---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_bayesian_trial_3
  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_bayesian_trial_3

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: 0.5840
- Bleu: 0.0667
- Precisions: [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846]
- Brevity Penalty: 0.0934
- Length Ratio: 0.2967
- Translation Length: 483
- Reference Length: 1628
- Cer: 0.7599
- Wer: 0.8328

## 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: 0.00017060423589132634
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.2012        | 1.0   | 253  | 0.6694          | 0.0547 | [0.7680851063829788, 0.6682808716707022, 0.6067415730337079, 0.5484949832775919] | 0.0851          | 0.2887       | 470                | 1628             | 0.7597 | 0.8411 |
| 0.127         | 2.0   | 506  | 0.6071          | 0.0638 | [0.7818930041152263, 0.6876456876456877, 0.6370967741935484, 0.5841269841269842] | 0.0954          | 0.2985       | 486                | 1628             | 0.7570 | 0.8360 |
| 0.0766        | 3.0   | 759  | 0.5786          | 0.0655 | [0.8125, 0.735224586288416, 0.6885245901639344, 0.6407766990291263]              | 0.0915          | 0.2948       | 480                | 1628             | 0.7564 | 0.8319 |
| 0.0259        | 4.0   | 1012 | 0.5840          | 0.0667 | [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846] | 0.0934          | 0.2967       | 483                | 1628             | 0.7599 | 0.8328 |


### Framework versions

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