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trocr-small-printedkorean-deleteunusedchar_noise
yumjunstar/trocr-small-printedkorean-deleteunusedchar_noise
trocr-small-printedkorean-deleteunusedchar_noise is a image-text-to-text model from yumjunstar. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Aug 29, 2023
- Updated
- Aug 30, 2023
- SHA
- 521a66e9
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
trocr-small-printedkorean-deleteunusedchar_noise
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3375
- Cer: 0.2783
- Wer: 0.2975
- Accuracy: 45.6667
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 128
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer | Accuracy |
|---|---|---|---|---|---|---|
| 1.711 | 0.43 | 1000 | 1.6485 | 0.3288 | 0.3944 | 30.6667 |
| 1.6849 | 0.85 | 2000 | 1.5361 | 0.3098 | 0.3809 | 32.3333 |
| 1.4933 | 1.28 | 3000 | 1.4302 | 0.2935 | 0.3533 | 34.6667 |
| 1.526 | 1.71 | 4000 | 1.4010 | 0.2922 | 0.3400 | 35.6667 |
| 1.3422 | 2.13 | 5000 | 1.3883 | 0.2846 | 0.3331 | 36.0 |
| 1.333 | 2.56 | 6000 | 1.3790 | 0.2871 | 0.3308 | 34.0 |
| 1.3295 | 2.99 | 7000 | 1.3644 | 0.2876 | 0.3294 | 35.6667 |
| 1.3294 | 3.42 | 8000 | 1.3588 | 0.2824 | 0.3202 | 36.6667 |
| 1.3578 | 3.84 | 9000 | 1.3502 | 0.2823 | 0.3162 | 40.6667 |
| 1.3029 | 4.27 | 10000 | 1.3514 | 0.2879 | 0.3228 | 37.0 |
| 1.2777 | 4.7 | 11000 | 1.3507 | 0.2813 | 0.3168 | 38.3333 |
| 1.1781 | 5.12 | 12000 | 1.3507 | 0.2791 | 0.3150 | 40.3333 |
| 1.3025 | 5.55 | 13000 | 1.3459 | 0.2818 | 0.3099 | 41.6667 |
| 1.2024 | 5.98 | 14000 | 1.3401 | 0.2801 | 0.3061 | 41.6667 |
| 1.1792 | 6.4 | 15000 | 1.3412 | 0.2763 | 0.3015 | 44.6667 |
| 1.1586 | 6.83 | 16000 | 1.3410 | 0.2799 | 0.3064 | 43.3333 |
| 1.2098 | 7.26 | 17000 | 1.3439 | 0.2777 | 0.3030 | 43.6667 |
| 1.2122 | 7.69 | 18000 | 1.3418 | 0.2816 | 0.3050 | 43.3333 |
| 1.1323 | 8.11 | 19000 | 1.3409 | 0.2767 | 0.2981 | 45.3333 |
| 1.2215 | 8.54 | 20000 | 1.3386 | 0.2781 | 0.3004 | 44.0 |
| 1.2068 | 8.97 | 21000 | 1.3375 | 0.2762 | 0.2972 | 45.0 |
| 1.0847 | 9.39 | 22000 | 1.3366 | 0.2765 | 0.2969 | 46.0 |
| 1.1791 | 9.82 | 23000 | 1.3375 | 0.2783 | 0.2975 | 45.6667 |
Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu116
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Aug 29, 2023
- Updated
- Aug 30, 2023