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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_7 |
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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_7 |
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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: 0.3786 |
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- Bleu: 0.0669 |
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- Precisions: [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332] |
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- Brevity Penalty: 0.0870 |
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- Length Ratio: 0.2905 |
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- Translation Length: 473 |
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- Reference Length: 1628 |
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- Cer: 0.7532 |
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- Wer: 0.8192 |
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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: 3.540464175534869e-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: 5 |
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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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| 0.5323 | 1.0 | 253 | 0.4204 | 0.0580 | [0.7710084033613446, 0.6778042959427207, 0.6132596685082873, 0.5639344262295082] | 0.0889 | 0.2924 | 476 | 1628 | 0.7617 | 0.8431 | |
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| 0.2487 | 2.0 | 506 | 0.3788 | 0.0609 | [0.8123667377398721, 0.7402912621359223, 0.6929577464788732, 0.6476510067114094] | 0.0845 | 0.2881 | 469 | 1628 | 0.7561 | 0.8279 | |
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| 0.1746 | 3.0 | 759 | 0.3551 | 0.0652 | [0.836864406779661, 0.7759036144578313, 0.729050279329609, 0.6843853820598007] | 0.0864 | 0.2899 | 472 | 1628 | 0.7541 | 0.8213 | |
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| 0.1191 | 4.0 | 1012 | 0.3690 | 0.0680 | [0.8547368421052631, 0.784688995215311, 0.7451523545706371, 0.7039473684210527] | 0.0883 | 0.2918 | 475 | 1628 | 0.7514 | 0.8192 | |
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| 0.1072 | 5.0 | 1265 | 0.3786 | 0.0669 | [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332] | 0.0870 | 0.2905 | 473 | 1628 | 0.7532 | 0.8192 | |
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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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