919
- Ece: 0.0850
- Aurc: 0.0200

## 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: 32
- eval_batch_size: 32
- 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   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1771        | 1.0   | 500  | 0.4121          | 0.8885   | 0.1719     | 1.2085 | 0.8885   | 0.8888   | 0.0509 | 0.0203 |
| 0.134         | 2.0   | 1000 | 0.4415          | 0.8882   | 0.1782     | 1.1210 | 0.8882   | 0.8886   | 0.0626 | 0.0212 |
| 0.0682        | 3.0   | 1500 | 0.4722          | 0.8855   | 0.1847     | 1.0778 | 0.8855   | 0.8858   | 0.0740 | 0.0213 |
| 0.0325        | 4.0   | 2000 | 0.4851          | 0.8905   | 0.1796     | 1.0195 | 0.8905   | 0.8911   | 0.0712 | 0.0213 |
| 0.0145        | 5.0   | 2500 | 0.5409          | 0.8842   | 0.1946     | 1.0096 | 0.8842   | 0.8850   | 0.0860 | 0.0217 |
| 0.0082        | 6.0   | 3000 | 0.5378          | 0.8872   | 0.1886     | 0.9573 | 0.8872   | 0.8879   | 0.0858 | 0.0206 |
| 0.0059        | 7.0   | 3500 | 0.5446          | 0.8895   | 0.1870     | 0.9288 | 0.8895   | 0.8897   | 0.0844 | 0.0206 |
| 0.0046        | 8.0   | 4000 | 0.5580          | 0.8885   | 0.1874     | 0.9153 | 0.8885   | 0.8889   | 0.0859 | 0.0203 |
| 0.0043        | 9.0   | 4500 | 0.5675          | 0.8905   | 0.1903     | 0.9313 | 0.8905   | 0.8910   | 0.0864 | 0.0201 |
| 0.004         | 10.0  | 5000 | 0.5671          | 0.8915   | 0.1895     | 0.9175 | 0.8915   | 0.8919   | 0.0850 | 0.0200 |


### Framework versions

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

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