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-metrics-combined-new-instance-050824
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# donut-base-sroie-metrics-combined-new-instance-050824

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: 0.6594
- Bleu: 0.0554
- Precisions: [0.7702970297029703, 0.671875, 0.6061381074168798, 0.5538922155688623]
- Brevity Penalty: 0.0858
- Length Ratio: 0.2894
- Translation Length: 505
- Reference Length: 1745
- Cer: 0.7675
- Wer: 0.8512

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 3.4087        | 1.0   | 253  | 1.6485          | 0.0082 | [0.4555984555984556, 0.14316702819956617, 0.06188118811881188, 0.01440922190201729] | 0.0936          | 0.2968       | 518                | 1745             | 0.8353 | 0.9368 |
| 1.1999        | 2.0   | 506  | 0.8976          | 0.0274 | [0.6748878923766816, 0.5347043701799485, 0.4578313253012048, 0.3890909090909091]    | 0.0543          | 0.2556       | 446                | 1745             | 0.7818 | 0.8754 |
| 0.8013        | 3.0   | 759  | 0.7295          | 0.0508 | [0.7580645161290323, 0.6583143507972665, 0.5890052356020943, 0.5384615384615384]    | 0.0806          | 0.2842       | 496                | 1745             | 0.7629 | 0.8557 |
| 0.6404        | 4.0   | 1012 | 0.6594          | 0.0554 | [0.7702970297029703, 0.671875, 0.6061381074168798, 0.5538922155688623]              | 0.0858          | 0.2894       | 505                | 1745             | 0.7675 | 0.8512 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.1.0
- Datasets 2.19.1
- Tokenizers 0.19.1
