---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_5
  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_experiment_5

This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3987
- Bleu: 0.0661
- Precisions: [0.8020833333333334, 0.7375886524822695, 0.6994535519125683, 0.6601941747572816]
- Brevity Penalty: 0.0915
- Length Ratio: 0.2948
- Translation Length: 480
- Reference Length: 1628
- Cer: 0.7576
- Wer: 0.8280

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Precisions                                                                       | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer    | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.3274        | 1.0   | 253  | 0.4698          | 0.0586 | [0.7707006369426752, 0.6956521739130435, 0.6582633053221288, 0.62]               | 0.0857          | 0.2893       | 471                | 1628             | 0.7660 | 0.8432 |
| 0.2539        | 2.0   | 506  | 0.4198          | 0.0643 | [0.799163179916318, 0.7315914489311164, 0.6868131868131868, 0.6416938110749185]  | 0.0902          | 0.2936       | 478                | 1628             | 0.7605 | 0.8313 |
| 0.224         | 3.0   | 759  | 0.3941          | 0.0658 | [0.8075313807531381, 0.7387173396674585, 0.7060439560439561, 0.6710097719869706] | 0.0902          | 0.2936       | 478                | 1628             | 0.7573 | 0.8283 |
| 0.1566        | 4.0   | 1012 | 0.3987          | 0.0661 | [0.8020833333333334, 0.7375886524822695, 0.6994535519125683, 0.6601941747572816] | 0.0915          | 0.2948       | 480                | 1628             | 0.7576 | 0.8280 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1