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merrybabyxmas/mindcast-topic-classifier
mindcast-topic-classifier is a text classification model from merrybabyxmas. Use it when you need a label for a piece of text. The card lists the license as apache-2.0.
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From the Hugging Face model README
한국어 텍스트의 주제를 분류하는 모델입니다.
이 모델은 LoRA (Low-Rank Adaptation)를 사용하여 효율적으로 파인튜닝되었으며, 최종적으로 base model과 merge되어 배포되었습니다.
Training Date: 2025-12-12
| Metric | Score |
|---|---|
| Accuracy | 0.5583 |
| F1 Score (Macro) | 0.1024 |
| F1 Score (Weighted) | 0.4001 |
[[67 0 0 0 0 0 0]
[24 0 0 0 0 0 0]
[ 6 0 0 0 0 0 0]
[15 0 0 0 0 0 0]
[ 6 0 0 0 0 0 0]
[ 1 0 0 0 0 0 0]
[ 1 0 0 0 0 0 0]]
precision recall f1-score support
사회 0.5583 1.0000 0.7166 67
정치 0.0000 0.0000 0.0000 24
생활문화 0.0000 0.0000 0.0000 6
세계 0.0000 0.0000 0.0000 15
경제 0.0000 0.0000 0.0000 6
IT과학 0.0000 0.0000 0.0000 1
스포츠 0.0000 0.0000 0.0000 1
micro avg 0.5583 0.5583 0.5583 120
macro avg 0.0798 0.1429 0.1024 120
weighted avg 0.3117 0.5583 0.4001 120
| Hyperparameter | Value |
|---|---|
| Base Model | klue/roberta-base |
| Batch Size | 64 |
| Epochs | 1 |
| Learning Rate | 0.0001 |
| Warmup Ratio | 0.1 |
| Weight Decay | 0.01 |
| LoRA r | 8 |
| LoRA alpha | 16 |
| LoRA dropout | 0.05 |
pip install transformers torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
# Load model
model_name = "merrybabyxmas/mindcast-topic-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Create pipeline
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Predict
text = "오늘 날씨가 정말 좋네요"
result = classifier(text)
print(result)
If you use this model, please cite:
@misc{mindcast-model,
author = {Mindcast Team},
title = {Mindcast Topic Classifier},
year = {2025},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/merrybabyxmas/mindcast-emotion-sc-only}},
}
For questions or feedback, please open an issue on the model repository.
This model card was automatically generated.