Model Card for Model ID
Model Details
Model Description:
This model is a fine-tuned version of klue/roberta-base on the KLUE YNAT (Korean News Article Topic Classification) dataset.
It classifies Korean news headlines into one of 7 categories.
Base model: klue/roberta-base
Task: Text classification (News Category)
Language: Korean
Fine-tuned by: Joonhyun Kim
Framework: PyTorch + Hugging Face Transformers
Dataset: KLUE-YNAT
Accuracy: ~86% on validation set
License: Apache-2.0 (same as base model)
Model Sources [optional]
Uses
✅ Direct Use
The model can be used for Korean news article classification, such as categorizing headlines into topics like IT/Science, Sports, Politics, Economy, Culture, etc.
⚙️ Example Code
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="joonhyun-kim/roberta-base-klue-ynat-classification"
)
result = classifier("尹 대통령, 한미정상회담 참석 위해 출국")
print(result)
[{'label': '정치', 'score': 0.95}]
Training Details
Setting Value
Base model klue/roberta-base
Dataset KLUE-YNAT
Epochs 1
Batch size 8
Learning rate 5e-5
Optimizer AdamW
Framework Hugging Face Transformers
Train accuracy ~0.86
Eval loss 0.46
Evaluation
Metric Score
Accuracy 0.863
Eval loss 0.464
Technical Specifications [optional]
Architecture: RoBERTa-base (12-layer, hidden size 768, 125M parameters)
Hardware used: NVIDIA GPU (Colab / local machine)
Framework: PyTorch 2.4 + Transformers 4.x
Tokenization: SentencePiece BPE (same as base KLUE model)
Citation [optional]
If you use this model, please cite:
@misc{kim2025roberta-klue-ynat,
author = {Joonhyun Kim},
title = {RoBERTa-base fine-tuned on KLUE YNAT for Korean News Classification},
year = {2025},
howpublished = {\url{
https://huggingface.co/joonhyun-kim/roberta-base-klue-ynat-classification}}
}
