transformers tensorboard safetensors vision-encoder-decoder generated_from_trainer dataset:imagefolder base_model:naver-clova-ix/donut-base license:mit endpoints_compatible region:us
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- bleu
- wer
model-index:
- name: donut-base-sroie-v3
  results: []
---

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# donut-base-sroie-v3

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: 1.7091
- Bleu: 0.0045
- Precisions: [0.32051282051282054, 0.10617283950617284, 0.043859649122807015, 0.02867383512544803]
- Brevity Penalty: 0.0555
- Length Ratio: 0.2570
- Translation Length: 468
- Reference Length: 1821
- Cer: 0.8657
- Wer: 0.9978

## 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   | Precisions                                                                            | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer    | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| No log        | 0.99  | 62   | 1.7048          | 0.0026 | [0.3287981859410431, 0.09259259259259259, 0.025396825396825397, 0.015873015873015872] | 0.0438          | 0.2422       | 441                | 1821             | 0.8729 | 1.0    |
| 0.6536        | 2.0   | 125  | 1.7425          | 0.0035 | [0.32051282051282054, 0.0962962962962963, 0.029239766081871343, 0.017921146953405017] | 0.0555          | 0.2570       | 468                | 1821             | 0.8701 | 0.9986 |
| 0.6536        | 2.99  | 187  | 1.6949          | 0.0038 | [0.3148936170212766, 0.09582309582309582, 0.03197674418604651, 0.021352313167259787]  | 0.0564          | 0.2581       | 470                | 1821             | 0.8670 | 0.9978 |
| 0.6585        | 3.97  | 248  | 1.7091          | 0.0045 | [0.32051282051282054, 0.10617283950617284, 0.043859649122807015, 0.02867383512544803] | 0.0555          | 0.2570       | 468                | 1821             | 0.8657 | 0.9978 |


### Framework versions

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