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--- |
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license: mit |
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base_model: naver-clova-ix/donut-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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- wer |
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model-index: |
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- name: donut_experiment_bayesian_trial_13 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# donut_experiment_bayesian_trial_13 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5500 |
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- Bleu: 0.0682 |
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- Precisions: [0.8219461697722568, 0.755868544600939, 0.7073170731707317, 0.6474358974358975] |
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- Brevity Penalty: 0.0934 |
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- Length Ratio: 0.2967 |
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- Translation Length: 483 |
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- Reference Length: 1628 |
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- Cer: 0.7531 |
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- Wer: 0.8285 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6.0814226870239416e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 2 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| |
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| 0.0455 | 1.0 | 253 | 0.5262 | 0.0660 | [0.8166666666666667, 0.7446808510638298, 0.6939890710382514, 0.6407766990291263] | 0.0915 | 0.2948 | 480 | 1628 | 0.7601 | 0.8316 | |
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| 0.0276 | 2.0 | 506 | 0.5500 | 0.0682 | [0.8219461697722568, 0.755868544600939, 0.7073170731707317, 0.6474358974358975] | 0.0934 | 0.2967 | 483 | 1628 | 0.7531 | 0.8285 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.1.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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