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---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- bleu
- wer
model-index:
- name: donut-base-sroie-test-050824
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-base-sroie-test-050824
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5823
- Bleu: 0.0595
- Precisions: [0.7332123411978222, 0.6290983606557377, 0.5741176470588235, 0.5248618784530387]
- Brevity Penalty: 0.0974
- Length Ratio: 0.3004
- Translation Length: 551
- Reference Length: 1834
- Cer: 0.7739
- Wer: 0.8665
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 3.716 | 1.0 | 250 | 1.7346 | 0.0080 | [0.3276089828269485, 0.05475504322766571, 0.01901743264659271, 0.0035211267605633804] | 0.2411 | 0.4128 | 757 | 1834 | 0.8647 | 0.9639 |
| 1.3396 | 2.0 | 500 | 0.8150 | 0.0346 | [0.6405353728489483, 0.46304347826086956, 0.3702770780856423, 0.2964071856287425] | 0.0815 | 0.2852 | 523 | 1834 | 0.7877 | 0.8990 |
| 0.8804 | 3.0 | 750 | 0.6424 | 0.0586 | [0.7463235294117647, 0.6486486486486487, 0.5933014354066986, 0.5408450704225352] | 0.0934 | 0.2966 | 544 | 1834 | 0.7674 | 0.8638 |
| 0.6436 | 4.0 | 1000 | 0.5823 | 0.0595 | [0.7332123411978222, 0.6290983606557377, 0.5741176470588235, 0.5248618784530387] | 0.0974 | 0.3004 | 551 | 1834 | 0.7739 | 0.8665 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.1.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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