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

<!-- 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_ce

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: 0.5626
- Accuracy: 0.8932
- Brier Loss: 0.1854
- Nll: 0.8898
- F1 Micro: 0.8932
- F1 Macro: 0.8934
- Ece: 0.0831
- Aurc: 0.0199

## 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   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1771        | 1.0   | 500  | 0.4123          | 0.887    | 0.1720     | 1.2003 | 0.887    | 0.8872   | 0.0534 | 0.0204 |
| 0.1349        | 2.0   | 1000 | 0.4344          | 0.8895   | 0.1754     | 1.1219 | 0.8895   | 0.8900   | 0.0614 | 0.0207 |
| 0.0656        | 3.0   | 1500 | 0.4602          | 0.8852   | 0.1836     | 1.0477 | 0.8852   | 0.8856   | 0.0734 | 0.0197 |
| 0.0314        | 4.0   | 2000 | 0.5044          | 0.889    | 0.1851     | 1.0124 | 0.889    | 0.8888   | 0.0729 | 0.0230 |
| 0.0134        | 5.0   | 2500 | 0.5193          | 0.8895   | 0.1861     | 0.9779 | 0.8895   | 0.8905   | 0.0803 | 0.0207 |
| 0.0075        | 6.0   | 3000 | 0.5300          | 0.8915   | 0.1848     | 0.9515 | 0.8915   | 0.8922   | 0.0793 | 0.0203 |
| 0.0057        | 7.0   | 3500 | 0.5552          | 0.89     | 0.1893     | 0.9200 | 0.89     | 0.8897   | 0.0852 | 0.0205 |
| 0.0047        | 8.0   | 4000 | 0.5589          | 0.892    | 0.1871     | 0.9245 | 0.892    | 0.8923   | 0.0826 | 0.0198 |
| 0.0046        | 9.0   | 4500 | 0.5620          | 0.8935   | 0.1854     | 0.8987 | 0.8935   | 0.8937   | 0.0828 | 0.0199 |
| 0.0042        | 10.0  | 5000 | 0.5626          | 0.8932   | 0.1854     | 0.8898 | 0.8932   | 0.8934   | 0.0831 | 0.0199 |


### Framework versions

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