| | --- |
| | license: mit |
| | base_model: naver-clova-ix/donut-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - bleu |
| | - wer |
| | model-index: |
| | - name: donut_experiment_bayesian_trial_16 |
| | 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_16 |
| |
|
| | 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: 0.5541 |
| | - Bleu: 0.0670 |
| | - Precisions: [0.8417721518987342, 0.7841726618705036, 0.7388888888888889, 0.6996699669966997] |
| | - Brevity Penalty: 0.0876 |
| | - Length Ratio: 0.2912 |
| | - Translation Length: 474 |
| | - Reference Length: 1628 |
| | - Cer: 0.7567 |
| | - Wer: 0.8224 |
| |
|
| | ## 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: 0.00011219603369833024 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| |
| | | 0.0965 | 1.0 | 253 | 0.5550 | 0.0624 | [0.7995824634655533, 0.7085308056872038, 0.6520547945205479, 0.6038961038961039] | 0.0908 | 0.2942 | 479 | 1628 | 0.7576 | 0.8347 | |
| | | 0.0844 | 2.0 | 506 | 0.5896 | 0.0651 | [0.8218029350104822, 0.7476190476190476, 0.696969696969697, 0.6535947712418301] | 0.0895 | 0.2930 | 477 | 1628 | 0.7557 | 0.8302 | |
| | | 0.0539 | 3.0 | 759 | 0.5594 | 0.0666 | [0.8322851153039832, 0.7642857142857142, 0.7134986225895317, 0.673202614379085] | 0.0895 | 0.2930 | 477 | 1628 | 0.7552 | 0.8223 | |
| | | 0.023 | 4.0 | 1012 | 0.5541 | 0.0670 | [0.8417721518987342, 0.7841726618705036, 0.7388888888888889, 0.6996699669966997] | 0.0876 | 0.2912 | 474 | 1628 | 0.7567 | 0.8224 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.0 |
| | - Pytorch 2.1.0 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.19.1 |
| | |
