---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_bayesian_trial_0
  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_0

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: 0.4050
- Bleu: 0.0639
- Precisions: [0.79957805907173, 0.7386091127098321, 0.7083333333333334, 0.6765676567656765]
- Brevity Penalty: 0.0876
- Length Ratio: 0.2912
- Translation Length: 474
- Reference Length: 1628
- Cer: 0.7653
- Wer: 0.8371

## 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: 1.2045081648781836e-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: 5
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.9353        | 1.0   | 253  | 0.6228          | 0.0486 | [0.7096774193548387, 0.6053921568627451, 0.5612535612535613, 0.5102040816326531] | 0.0820          | 0.2856       | 465                | 1628             | 0.7751 | 0.8592 |
| 0.462         | 2.0   | 506  | 0.4846          | 0.0568 | [0.7913978494623656, 0.7058823529411765, 0.6609686609686609, 0.6224489795918368] | 0.0820          | 0.2856       | 465                | 1628             | 0.7650 | 0.8423 |
| 0.4071        | 3.0   | 759  | 0.4226          | 0.0626 | [0.7899159663865546, 0.711217183770883, 0.6767955801104972, 0.6459016393442623]  | 0.0889          | 0.2924       | 476                | 1628             | 0.7685 | 0.8436 |
| 0.3007        | 4.0   | 1012 | 0.4092          | 0.0638 | [0.7957894736842105, 0.7344497607655502, 0.7008310249307479, 0.6644736842105263] | 0.0883          | 0.2918       | 475                | 1628             | 0.7640 | 0.8397 |
| 0.3114        | 5.0   | 1265 | 0.4050          | 0.0639 | [0.79957805907173, 0.7386091127098321, 0.7083333333333334, 0.6765676567656765]   | 0.0876          | 0.2912       | 474                | 1628             | 0.7653 | 0.8371 |


### Framework versions

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