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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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datasets: |
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- imagefolder |
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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-test-050824 |
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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-test-050824 |
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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 imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5823 |
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- Bleu: 0.0595 |
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- Precisions: [0.7332123411978222, 0.6290983606557377, 0.5741176470588235, 0.5248618784530387] |
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- Brevity Penalty: 0.0974 |
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- Length Ratio: 0.3004 |
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- Translation Length: 551 |
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- Reference Length: 1834 |
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- Cer: 0.7739 |
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- Wer: 0.8665 |
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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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| 3.716 | 1.0 | 250 | 1.7346 | 0.0080 | [0.3276089828269485, 0.05475504322766571, 0.01901743264659271, 0.0035211267605633804] | 0.2411 | 0.4128 | 757 | 1834 | 0.8647 | 0.9639 | |
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| 1.3396 | 2.0 | 500 | 0.8150 | 0.0346 | [0.6405353728489483, 0.46304347826086956, 0.3702770780856423, 0.2964071856287425] | 0.0815 | 0.2852 | 523 | 1834 | 0.7877 | 0.8990 | |
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| 0.8804 | 3.0 | 750 | 0.6424 | 0.0586 | [0.7463235294117647, 0.6486486486486487, 0.5933014354066986, 0.5408450704225352] | 0.0934 | 0.2966 | 544 | 1834 | 0.7674 | 0.8638 | |
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| 0.6436 | 4.0 | 1000 | 0.5823 | 0.0595 | [0.7332123411978222, 0.6290983606557377, 0.5741176470588235, 0.5248618784530387] | 0.0974 | 0.3004 | 551 | 1834 | 0.7739 | 0.8665 | |
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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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