
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base_rvl_cdip_crl

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: 0.6238
- Accuracy: 0.8956
- Brier Loss: 0.1819
- Nll: 1.1791
- F1 Micro: 0.8957
- F1 Macro: 0.8958
- Ece: 0.0846
- Aurc: 0.0210

## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1844        | 1.0   | 1250  | 0.4411          | 0.8961   | 0.1614     | 1.1240 | 0.8961   | 0.8963   | 0.0528 | 0.0161 |
| 0.1394        | 2.0   | 2500  | 0.4830          | 0.8927   | 0.1716     | 1.1324 | 0.8927   | 0.8927   | 0.0646 | 0.0175 |
| 0.1           | 3.0   | 3750  | 0.5257          | 0.8911   | 0.1791     | 1.1569 | 0.8911   | 0.8912   | 0.0737 | 0.0187 |
| 0.068         | 4.0   | 5000  | 0.5497          | 0.8913   | 0.1806     | 1.1705 | 0.8913   | 0.8913   | 0.0770 | 0.0192 |
| 0.048         | 5.0   | 6250  | 0.5762          | 0.8915   | 0.1834     | 1.1906 | 0.8915   | 0.8914   | 0.0808 | 0.0195 |
| 0.033         | 6.0   | 7500  | 0.5877          | 0.8936   | 0.1822     | 1.1690 | 0.8936   | 0.8938   | 0.0817 | 0.0196 |
| 0.0231        | 7.0   | 8750  | 0.6000          | 0.8938   | 0.1822     | 1.1867 | 0.8938   | 0.8939   | 0.0833 | 0.0206 |
| 0.0162        | 8.0   | 10000 | 0.6187          | 0.8948   | 0.1834     | 1.1827 | 0.8948   | 0.8949   | 0.0841 | 0.0208 |
| 0.0123        | 9.0   | 11250 | 0.6191          | 0.8953   | 0.1824     | 1.1868 | 0.8953   | 0.8955   | 0.0836 | 0.0207 |
| 0.0102        | 10.0  | 12500 | 0.6238          | 0.8956   | 0.1819     | 1.1791 | 0.8957   | 0.8958   | 0.0846 | 0.0210 |


### Framework versions

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

letter form email handwritten advertisement scientific_report scientific_publication specification file_folder news_article budget invoice presentation questionnaire resume memo