---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_bayesian_trial_6
  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_6

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: 0.5515
- Bleu: 0.0683
- Precisions: [0.8127572016460906, 0.7412587412587412, 0.6854838709677419, 0.638095238095238]
- Brevity Penalty: 0.0954
- Length Ratio: 0.2985
- Translation Length: 486
- Reference Length: 1628
- Cer: 0.7532
- Wer: 0.8274

## 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: 0.00016063260663724173
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.3276        | 1.0   | 253  | 0.6672          | 0.0589 | [0.76875, 0.6737588652482269, 0.6092896174863388, 0.5436893203883495]            | 0.0915          | 0.2948       | 480                | 1628             | 0.7586 | 0.8473 |
| 0.2008        | 2.0   | 506  | 0.5780          | 0.0662 | [0.7905544147843943, 0.7069767441860465, 0.6595174262734584, 0.6107594936708861] | 0.0960          | 0.2991       | 487                | 1628             | 0.7559 | 0.8374 |
| 0.1356        | 3.0   | 759  | 0.5355          | 0.0651 | [0.8238993710691824, 0.7452380952380953, 0.6942148760330579, 0.6535947712418301] | 0.0895          | 0.2930       | 477                | 1628             | 0.7580 | 0.8299 |
| 0.0394        | 4.0   | 1012 | 0.5515          | 0.0683 | [0.8127572016460906, 0.7412587412587412, 0.6854838709677419, 0.638095238095238]  | 0.0954          | 0.2985       | 486                | 1628             | 0.7532 | 0.8274 |


### Framework versions

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