---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_1
  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_1

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: 0.4233
- Bleu: 0.0659
- Precisions: [0.8058455114822547, 0.7440758293838863, 0.7013698630136986, 0.6590909090909091]
- Brevity Penalty: 0.0908
- Length Ratio: 0.2942
- Translation Length: 479
- Reference Length: 1628
- Cer: 0.7576
- Wer: 0.8295

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.8942        | 1.0   | 253  | 0.5716          | 0.0571 | [0.7436974789915967, 0.6610978520286396, 0.6104972375690608, 0.5672131147540984] | 0.0889          | 0.2924       | 476                | 1628             | 0.7669 | 0.8416 |
| 0.3794        | 2.0   | 506  | 0.4522          | 0.0594 | [0.770042194092827, 0.697841726618705, 0.6472222222222223, 0.6072607260726073]   | 0.0876          | 0.2912       | 474                | 1628             | 0.7642 | 0.8415 |
| 0.3017        | 3.0   | 759  | 0.4154          | 0.0642 | [0.8029350104821803, 0.7357142857142858, 0.6887052341597796, 0.6503267973856209] | 0.0895          | 0.2930       | 477                | 1628             | 0.7577 | 0.8320 |
| 0.222         | 4.0   | 1012 | 0.4233          | 0.0659 | [0.8058455114822547, 0.7440758293838863, 0.7013698630136986, 0.6590909090909091] | 0.0908          | 0.2942       | 479                | 1628             | 0.7576 | 0.8295 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1