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-fixed-version
  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-fixed-version

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: 0.4187
- Bleu: 0.0659
- Precisions: [0.8259958071278826, 0.7547619047619047, 0.7107438016528925, 0.6601307189542484]
- Brevity Penalty: 0.0895
- Length Ratio: 0.2930
- Translation Length: 477
- Reference Length: 1628
- Cer: 0.7531
- Wer: 0.8260

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.9081        | 1.0   | 253  | 0.5714          | 0.0513 | [0.7483731019522777, 0.6584158415841584, 0.6138328530259366, 0.5724137931034483] | 0.0795          | 0.2832       | 461                | 1628             | 0.7796 | 0.8572 |
| 0.3952        | 2.0   | 506  | 0.4489          | 0.0579 | [0.7913978494623656, 0.7254901960784313, 0.6809116809116809, 0.6360544217687075] | 0.0820          | 0.2856       | 465                | 1628             | 0.7632 | 0.8394 |
| 0.3077        | 3.0   | 759  | 0.4266          | 0.0666 | [0.8218029350104822, 0.7642857142857142, 0.721763085399449, 0.673202614379085]   | 0.0895          | 0.2930       | 477                | 1628             | 0.7556 | 0.8273 |
| 0.2307        | 4.0   | 1012 | 0.4187          | 0.0659 | [0.8259958071278826, 0.7547619047619047, 0.7107438016528925, 0.6601307189542484] | 0.0895          | 0.2930       | 477                | 1628             | 0.7531 | 0.8260 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1
