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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-base-sroie-metrics-combined-new |
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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-base-sroie-metrics-combined-new |
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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: 0.4671 |
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- Bleu: 0.0662 |
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- Precisions: [0.785140562248996, 0.6825396825396826, 0.6197916666666666, 0.5626911314984709] |
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- Brevity Penalty: 0.1007 |
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- Length Ratio: 0.3035 |
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- Translation Length: 498 |
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- Reference Length: 1641 |
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- Cer: 0.7528 |
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- Wer: 0.8385 |
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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: 2e-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: 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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| 3.6559 | 1.0 | 253 | 1.5613 | 0.0007 | [0.5056179775280899, 0.1943127962085308, 0.07692307692307693, 0.02830188679245283] | 0.0058 | 0.1627 | 267 | 1641 | 0.8768 | 0.9436 | |
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| 1.2493 | 2.0 | 506 | 0.6697 | 0.0409 | [0.6560509554140127, 0.5048309178743962, 0.4481792717086835, 0.39] | 0.0834 | 0.2870 | 471 | 1641 | 0.7766 | 0.8837 | |
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| 0.9257 | 3.0 | 759 | 0.5168 | 0.0594 | [0.75, 0.6275862068965518, 0.5714285714285714, 0.5264797507788161] | 0.0968 | 0.2998 | 492 | 1641 | 0.7570 | 0.8499 | |
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| 0.6416 | 4.0 | 1012 | 0.4671 | 0.0662 | [0.785140562248996, 0.6825396825396826, 0.6197916666666666, 0.5626911314984709] | 0.1007 | 0.3035 | 498 | 1641 | 0.7528 | 0.8385 | |
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### Framework versions |
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- Transformers 4.41.0.dev0 |
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- Pytorch 2.1.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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