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---
library_name: transformers
base_model: klue/roberta-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-small-hangul-2-hanja
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. -->
# roberta-small-hangul-2-hanja
This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9918
- F1: 0.9824
- Loss: 0.0868
- Precision: 0.9814
- Recall: 0.9835
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| No log | 1.0 | 482 | 0.8993 | 0.4607 | 0.8157 | 0.6392 | 0.3602 |
| 1.7229 | 2.0 | 964 | 0.9606 | 0.8537 | 0.4586 | 0.8728 | 0.8354 |
| 0.6279 | 3.0 | 1446 | 0.9701 | 0.9159 | 0.3291 | 0.9244 | 0.9075 |
| 0.418 | 4.0 | 1928 | 0.9761 | 0.9426 | 0.2596 | 0.9439 | 0.9413 |
| 0.316 | 5.0 | 2410 | 0.9797 | 0.9530 | 0.2159 | 0.9545 | 0.9515 |
| 0.2553 | 6.0 | 2892 | 0.9821 | 0.9589 | 0.1848 | 0.9608 | 0.9571 |
| 0.213 | 7.0 | 3374 | 0.9832 | 0.9613 | 0.1622 | 0.9616 | 0.9610 |
| 0.1819 | 8.0 | 3856 | 0.9858 | 0.9707 | 0.1430 | 0.9691 | 0.9722 |
| 0.16 | 9.0 | 4338 | 0.9871 | 0.9712 | 0.1307 | 0.9705 | 0.9719 |
| 0.1409 | 10.0 | 4820 | 0.9885 | 0.9749 | 0.1197 | 0.9734 | 0.9764 |
| 0.1295 | 11.0 | 5302 | 0.9893 | 0.9759 | 0.1120 | 0.9747 | 0.9771 |
| 0.1174 | 12.0 | 5784 | 0.9893 | 0.9763 | 0.1065 | 0.9744 | 0.9782 |
| 0.1085 | 13.0 | 6266 | 0.9896 | 0.9770 | 0.1005 | 0.9755 | 0.9785 |
| 0.1011 | 14.0 | 6748 | 0.9905 | 0.9794 | 0.0968 | 0.9786 | 0.9803 |
| 0.0954 | 15.0 | 7230 | 0.9910 | 0.9801 | 0.0941 | 0.9793 | 0.9810 |
| 0.0899 | 16.0 | 7712 | 0.9912 | 0.9807 | 0.0916 | 0.9796 | 0.9817 |
| 0.0866 | 17.0 | 8194 | 0.9917 | 0.9819 | 0.0893 | 0.9810 | 0.9828 |
| 0.0847 | 18.0 | 8676 | 0.9918 | 0.9824 | 0.0880 | 0.9814 | 0.9835 |
| 0.0814 | 19.0 | 9158 | 0.9918 | 0.9824 | 0.0870 | 0.9814 | 0.9835 |
| 0.0822 | 20.0 | 9640 | 0.9918 | 0.9824 | 0.0868 | 0.9814 | 0.9835 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
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