Score Range	Meaning
+0.6 ~ +1.0	강한 긍정 감정
+0.2 ~ +0.6	약한 긍정 감정
-0.2 ~ +0.2	중립 또는 감정 표현이 미약
-0.6 ~ -0.2	약한 부정 감정
-1.0 ~ -0.6	강한 부정 감정

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

MODEL_NAME = "LimYeri/HowRU-KoELECTRA-Emotion-Regression"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

def predict_score(text: str):
    """
    Returns:
        - emotion_score: 감정 강도 (-1.0 ~ 1.0)
    """
    inputs = tokenizer(
        text,
        return_tensors="pt",
        truncation=True,
        padding=True,
        max_length=512
    ).to(device)

    with torch.no_grad():
        outputs = model(**inputs).logits
        score = outputs.item()

    return {"text": text, "emotion_score": score}


# Example
result = predict_score("오늘은 정말 즐겁고 행복한 최고의 하루였어!")
print(result)

pipeline

python

from transformers import pipeline

MODEL_NAME = "LimYeri/HowRU-KoELECTRA-Emotion-Regression"

regressor = pipeline(
    "text-classification",   # Regression도 동일 Task로 동작
    model=MODEL_NAME,
    tokenizer=MODEL_NAME,
    function_to_apply="none"  # Softmax 제거 → raw value 그대로 사용
)

text = "오늘은 정말 즐겁고 행복한 최고의 하루였어!"
result = regressor(text)[0]

print("입력 문장:", text)
print("감정 스코어:", result["score"])

Training Details

Training Data

Total(9:1로 분할): 42,000행
Train: 37,800행
Validation: 4,200행

Training Procedure

Base Model: monologg/koelectra-base-v3-discriminator
Max Length: 512

Training Hyperparameters

num_train_epochs: 4
learning_rate: 1.8e-5
weight_decay: 0.01
warmup_ratio: 0.12
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
loss_function: Huber Loss (δ = 1.0)

Performance

Metric	Score
Eval MAE	0.0461
Eval Pearson Correlation	0.9951
Eval Loss	0.00199

Model Architecture

1) ELECTRA Encoder (Base-size)

Hidden size: 768
Layers: 12 Transformer blocks
Attention heads: 12
MLP intermediate size: 3072
Activation: GELU
Dropout: 0.1

2) Classification Head

감정 강도(–1.0 ~ 1.0)를 출력하는 단일 회귀 헤드:

Dense Layer: 768 → 768
Activation: GELU
Dropout: 0.1
Output Projection: 768 → 1

최종 출력 값은 Softmax 없이 logits 그대로 사용하며,
–1.0 ~ 1.0 범위 감정 강도로 해석됩니다.

Citation

bibtex

@misc{HowRUEmotionRegression2025,
  title={HowRU KoELECTRA Emotion Regression},
  author={Lim, Yeri},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/LimYeri/HowRU-KoELECTRA-Emotion-Regression}}
}