Local sentiment analysis upload.

2025-08-23 15:55:07 +08:00
parent 4e33224633
commit f448ddd466
65 changed files with 1563359 additions and 11 deletions
@@ -0,0 +1,165 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import re
+
+def preprocess_text(text):
+    """简单的文本预处理，适用于多语言文本"""
+    return text
+
+def main():
+    print("正在加载多语言情感分析模型...")
+    
+    # 使用多语言情感分析模型
+    model_name = "tabularisai/multilingual-sentiment-analysis"
+    local_model_path = "./model"
+    
+    try:
+        # 检查本地是否已有模型
+        import os
+        if os.path.exists(local_model_path):
+            print("从本地加载模型...")
+            tokenizer = AutoTokenizer.from_pretrained(local_model_path)
+            model = AutoModelForSequenceClassification.from_pretrained(local_model_path)
+        else:
+            print("首次使用，正在下载模型到本地...")
+            # 下载并保存到本地
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            model = AutoModelForSequenceClassification.from_pretrained(model_name)
+            
+            # 保存到本地
+            tokenizer.save_pretrained(local_model_path)
+            model.save_pretrained(local_model_path)
+            print(f"模型已保存到: {local_model_path}")
+        
+        # 设置设备
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        model.to(device)
+        model.eval()
+        print(f"模型加载成功! 使用设备: {device}")
+        
+        # 情感标签映射（5级分类）
+        sentiment_map = {
+            0: "非常负面", 1: "负面", 2: "中性", 3: "正面", 4: "非常正面"
+        }
+        
+    except Exception as e:
+        print(f"模型加载失败: {e}")
+        print("请检查网络连接")
+        return
+    
+    print("\n============= 多语言情感分析 =============")
+    print("支持语言: 中文、英文、西班牙文、阿拉伯文、日文、韩文等22种语言")
+    print("情感等级: 非常负面、负面、中性、正面、非常正面")
+    print("输入文本进行分析 (输入 'q' 退出):")
+    print("输入 'demo' 查看多语言示例")
+    
+    while True:
+        text = input("\n请输入文本: ")
+        if text.lower() == 'q':
+            break
+        
+        if text.lower() == 'demo':
+            show_multilingual_demo(tokenizer, model, device, sentiment_map)
+            continue
+        
+        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)
+            
+            # 分词编码
+            inputs = tokenizer(
+                processed_text,
+                max_length=512,
+                padding=True,
+                truncation=True,
+                return_tensors='pt'
+            )
+            
+            # 转移到设备
+            inputs = {k: v.to(device) for k, v in inputs.items()}
+            
+            # 预测
+            with torch.no_grad():
+                outputs = model(**inputs)
+                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 = sentiment_map[prediction]
+            
+            print(f"预测结果: {label} (置信度: {confidence:.4f})")
+            
+            # 显示所有类别的概率
+            print("详细概率分布:")
+            for i, (label_name, prob) in enumerate(zip(sentiment_map.values(), probabilities[0])):
+                print(f"  {label_name}: {prob:.4f}")
+            
+        except Exception as e:
+            print(f"预测时发生错误: {e}")
+            continue
+
+def show_multilingual_demo(tokenizer, model, device, sentiment_map):
+    """展示多语言情感分析示例"""
+    print("\n=== 多语言情感分析示例 ===")
+    
+    demo_texts = [
+        # 中文
+        ("今天天气真好，心情特别棒！", "中文"),
+        ("这家餐厅的菜味道非常棒！", "中文"),
+        ("服务态度太差了，很失望", "中文"),
+        
+        # 英文
+        ("I absolutely love this product!", "英文"),
+        ("The customer service was disappointing.", "英文"),
+        ("The weather is fine, nothing special.", "英文"),
+        
+        # 日文
+        ("このレストランの料理は本当に美味しいです！", "日文"),
+        ("このホテルのサービスはがっかりしました。", "日文"),
+        
+        # 韩文
+        ("이 가게의 케이크는 정말 맛있어요！", "韩文"),
+        ("서비스가 너무 별로였어요。", "韩文"),
+        
+        # 西班牙文
+        ("¡Me encanta cómo quedó la decoración!", "西班牙文"),
+        ("El servicio fue terrible y muy lento.", "西班牙文"),
+    ]
+    
+    for text, language in demo_texts:
+        try:
+            inputs = tokenizer(
+                text,
+                max_length=512,
+                padding=True,
+                truncation=True,
+                return_tensors='pt'
+            )
+            
+            inputs = {k: v.to(device) for k, v in inputs.items()}
+            
+            with torch.no_grad():
+                outputs = model(**inputs)
+                logits = outputs.logits
+                probabilities = torch.softmax(logits, dim=1)
+                prediction = torch.argmax(probabilities, dim=1).item()
+            
+            confidence = probabilities[0][prediction].item()
+            label = sentiment_map[prediction]
+            
+            print(f"\n{language}: {text}")
+            print(f"结果: {label} (置信度: {confidence:.4f})")
+            
+        except Exception as e:
+            print(f"处理 {text} 时出错: {e}")
+    
+    print("\n=== 示例结束 ===")
+
+if __name__ == "__main__":
+    main()