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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_5 |
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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_5 |
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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: 0.3987 |
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- Bleu: 0.0661 |
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- Precisions: [0.8020833333333334, 0.7375886524822695, 0.6994535519125683, 0.6601941747572816] |
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- Brevity Penalty: 0.0915 |
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- Length Ratio: 0.2948 |
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- Translation Length: 480 |
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- Reference Length: 1628 |
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- Cer: 0.7576 |
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- Wer: 0.8280 |
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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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| 0.3274 | 1.0 | 253 | 0.4698 | 0.0586 | [0.7707006369426752, 0.6956521739130435, 0.6582633053221288, 0.62] | 0.0857 | 0.2893 | 471 | 1628 | 0.7660 | 0.8432 | |
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| 0.2539 | 2.0 | 506 | 0.4198 | 0.0643 | [0.799163179916318, 0.7315914489311164, 0.6868131868131868, 0.6416938110749185] | 0.0902 | 0.2936 | 478 | 1628 | 0.7605 | 0.8313 | |
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| 0.224 | 3.0 | 759 | 0.3941 | 0.0658 | [0.8075313807531381, 0.7387173396674585, 0.7060439560439561, 0.6710097719869706] | 0.0902 | 0.2936 | 478 | 1628 | 0.7573 | 0.8283 | |
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| 0.1566 | 4.0 | 1012 | 0.3987 | 0.0661 | [0.8020833333333334, 0.7375886524822695, 0.6994535519125683, 0.6601941747572816] | 0.0915 | 0.2948 | 480 | 1628 | 0.7576 | 0.8280 | |
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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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