---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_bayesian_trial_10
  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_10

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: 0.4219
- Bleu: 0.0632
- Precisions: [0.809322033898305, 0.7493975903614458, 0.7094972067039106, 0.6644518272425249]
- Brevity Penalty: 0.0864
- Length Ratio: 0.2899
- Translation Length: 472
- Reference Length: 1628
- Cer: 0.7596
- Wer: 0.8312

## 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: 1.0082458996730595e-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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.3888        | 1.0   | 253  | 0.5110          | 0.0673 | [0.7909836065573771, 0.7099767981438515, 0.6657754010695187, 0.6277602523659306] | 0.0967          | 0.2998       | 488                | 1628             | 0.7690 | 0.8412 |
| 0.326         | 2.0   | 506  | 0.4539          | 0.0654 | [0.7908902691511387, 0.7276995305164319, 0.6775067750677507, 0.6153846153846154] | 0.0934          | 0.2967       | 483                | 1628             | 0.7604 | 0.8362 |
| 0.3191        | 3.0   | 759  | 0.4256          | 0.0654 | [0.7837837837837838, 0.7287735849056604, 0.6893732970027248, 0.6451612903225806] | 0.0921          | 0.2955       | 481                | 1628             | 0.7599 | 0.8331 |
| 0.2632        | 4.0   | 1012 | 0.4219          | 0.0632 | [0.809322033898305, 0.7493975903614458, 0.7094972067039106, 0.6644518272425249]  | 0.0864          | 0.2899       | 472                | 1628             | 0.7596 | 0.8312 |


### Framework versions

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