| | --- |
| | license: mit |
| | base_model: naver-clova-ix/donut-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - bleu |
| | - wer |
| | model-index: |
| | - name: donut_experiment_bayesian_trial_20 |
| | 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_20 |
| |
|
| | 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: 0.6042 |
| | - Bleu: 0.0711 |
| | - Precisions: [0.8440748440748441, 0.7924528301886793, 0.7493188010899182, 0.7064516129032258] |
| | - Brevity Penalty: 0.0921 |
| | - Length Ratio: 0.2955 |
| | - Translation Length: 481 |
| | - Reference Length: 1628 |
| | - Cer: 0.7518 |
| | - Wer: 0.8208 |
| |
|
| | ## 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: 1.4151037707088747e-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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| |
| | | 0.0012 | 1.0 | 253 | 0.6392 | 0.0724 | [0.8333333333333334, 0.7762237762237763, 0.7365591397849462, 0.6952380952380952] | 0.0954 | 0.2985 | 486 | 1628 | 0.7482 | 0.8193 | |
| | | 0.0068 | 2.0 | 506 | 0.6212 | 0.0697 | [0.8413361169102297, 0.7867298578199052, 0.7452054794520548, 0.7012987012987013] | 0.0908 | 0.2942 | 479 | 1628 | 0.7527 | 0.8230 | |
| | | 0.0087 | 3.0 | 759 | 0.6105 | 0.0687 | [0.83125, 0.7706855791962175, 0.726775956284153, 0.6828478964401294] | 0.0915 | 0.2948 | 480 | 1628 | 0.7573 | 0.8282 | |
| | | 0.0056 | 4.0 | 1012 | 0.6042 | 0.0711 | [0.8440748440748441, 0.7924528301886793, 0.7493188010899182, 0.7064516129032258] | 0.0921 | 0.2955 | 481 | 1628 | 0.7518 | 0.8208 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.0 |
| | - Pytorch 2.1.0 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.19.1 |
| | |
