A new microblog sentiment recognition model has been added, based on the fine-tuning of GPT2, but it has not yet been adapted to the system.

2025-05-18 22:07:18 +08:00
parent d24b47acfc
commit ed23ab1908
11 changed files with 120928 additions and 0 deletions
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+import torch
+from transformers import BertTokenizer
+from train import GPT2ClassifierWithAdapter
+
+def main():
+    # 设置设备
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"使用设备: {device}")
+    
+    # 使用本地模型路径而不是在线模型名称
+    local_model_path = './models/gpt2-chinese'
+    model_path = 'best_weibo_sentiment_model.pth'
+    
+    print(f"加载模型: {model_path}")
+    # 从本地加载tokenizer
+    tokenizer = BertTokenizer.from_pretrained(local_model_path)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = '[PAD]'
+    
+    # 加载模型，使用本地模型路径
+    model = GPT2ClassifierWithAdapter(local_model_path)
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.to(device)
+    model.eval()
+    
+    print("\n============= 微博情感分析 =============")
+    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
+        
+        # 对文本进行编码
+        encoding = tokenizer(
+            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})")
+
+if __name__ == "__main__":
+    main()