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_15
  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_15

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: 0.5777
- Bleu: 0.0659
- Precisions: [0.8158995815899581, 0.7434679334916865, 0.7060439560439561, 0.6644951140065146]
- Brevity Penalty: 0.0902
- Length Ratio: 0.2936
- Translation Length: 478
- Reference Length: 1628
- Cer: 0.7557
- Wer: 0.8239

## 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: 2.349414650597281e-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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.0066        | 1.0   | 253  | 0.5790          | 0.0648 | [0.8305084745762712, 0.7686746987951807, 0.7262569832402235, 0.6843853820598007] | 0.0864          | 0.2899       | 472                | 1628             | 0.7593 | 0.8258 |
| 0.0143        | 2.0   | 506  | 0.5824          | 0.0663 | [0.8225469728601252, 0.7511848341232228, 0.7041095890410959, 0.6525974025974026] | 0.0908          | 0.2942       | 479                | 1628             | 0.7577 | 0.8265 |
| 0.009         | 3.0   | 759  | 0.5826          | 0.0640 | [0.8185654008438819, 0.7458033573141487, 0.7055555555555556, 0.6600660066006601] | 0.0876          | 0.2912       | 474                | 1628             | 0.7553 | 0.8248 |
| 0.0103        | 4.0   | 1012 | 0.5777          | 0.0659 | [0.8158995815899581, 0.7434679334916865, 0.7060439560439561, 0.6644951140065146] | 0.0902          | 0.2936       | 478                | 1628             | 0.7557 | 0.8239 |


### Framework versions

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