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| 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 torch1from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
2
3# Load model
4model_name = "merrybabyxmas/mindcast-topic-classifier"
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForSequenceClassification.from_pretrained(model_name)
7
8# Create pipeline
9classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
10
11# Predict
12text = "오늘 날씨가 정말 좋네요"
13result = classifier(text)
14print(result)
151@misc{mindcast-model,
2 author = {Mindcast Team},
3 title = {Mindcast Topic Classifier},
4 year = {2025},
5 publisher = {HuggingFace},
6 howpublished = {\url{https://huggingface.co/merrybabyxmas/mindcast-emotion-sc-only}},
7}