---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-vishnu3
  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. -->

# donut-base-vishnu3

This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7699

## 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4209        | 1.0   | 127  | 2.1796          |
| 2.2026        | 2.0   | 254  | 1.5003          |
| 1.3437        | 3.0   | 381  | 1.2493          |
| 1.5722        | 4.0   | 508  | 1.1311          |
| 0.8412        | 5.0   | 635  | 1.0430          |
| 0.4204        | 6.0   | 762  | 0.9765          |
| 0.4769        | 7.0   | 889  | 0.9437          |
| 0.2852        | 8.0   | 1016 | 0.8951          |
| 0.4611        | 9.0   | 1143 | 0.8431          |
| 0.2971        | 10.0  | 1270 | 0.8496          |
| 0.2414        | 11.0  | 1397 | 0.8118          |
| 0.3451        | 12.0  | 1524 | 0.7813          |
| 0.1289        | 13.0  | 1651 | 0.7743          |
| 0.1248        | 14.0  | 1778 | 0.7769          |
| 0.122         | 15.0  | 1905 | 0.7699          |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1