| | --- |
| | license: mit |
| | base_model: naver-clova-ix/donut-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - bleu |
| | - wer |
| | model-index: |
| | - name: donut_experiment_bayesian_trial_3 |
| | 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_3 |
| |
|
| | 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: 0.5840 |
| | - Bleu: 0.0667 |
| | - Precisions: [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846] |
| | - Brevity Penalty: 0.0934 |
| | - Length Ratio: 0.2967 |
| | - Translation Length: 483 |
| | - Reference Length: 1628 |
| | - Cer: 0.7599 |
| | - Wer: 0.8328 |
| |
|
| | ## 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: 0.00017060423589132634 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| |
| | | 0.2012 | 1.0 | 253 | 0.6694 | 0.0547 | [0.7680851063829788, 0.6682808716707022, 0.6067415730337079, 0.5484949832775919] | 0.0851 | 0.2887 | 470 | 1628 | 0.7597 | 0.8411 | |
| | | 0.127 | 2.0 | 506 | 0.6071 | 0.0638 | [0.7818930041152263, 0.6876456876456877, 0.6370967741935484, 0.5841269841269842] | 0.0954 | 0.2985 | 486 | 1628 | 0.7570 | 0.8360 | |
| | | 0.0766 | 3.0 | 759 | 0.5786 | 0.0655 | [0.8125, 0.735224586288416, 0.6885245901639344, 0.6407766990291263] | 0.0915 | 0.2948 | 480 | 1628 | 0.7564 | 0.8319 | |
| | | 0.0259 | 4.0 | 1012 | 0.5840 | 0.0667 | [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846] | 0.0934 | 0.2967 | 483 | 1628 | 0.7599 | 0.8328 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.0 |
| | - Pytorch 2.1.0 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.19.1 |
| | |
