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_experiment_bayesian_trial_17
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# donut_experiment_bayesian_trial_17

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: 0.4635
- Bleu: 0.0675
- Precisions: [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307]
- Brevity Penalty: 0.0895
- Length Ratio: 0.2930
- Translation Length: 477
- Reference Length: 1628
- Cer: 0.7603
- Wer: 0.8297

## 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: 0.00018015728878154226
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.8044        | 1.0   | 253  | 0.7112          | 0.0610 | [0.7535641547861507, 0.6497695852534562, 0.5809018567639257, 0.5125]             | 0.0987          | 0.3016       | 491                | 1628             | 0.7647 | 0.8548 |
| 0.3513        | 2.0   | 506  | 0.5640          | 0.0632 | [0.7908902691511387, 0.7089201877934272, 0.6449864498644986, 0.5801282051282052] | 0.0934          | 0.2967       | 483                | 1628             | 0.7549 | 0.8416 |
| 0.2101        | 3.0   | 759  | 0.4754          | 0.0666 | [0.8198757763975155, 0.744131455399061, 0.6802168021680217, 0.6217948717948718]  | 0.0934          | 0.2967       | 483                | 1628             | 0.7508 | 0.8282 |
| 0.0756        | 4.0   | 1012 | 0.4635          | 0.0675 | [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307] | 0.0895          | 0.2930       | 477                | 1628             | 0.7603 | 0.8297 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1
