kobert-emotion-6class

KoBERT Emotion Classification - Korean Emotion Classification

한국어 감정 분류 모델 (6가지 감정)

Model Description

이 모델은 AI Hub의 공감형 대화 데이터셋을 사용하여 파인튜닝된 한국어 감정 분류 모델입니다.

• Base Model: monologg/kobert
• Task: 6-class emotion classification
• Language: Korean
• Dataset: AI Hub 공감형 대화 (Empathetic Dialogue)

Labels

Training Data

• Dataset: AI Hub 공감형 대화 (Empathetic Dialogue)
• Train samples: 22,758
• Validation samples: 1,591
• Test samples: 1,591

Performance

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "jeongyoonhuh/kobert-emotion-6class"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "오늘 정말 기분이 좋아요!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=1).item()

emotions = ['기쁨', '슬픔', '분노', '불안', '당황', '상처']
print(f"Predicted emotion: {emotions[prediction]}")

Training Hyperparameters

• Learning rate: 2e-5
• Batch size: 16
• Epochs: 5
• Weight decay: 0.01
• Warmup steps: 500
• Max sequence length: 256