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# vit-base_rvl_cdip-N1K_ce_8

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: 0.8308
- Accuracy: 0.8822
- Brier Loss: 0.2169
- Nll: 0.9246
- F1 Micro: 0.8822
- F1 Macro: 0.8823
- Ece: 0.1044
- Aurc: 0.0265

## 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: 8
- eval_batch_size: 8
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.2906        | 1.0   | 2000  | 0.5302          | 0.867    | 0.2106     | 1.2043 | 0.867    | 0.8685   | 0.0746 | 0.0284 |
| 0.236         | 2.0   | 4000  | 0.5819          | 0.8695   | 0.2142     | 1.1215 | 0.8695   | 0.8688   | 0.0909 | 0.0267 |
| 0.1236        | 3.0   | 6000  | 0.7115          | 0.8605   | 0.2390     | 1.1453 | 0.8605   | 0.8604   | 0.1069 | 0.0295 |
| 0.0703        | 4.0   | 8000  | 0.6965          | 0.8715   | 0.2265     | 1.0124 | 0.8715   | 0.8720   | 0.1015 | 0.0290 |
| 0.0307        | 5.0   | 10000 | 0.7503          | 0.8742   | 0.2229     | 0.9824 | 0.8742   | 0.8746   | 0.1052 | 0.0257 |
| 0.0229        | 6.0   | 12000 | 0.8042          | 0.874    | 0.2304     | 1.0125 | 0.874    | 0.8742   | 0.1091 | 0.0269 |
| 0.0114        | 7.0   | 14000 | 0.8335          | 0.8715   | 0.2283     | 1.0146 | 0.8715   | 0.8709   | 0.1103 | 0.0267 |
| 0.0082        | 8.0   | 16000 | 0.8655          | 0.873    | 0.2297     | 1.0222 | 0.8730   | 0.8735   | 0.1112 | 0.0279 |
| 0.002         | 9.0   | 18000 | 0.8350          | 0.8808   | 0.2180     | 0.9519 | 0.8808   | 0.8812   | 0.1067 | 0.0266 |
| 0.0041        | 10.0  | 20000 | 0.8308          | 0.8822   | 0.2169     | 0.9246 | 0.8822   | 0.8823   | 0.1044 | 0.0265 |


### Framework versions

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

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