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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_6 |
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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_6 |
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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: 0.5515 |
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- Bleu: 0.0683 |
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- Precisions: [0.8127572016460906, 0.7412587412587412, 0.6854838709677419, 0.638095238095238] |
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- Brevity Penalty: 0.0954 |
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- Length Ratio: 0.2985 |
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- Translation Length: 486 |
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- Reference Length: 1628 |
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- Cer: 0.7532 |
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- Wer: 0.8274 |
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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: 0.00016063260663724173 |
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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: 4 |
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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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| 0.3276 | 1.0 | 253 | 0.6672 | 0.0589 | [0.76875, 0.6737588652482269, 0.6092896174863388, 0.5436893203883495] | 0.0915 | 0.2948 | 480 | 1628 | 0.7586 | 0.8473 | |
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| 0.2008 | 2.0 | 506 | 0.5780 | 0.0662 | [0.7905544147843943, 0.7069767441860465, 0.6595174262734584, 0.6107594936708861] | 0.0960 | 0.2991 | 487 | 1628 | 0.7559 | 0.8374 | |
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| 0.1356 | 3.0 | 759 | 0.5355 | 0.0651 | [0.8238993710691824, 0.7452380952380953, 0.6942148760330579, 0.6535947712418301] | 0.0895 | 0.2930 | 477 | 1628 | 0.7580 | 0.8299 | |
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| 0.0394 | 4.0 | 1012 | 0.5515 | 0.0683 | [0.8127572016460906, 0.7412587412587412, 0.6854838709677419, 0.638095238095238] | 0.0954 | 0.2985 | 486 | 1628 | 0.7532 | 0.8274 | |
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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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