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1from transformers import AutoModelForSequenceClassification, AutoTokenizer
2from peft import PeftModel
3import torch
4
5# 베이스 모델 로드
6base_model = AutoModelForSequenceClassification.from_pretrained(
7 "klue/bert-base",
8 num_labels=2
9)
10
11# LoRA 어댑터 로드
12model = PeftModel.from_pretrained(base_model, "JINIIII/nsmc-sentiment-lora-kjs")
13tokenizer = AutoTokenizer.from_pretrained("JINIIII/nsmc-sentiment-lora-kjs")
14
15# 추론
16text = "이 영화 정말 재미있어요!"
17
18inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
19outputs = model(**inputs)
20
21probs = torch.softmax(outputs.logits, dim=-1)
22pred = torch.argmax(probs, dim=-1).item()
23
24label = "긍정" if pred == 1 else "부정"
25confidence = probs[0][pred].item()
26
27print(f"결과: {label} (확신도: {confidence:.2%})")1@misc{nsmc-sentiment-lora,
2 author = {JINIIII},
3 title = {NSMC Sentiment Analysis with LoRA},
4 year = {2024},
5 publisher = {Hugging Face},
6 url = {https://huggingface.co/JINIIII/nsmc-sentiment-lora-kjs}
7}