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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_2 |
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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_2 |
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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: 0.3855 |
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- Bleu: 0.0663 |
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- Precisions: [0.8273684210526315, 0.7703349282296651, 0.7285318559556787, 0.6842105263157895] |
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- Brevity Penalty: 0.0883 |
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- Length Ratio: 0.2918 |
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- Translation Length: 475 |
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- Reference Length: 1628 |
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- Cer: 0.7539 |
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- Wer: 0.8251 |
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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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| 0.9517 | 1.0 | 253 | 0.5797 | 0.0543 | [0.7160751565762005, 0.6184834123222749, 0.5671232876712329, 0.5097402597402597] | 0.0908 | 0.2942 | 479 | 1628 | 0.7738 | 0.8500 | |
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| 0.3907 | 2.0 | 506 | 0.4532 | 0.0590 | [0.7851063829787234, 0.711864406779661, 0.6657303370786517, 0.6220735785953178] | 0.0851 | 0.2887 | 470 | 1628 | 0.7610 | 0.8370 | |
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| 0.3245 | 3.0 | 759 | 0.4102 | 0.0625 | [0.8008474576271186, 0.7397590361445783, 0.7011173184357542, 0.6611295681063123] | 0.0864 | 0.2899 | 472 | 1628 | 0.7593 | 0.8336 | |
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| 0.2318 | 4.0 | 1012 | 0.3855 | 0.0663 | [0.8273684210526315, 0.7703349282296651, 0.7285318559556787, 0.6842105263157895] | 0.0883 | 0.2918 | 475 | 1628 | 0.7539 | 0.8251 | |
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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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