---
tags:
- generated_from_trainer
metrics:
- wer
- accuracy
model-index:
- name: trocr-small-printedkorean-deleteunusedchar_noise
  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_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