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male, female1from transformers import pipeline
2
3classifier = pipeline("text-classification", model="solonsophy/name-gender-classifier-ko")
4
5# Korean names
6classifier("민준") # → male
7classifier("서연") # → female
8classifier("김민준") # → male
9
10# English names
11classifier("James") # → male
12classifier("Emma") # → female
13
14# Cross-cultural names
15classifier("다니엘") # → male
16classifier("소피아") # → female1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4tokenizer = AutoTokenizer.from_pretrained("solonsophy/name-gender-classifier-ko")
5model = AutoModelForSequenceClassification.from_pretrained("solonsophy/name-gender-classifier-ko")
6
7def predict(name):
8 inputs = tokenizer(name, return_tensors="pt", padding=True, truncation=True, max_length=32)
9 with torch.no_grad():
10 outputs = model(**inputs)
11 probs = torch.softmax(outputs.logits, dim=1)
12 pred_id = torch.argmax(probs, dim=1).item()
13 return model.config.id2label[pred_id], probs[0][pred_id].item()
14
15print(predict("서준")) # ('male', 0.996)