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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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- accuracy |
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model-index: |
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- name: trocr-small-printedkorean-deleteunusedchar_noise |
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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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# trocr-small-printedkorean-deleteunusedchar_noise |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3375 |
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- Cer: 0.2783 |
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- Wer: 0.2975 |
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- Accuracy: 45.6667 |
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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: 4e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 192 |
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- seed: 42 |
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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: 10 |
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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 | Cer | Wer | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:--------:| |
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| 1.711 | 0.43 | 1000 | 1.6485 | 0.3288 | 0.3944 | 30.6667 | |
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| 1.6849 | 0.85 | 2000 | 1.5361 | 0.3098 | 0.3809 | 32.3333 | |
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| 1.4933 | 1.28 | 3000 | 1.4302 | 0.2935 | 0.3533 | 34.6667 | |
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| 1.526 | 1.71 | 4000 | 1.4010 | 0.2922 | 0.3400 | 35.6667 | |
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| 1.3422 | 2.13 | 5000 | 1.3883 | 0.2846 | 0.3331 | 36.0 | |
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| 1.333 | 2.56 | 6000 | 1.3790 | 0.2871 | 0.3308 | 34.0 | |
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| 1.3295 | 2.99 | 7000 | 1.3644 | 0.2876 | 0.3294 | 35.6667 | |
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| 1.3294 | 3.42 | 8000 | 1.3588 | 0.2824 | 0.3202 | 36.6667 | |
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| 1.3578 | 3.84 | 9000 | 1.3502 | 0.2823 | 0.3162 | 40.6667 | |
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| 1.3029 | 4.27 | 10000 | 1.3514 | 0.2879 | 0.3228 | 37.0 | |
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| 1.2777 | 4.7 | 11000 | 1.3507 | 0.2813 | 0.3168 | 38.3333 | |
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| 1.1781 | 5.12 | 12000 | 1.3507 | 0.2791 | 0.3150 | 40.3333 | |
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| 1.3025 | 5.55 | 13000 | 1.3459 | 0.2818 | 0.3099 | 41.6667 | |
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| 1.2024 | 5.98 | 14000 | 1.3401 | 0.2801 | 0.3061 | 41.6667 | |
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| 1.1792 | 6.4 | 15000 | 1.3412 | 0.2763 | 0.3015 | 44.6667 | |
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| 1.1586 | 6.83 | 16000 | 1.3410 | 0.2799 | 0.3064 | 43.3333 | |
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| 1.2098 | 7.26 | 17000 | 1.3439 | 0.2777 | 0.3030 | 43.6667 | |
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| 1.2122 | 7.69 | 18000 | 1.3418 | 0.2816 | 0.3050 | 43.3333 | |
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| 1.1323 | 8.11 | 19000 | 1.3409 | 0.2767 | 0.2981 | 45.3333 | |
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| 1.2215 | 8.54 | 20000 | 1.3386 | 0.2781 | 0.3004 | 44.0 | |
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| 1.2068 | 8.97 | 21000 | 1.3375 | 0.2762 | 0.2972 | 45.0 | |
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| 1.0847 | 9.39 | 22000 | 1.3366 | 0.2765 | 0.2969 | 46.0 | |
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| 1.1791 | 9.82 | 23000 | 1.3375 | 0.2783 | 0.2975 | 45.6667 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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