transformers tensorboard safetensors vision-encoder-decoder image-text-to-text generated_from_trainer 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
license: mit
metrics:
- bleu
- wer
tags:
- generated_from_trainer
model-index:
- name: donut-base-sroie-bayesian-optimization
  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-bayesian-optimization

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: 0.1396
- Bleu: 0.0196
- Precisions: [0.9883177570093458, 0.9724655819774718, 0.954177897574124, 0.9328467153284672]
- Brevity Penalty: 0.0203
- Length Ratio: 0.2043
- Translation Length: 856
- Reference Length: 4190
- Cer: 0.8584
- Wer: 1.0

## 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: 1.2010406976282324e-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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:---:|
| 0.021         | 1.0   | 253  | 0.1656          | 0.0194 | [0.9848130841121495, 0.9649561952440551, 0.9420485175202157, 0.9153284671532846] | 0.0203          | 0.2043       | 856                | 4190             | 0.8596 | 1.0 |
| 0.0353        | 2.0   | 506  | 0.1501          | 0.0195 | [0.9813736903376019, 0.9588528678304239, 0.9328859060402684, 0.9026162790697675] | 0.0207          | 0.2050       | 859                | 4190             | 0.8595 | 1.0 |
| 0.0417        | 3.0   | 759  | 0.1423          | 0.0195 | [0.9871495327102804, 0.9699624530663329, 0.9501347708894878, 0.927007299270073]  | 0.0203          | 0.2043       | 856                | 4190             | 0.8586 | 1.0 |
| 0.0308        | 4.0   | 1012 | 0.1403          | 0.0193 | [0.9859649122807017, 0.9674185463659147, 0.9460188933873145, 0.9210526315789473] | 0.0202          | 0.2041       | 855                | 4190             | 0.8593 | 1.0 |
| 0.0464        | 5.0   | 1265 | 0.1396          | 0.0196 | [0.9883177570093458, 0.9724655819774718, 0.954177897574124, 0.9328467153284672]  | 0.0203          | 0.2043       | 856                | 4190             | 0.8584 | 1.0 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.1.0
- Datasets 2.19.0
- Tokenizers 0.19.1
