| | --- |
| | license: mit |
| | base_model: naver-clova-ix/donut-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - bleu |
| | - wer |
| | model-index: |
| | - name: donut_experiment_bayesian_trial_10 |
| | 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_10 |
| |
|
| | 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: 0.4219 |
| | - Bleu: 0.0632 |
| | - Precisions: [0.809322033898305, 0.7493975903614458, 0.7094972067039106, 0.6644518272425249] |
| | - Brevity Penalty: 0.0864 |
| | - Length Ratio: 0.2899 |
| | - Translation Length: 472 |
| | - Reference Length: 1628 |
| | - Cer: 0.7596 |
| | - Wer: 0.8312 |
| |
|
| | ## 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: 1.0082458996730595e-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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| |
| | | 0.3888 | 1.0 | 253 | 0.5110 | 0.0673 | [0.7909836065573771, 0.7099767981438515, 0.6657754010695187, 0.6277602523659306] | 0.0967 | 0.2998 | 488 | 1628 | 0.7690 | 0.8412 | |
| | | 0.326 | 2.0 | 506 | 0.4539 | 0.0654 | [0.7908902691511387, 0.7276995305164319, 0.6775067750677507, 0.6153846153846154] | 0.0934 | 0.2967 | 483 | 1628 | 0.7604 | 0.8362 | |
| | | 0.3191 | 3.0 | 759 | 0.4256 | 0.0654 | [0.7837837837837838, 0.7287735849056604, 0.6893732970027248, 0.6451612903225806] | 0.0921 | 0.2955 | 481 | 1628 | 0.7599 | 0.8331 | |
| | | 0.2632 | 4.0 | 1012 | 0.4219 | 0.0632 | [0.809322033898305, 0.7493975903614458, 0.7094972067039106, 0.6644518272425249] | 0.0864 | 0.2899 | 472 | 1628 | 0.7596 | 0.8312 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.0 |
| | - Pytorch 2.1.0 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.19.1 |
| | |
