---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut-base-sroie-metrics-combined-new-instance-050824-NUM-01
  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-metrics-combined-new-instance-050824-NUM-01

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: 0.4660
- Bleu: 0.0647
- Precisions: [0.8446215139442231, 0.7842696629213484, 0.7422680412371134, 0.7129909365558912]
- Brevity Penalty: 0.0841
- Length Ratio: 0.2877
- Translation Length: 502
- Reference Length: 1745
- Cer: 0.7571
- Wer: 0.8220

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.8919        | 1.0   | 253  | 0.6234          | 0.0484 | [0.7540983606557377, 0.6450116009280742, 0.5962566844919787, 0.5646687697160884] | 0.0761          | 0.2797       | 488                | 1745             | 0.7741 | 0.8528 |
| 0.4361        | 2.0   | 506  | 0.5208          | 0.0584 | [0.8161616161616162, 0.7442922374429224, 0.7007874015748031, 0.6635802469135802] | 0.0800          | 0.2837       | 495                | 1745             | 0.7647 | 0.8335 |
| 0.2804        | 3.0   | 759  | 0.4764          | 0.0608 | [0.8481781376518218, 0.7803203661327232, 0.7394736842105263, 0.6996904024767802] | 0.0795          | 0.2831       | 494                | 1745             | 0.7568 | 0.8211 |
| 0.2261        | 4.0   | 1012 | 0.4660          | 0.0647 | [0.8446215139442231, 0.7842696629213484, 0.7422680412371134, 0.7129909365558912] | 0.0841          | 0.2877       | 502                | 1745             | 0.7571 | 0.8220 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.1.0
- Datasets 2.19.1
- Tokenizers 0.19.1