---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- wer
model-index:
- name: donut-base-sroie-metrics-combined
  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

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: 0.2827
- Bleu score: 0.0762
- Precisions: [0.8396396396396396, 0.7886178861788617, 0.7435897435897436, 0.7049180327868853]
- Brevity penalty: 0.0993
- Length ratio: 0.3021
- Translation length: 555
- Reference length: 1837
- Cer: 0.7452
- Wer: 0.8162
- Cer Hugging Face: 0.7544
- Wer Hugging Face: 0.8233

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 score | Precisions                                                                       | Brevity penalty | Length ratio | Translation length | Reference length | Cer    | Wer    | Cer Hugging Face | Wer Hugging Face |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|:----------------:|:----------------:|
| No log        | 0.99  | 62   | 0.3478          | 0.0756     | [0.8178571428571428, 0.7625754527162978, 0.7142857142857143, 0.6711590296495957] | 0.1022          | 0.3048       | 560                | 1837             | 0.7474 | 0.8243 | 0.7570           | 0.8333           |
| 0.2634        | 2.0   | 125  | 0.2873          | 0.0763     | [0.8345323741007195, 0.7829614604462475, 0.7418604651162791, 0.7029972752043597] | 0.0999          | 0.3027       | 556                | 1837             | 0.7435 | 0.8219 | 0.7527           | 0.8288           |
| 0.2634        | 2.99  | 187  | 0.2817          | 0.0777     | [0.8369175627240143, 0.7838383838383839, 0.7476851851851852, 0.7127371273712737] | 0.1011          | 0.3038       | 558                | 1837             | 0.7407 | 0.8152 | 0.7498           | 0.8215           |
| 0.263         | 3.97  | 248  | 0.2827          | 0.0762     | [0.8396396396396396, 0.7886178861788617, 0.7435897435897436, 0.7049180327868853] | 0.0993          | 0.3021       | 555                | 1837             | 0.7452 | 0.8162 | 0.7544           | 0.8233           |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.15.2