---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- bleu
model-index:
- name: donut-base-sroie-v2
  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-v2

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: 0.1570
- Bleu: 0.0203
- Precisions: [0.9831401475237092, 0.9604966139954854, 0.9343863912515188, 0.9039473684210526]
- Brevity Penalty: 0.0215
- Length Ratio: 0.2067
- Translation Length: 949
- Reference Length: 4592

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
| No log        | 0.99  | 62   | 0.2030          | 0.0201 | [0.9789029535864979, 0.9559322033898305, 0.9294403892944039, 0.8985507246376812] | 0.0214          | 0.2064       | 948                | 4592             |
| 0.0922        | 2.0   | 125  | 0.1648          | 0.0207 | [0.9831932773109243, 0.9628796400449944, 0.9382566585956417, 0.90956749672346]   | 0.0219          | 0.2073       | 952                | 4592             |
| 0.0922        | 2.99  | 187  | 0.1572          | 0.0202 | [0.9831223628691983, 0.96045197740113, 0.9343065693430657, 0.9038208168642952]   | 0.0214          | 0.2064       | 948                | 4592             |
| 0.1224        | 3.97  | 248  | 0.1570          | 0.0203 | [0.9831401475237092, 0.9604966139954854, 0.9343863912515188, 0.9039473684210526] | 0.0215          | 0.2067       | 949                | 4592             |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2