import onnxruntime as ort
import numpy as np
from transformers import BertTokenizer

# 모델 로드
tokenizer = BertTokenizer.from_pretrained("MongsangGa/home_life_improved-onnx")
session = ort.InferenceSession("model.onnx")

# 추론
text = "김치찌개 맛있게 끓이는 방법"
inputs = tokenizer(text, truncation=True, padding="max_length", max_length=128, return_tensors="np")
ort_inputs = {
    "input_ids": inputs["input_ids"].astype(np.int64),
    "attention_mask": inputs["attention_mask"].astype(np.int64)
}
logits = session.run(None, ort_inputs)[0]

학습 상세

Base Model: klue/roberta-small
Learning Rate: 1e-5
Batch Size: 32
Focal Loss: α=0.75, γ=1.5
Early Stopping: patience=3
Training Data: 270,465개 (원본 + 증강)

라이선스

Apache 2.0