---
license: apache-2.0
base_model: jordyvl/vit-base_rvl-cdip
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base_rvl_cdip_aurc
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base_rvl_cdip_aurc

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: 0.2759
- Accuracy: 0.893
- Brier Loss: 0.1798
- Nll: 0.8614
- F1 Micro: 0.893
- F1 Macro: 0.8928
- Ece: 0.0750
- Aurc: 0.0215

## 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: 2
- total_train_batch_size: 32
- 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   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0303        | 1.0   | 500  | 0.1865          | 0.8795   | 0.1840     | 1.2087 | 0.8795   | 0.8791   | 0.0495 | 0.0241 |
| 0.0262        | 2.0   | 1000 | 0.2146          | 0.8788   | 0.1909     | 1.1956 | 0.8788   | 0.8789   | 0.0603 | 0.0257 |
| 0.0121        | 3.0   | 1500 | 0.2117          | 0.886    | 0.1799     | 1.0878 | 0.886    | 0.8865   | 0.0611 | 0.0230 |
| 0.0057        | 4.0   | 2000 | 0.2279          | 0.8878   | 0.1803     | 1.0108 | 0.8878   | 0.8879   | 0.0678 | 0.0228 |
| 0.0038        | 5.0   | 2500 | 0.2491          | 0.8872   | 0.1827     | 0.9661 | 0.8872   | 0.8877   | 0.0725 | 0.0234 |
| 0.0028        | 6.0   | 3000 | 0.2398          | 0.89     | 0.1806     | 0.9378 | 0.89     | 0.8901   | 0.0725 | 0.0215 |
| 0.0016        | 7.0   | 3500 | 0.2736          | 0.891    | 0.1792     | 0.8975 | 0.891    | 0.8914   | 0.0744 | 0.0221 |
| 0.0014        | 8.0   | 4000 | 0.2357          | 0.8905   | 0.1811     | 0.8993 | 0.8905   | 0.8910   | 0.0764 | 0.0210 |
| 0.001         | 9.0   | 4500 | 0.2714          | 0.8898   | 0.1807     | 0.8650 | 0.8898   | 0.8897   | 0.0783 | 0.0213 |
| 0.0009        | 10.0  | 5000 | 0.2759          | 0.893    | 0.1798     | 0.8614 | 0.893    | 0.8928   | 0.0750 | 0.0215 |


### Framework versions

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