name-gender-classifier-ko

Name Gender Classifier (Korean/English)

XLM-RoBERTa 기반 한국어/영어 이름 성별 분류 모델

Model Description

• Base Model: FacebookAI/xlm-roberta-base
• Task: Text Classification (name → gender)
• Languages: Korean (ko), English (en)
• Labels: male, female

Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="solonsophy/name-gender-classifier-ko")

# Korean names
classifier("민준")     # → male
classifier("서연")     # → female
classifier("김민준")   # → male

# English names  
classifier("James")    # → male
classifier("Emma")     # → female

# Cross-cultural names
classifier("다니엘")   # → male
classifier("소피아")   # → female

Direct Model Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("solonsophy/name-gender-classifier-ko")
model = AutoModelForSequenceClassification.from_pretrained("solonsophy/name-gender-classifier-ko")

def predict(name):
    inputs = tokenizer(name, return_tensors="pt", padding=True, truncation=True, max_length=32)
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    pred_id = torch.argmax(probs, dim=1).item()
    return model.config.id2label[pred_id], probs[0][pred_id].item()

print(predict("서준"))  # ('male', 0.996)

Limitations

• Optimized for Korean and English names
• May have lower accuracy for names from other language origins
• Some unisex names may show lower confidence scores

Acknowledgments

This model was trained with computing resources provided by DDOK.AI.

License

Apache-2.0