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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
- accuracy
model-index:
- name: trocr-small-printedkorean-deleteunusedchar
results: []
---
<!-- 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
This model is a fine-tuned version of [team-lucid/trocr-small-korean](https://huggingface.co/team-lucid/trocr-small-korean) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1253
- Cer: 0.3884
- Wer: 0.4405
- Accuracy: 27.0903
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:--------:|
| 3.5404 | 0.43 | 1000 | 3.5688 | 0.5737 | 0.6446 | 17.7258 |
| 3.365 | 0.85 | 2000 | 3.4212 | 0.5664 | 0.6546 | 18.0602 |
| 3.266 | 1.28 | 3000 | 3.3538 | 0.5637 | 0.6815 | 18.7291 |
| 3.2922 | 1.71 | 4000 | 3.3051 | 0.5613 | 0.7161 | 18.0602 |
| 3.1287 | 2.13 | 5000 | 3.2649 | 0.5577 | 0.7164 | 18.7291 |
| 3.0906 | 2.56 | 6000 | 3.2016 | 0.5572 | 0.6139 | 18.3946 |
| 3.0015 | 2.99 | 7000 | 3.0851 | 0.5530 | 0.6018 | 18.3946 |
| 2.7418 | 3.42 | 8000 | 2.8927 | 0.5264 | 0.6052 | 19.3980 |
| 2.5107 | 3.84 | 9000 | 2.6466 | 0.4821 | 0.5881 | 19.3980 |
| 2.3091 | 4.27 | 10000 | 2.4865 | 0.4545 | 0.5651 | 20.4013 |
| 2.1395 | 4.7 | 11000 | 2.3749 | 0.4336 | 0.5323 | 21.7391 |
| 2.0453 | 5.12 | 12000 | 2.3070 | 0.4222 | 0.5157 | 21.7391 |
| 2.0421 | 5.55 | 13000 | 2.2667 | 0.4143 | 0.5001 | 24.0803 |
| 2.0085 | 5.98 | 14000 | 2.2201 | 0.4060 | 0.4809 | 24.0803 |
| 1.8766 | 6.4 | 15000 | 2.1988 | 0.4026 | 0.4766 | 23.7458 |
| 1.8987 | 6.83 | 16000 | 2.1788 | 0.3969 | 0.4658 | 24.4147 |
| 1.8921 | 7.26 | 17000 | 2.1702 | 0.3955 | 0.4581 | 24.4147 |
| 1.8253 | 7.69 | 18000 | 2.1554 | 0.3938 | 0.4513 | 26.7559 |
| 1.8281 | 8.11 | 19000 | 2.1481 | 0.3925 | 0.4534 | 26.0870 |
| 1.8751 | 8.54 | 20000 | 2.1419 | 0.3905 | 0.4500 | 26.0870 |
| 1.8226 | 8.97 | 21000 | 2.1317 | 0.3905 | 0.4439 | 26.7559 |
| 1.813 | 9.39 | 22000 | 2.1285 | 0.3895 | 0.4407 | 26.0870 |
| 1.7472 | 9.82 | 23000 | 2.1253 | 0.3884 | 0.4405 | 27.0903 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu116
- Datasets 2.14.4
- Tokenizers 0.13.3
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