Local sentiment analysis upload.

SentimentAnalysisModel/WeiboSentiment_Finetuned/GPT2-Lora/predict.py

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+import torch
+from transformers import GPT2ForSequenceClassification, BertTokenizer
+from peft import PeftModel
+import os
+import re
+
+def preprocess_text(text):
+    return text
+
+def main():
+    # 设置设备
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"使用设备: {device}")
+    
+    # 模型和权重路径
+    base_model_path = './models/gpt2-chinese'
+    lora_model_path = './best_weibo_sentiment_lora'
+    
+    print("加载模型和tokenizer...")
+    
+    # 检查LoRA模型是否存在
+    if not os.path.exists(lora_model_path):
+        print(f"错误: 找不到LoRA模型路径 {lora_model_path}")
+        print("请先运行 train.py 进行训练")
+        return
+    
+    # 加载tokenizer
+    try:
+        tokenizer = BertTokenizer.from_pretrained(base_model_path)
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = '[PAD]'
+    except Exception as e:
+        print(f"加载tokenizer失败: {e}")
+        print("请确保models/gpt2-chinese目录包含tokenizer文件")
+        return
+    
+    # 加载基础模型
+    try:
+        base_model = GPT2ForSequenceClassification.from_pretrained(
+            base_model_path, 
+            num_labels=2
+        )
+        base_model.config.pad_token_id = tokenizer.pad_token_id
+    except Exception as e:
+        print(f"加载基础模型失败: {e}")
+        print("请确保models/gpt2-chinese目录包含模型文件")
+        return
+    
+    # 加载LoRA权重
+    try:
+        model = PeftModel.from_pretrained(base_model, lora_model_path)
+        model.to(device)
+        model.eval()
+        print("LoRA模型加载成功!")
+    except Exception as e:
+        print(f"加载LoRA权重失败: {e}")
+        print("请确保LoRA权重文件存在且格式正确")
+        return
+    
+    print("\n============= 微博情感分析 (LoRA版) =============")
+    print("输入微博内容进行分析 (输入 'q' 退出):")
+    
+    while True:
+        text = input("\n请输入微博内容: ")
+        if text.lower() == 'q':
+            break
+        
+        if not text.strip():
+            print("输入不能为空，请重新输入")
+            continue
+        
+        try:
+            # 预处理文本
+            processed_text = preprocess_text(text)
+            
+            # 对文本进行编码
+            encoding = tokenizer(
+                processed_text,
+                max_length=128,
+                padding='max_length',
+                truncation=True,
+                return_tensors='pt'
+            )
+            
+            # 转移到设备
+            input_ids = encoding['input_ids'].to(device)
+            attention_mask = encoding['attention_mask'].to(device)
+            
+            # 预测
+            with torch.no_grad():
+                outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+                logits = outputs.logits
+                probabilities = torch.softmax(logits, dim=1)
+                prediction = torch.argmax(probabilities, dim=1).item()
+            
+            # 输出结果
+            confidence = probabilities[0][prediction].item()
+            label = "正面情感" if prediction == 1 else "负面情感"
+            
+            print(f"预测结果: {label} (置信度: {confidence:.4f})")
+            
+        except Exception as e:
+            print(f"预测时发生错误: {e}")
+            continue
+
+if __name__ == "__main__":
+    main()