transformers tensorboard safetensors vision-encoder-decoder image-text-to-text generated_from_trainer dataset:imagefolder base_model:naver-clova-ix/donut-base base_model:finetune:naver-clova-ix/donut-base license:mit endpoints_compatible region:us
---
base_model: naver-clova-ix/donut-base
datasets:
- imagefolder
license: mit
metrics:
- bleu
- wer
tags:
- generated_from_trainer
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
