---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_bayesian_trial_16
  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_16

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: 0.5541
- Bleu: 0.0670
- Precisions: [0.8417721518987342, 0.7841726618705036, 0.7388888888888889, 0.6996699669966997]
- Brevity Penalty: 0.0876
- Length Ratio: 0.2912
- Translation Length: 474
- Reference Length: 1628
- Cer: 0.7567
- Wer: 0.8224

## 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: 0.00011219603369833024
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.0965        | 1.0   | 253  | 0.5550          | 0.0624 | [0.7995824634655533, 0.7085308056872038, 0.6520547945205479, 0.6038961038961039] | 0.0908          | 0.2942       | 479                | 1628             | 0.7576 | 0.8347 |
| 0.0844        | 2.0   | 506  | 0.5896          | 0.0651 | [0.8218029350104822, 0.7476190476190476, 0.696969696969697, 0.6535947712418301]  | 0.0895          | 0.2930       | 477                | 1628             | 0.7557 | 0.8302 |
| 0.0539        | 3.0   | 759  | 0.5594          | 0.0666 | [0.8322851153039832, 0.7642857142857142, 0.7134986225895317, 0.673202614379085]  | 0.0895          | 0.2930       | 477                | 1628             | 0.7552 | 0.8223 |
| 0.023         | 4.0   | 1012 | 0.5541          | 0.0670 | [0.8417721518987342, 0.7841726618705036, 0.7388888888888889, 0.6996699669966997] | 0.0876          | 0.2912       | 474                | 1628             | 0.7567 | 0.8224 |


### Framework versions

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