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# vit-base_rvl_cdip-N1K_ce_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: 0.5145
- Accuracy: 0.8908
- Brier Loss: 0.1847
- Nll: 0.9466
- F1 Micro: 0.8907
- F1 Macro: 0.8910
- Ece: 0.0829
- Aurc: 0.0191

## 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  | 0.4009          | 0.8892   | 0.1695     | 1.1791 | 0.8892   | 0.8896   | 0.0538 | 0.0185 |
| 0.1472        | 2.0   | 500  | 0.4214          | 0.8938   | 0.1688     | 1.1365 | 0.8938   | 0.8948   | 0.0527 | 0.0199 |
| 0.1472        | 3.0   | 750  | 0.4245          | 0.8898   | 0.1722     | 1.0919 | 0.8898   | 0.8900   | 0.0633 | 0.0185 |
| 0.0462        | 4.0   | 1000 | 0.4571          | 0.891    | 0.1776     | 1.0386 | 0.891    | 0.8914   | 0.0699 | 0.0198 |
| 0.0462        | 5.0   | 1250 | 0.4775          | 0.8922   | 0.1797     | 1.0236 | 0.8922   | 0.8926   | 0.0745 | 0.0196 |
| 0.0118        | 6.0   | 1500 | 0.4953          | 0.8878   | 0.1845     | 0.9920 | 0.8878   | 0.8882   | 0.0823 | 0.0190 |
| 0.0118        | 7.0   | 1750 | 0.5052          | 0.89     | 0.1847     | 0.9631 | 0.89     | 0.8903   | 0.0820 | 0.0193 |
| 0.0065        | 8.0   | 2000 | 0.5068          | 0.8905   | 0.1832     | 0.9653 | 0.8905   | 0.8910   | 0.0816 | 0.0190 |
| 0.0065        | 9.0   | 2250 | 0.5143          | 0.8905   | 0.1850     | 0.9551 | 0.8905   | 0.8908   | 0.0833 | 0.0191 |
| 0.0053        | 10.0  | 2500 | 0.5145          | 0.8908   | 0.1847     | 0.9466 | 0.8907   | 0.8910   | 0.0829 | 0.0191 |


### Framework versions

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

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