| --- |
| license: mit |
| base_model: naver-clova-ix/donut-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| - wer |
| 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 |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # 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 |
| |
