---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_2
  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_2

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: 0.3855
- Bleu: 0.0663
- Precisions: [0.8273684210526315, 0.7703349282296651, 0.7285318559556787, 0.6842105263157895]
- Brevity Penalty: 0.0883
- Length Ratio: 0.2918
- Translation Length: 475
- Reference Length: 1628
- Cer: 0.7539
- Wer: 0.8251

## 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: 2e-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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.9517        | 1.0   | 253  | 0.5797          | 0.0543 | [0.7160751565762005, 0.6184834123222749, 0.5671232876712329, 0.5097402597402597] | 0.0908          | 0.2942       | 479                | 1628             | 0.7738 | 0.8500 |
| 0.3907        | 2.0   | 506  | 0.4532          | 0.0590 | [0.7851063829787234, 0.711864406779661, 0.6657303370786517, 0.6220735785953178]  | 0.0851          | 0.2887       | 470                | 1628             | 0.7610 | 0.8370 |
| 0.3245        | 3.0   | 759  | 0.4102          | 0.0625 | [0.8008474576271186, 0.7397590361445783, 0.7011173184357542, 0.6611295681063123] | 0.0864          | 0.2899       | 472                | 1628             | 0.7593 | 0.8336 |
| 0.2318        | 4.0   | 1012 | 0.3855          | 0.0663 | [0.8273684210526315, 0.7703349282296651, 0.7285318559556787, 0.6842105263157895] | 0.0883          | 0.2918       | 475                | 1628             | 0.7539 | 0.8251 |


### Framework versions

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