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