The integration process and a complete use example are given

model_pro/using_example.py

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+import torch
+import numpy as np
+from transformers.models.bert import BertTokenizer, BertModel
+from MHA import MultiHeadAttentionLayer
+from classifier import FinalClassifier
+
+
+# 加载BERT模型并生成嵌入
+def get_sentence_embeddings(sentences, bert_model_path, max_length=80):
+    """使用BERT生成多个句子的嵌入"""
+    tokenizer = BertTokenizer.from_pretrained(bert_model_path)
+    model = BertModel.from_pretrained(bert_model_path)
+
+    embeddings = []
+    for sentence in sentences:
+        inputs = tokenizer(sentence, return_tensors="pt", padding="max_length", truncation=True, max_length=max_length)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        embedding = outputs.last_hidden_state.cpu().numpy()
+        embeddings.append(embedding)
+
+    return np.vstack(embeddings)  # 保持多句子输出格式一致
+
+
+# 加载已经训练好的模型
+def load_model(model_path):
+    print(f"加载模型 {model_path}...")
+    model = torch.load(model_path)
+    model.eval()  # 设置为评估模式
+    return model
+
+
+# 多句子的预测函数
+def predict_sentences(sentences, model, bert_model_path, max_length=80):
+    # 检查是否为单个句子输入，如果是，将其包装为列表
+    if isinstance(sentences, str):
+        sentences = [sentences]
+
+    # 生成句子的BERT嵌入
+    embeddings = get_sentence_embeddings(sentences, bert_model_path, max_length)
+
+    # 转换为Tensor
+    embedding_tensors = torch.tensor(embeddings, dtype=torch.float32).squeeze(1)  # 修改squeeze以适应多个句子
+
+    # 检查嵌入维度是否符合注意力层要求
+    embed_size = embedding_tensors.size(-1)
+    num_heads = 12
+    if embed_size % num_heads != 0:
+        raise ValueError(f"嵌入维度 {embed_size} 无法被注意力头数量 {num_heads} 整除")
+
+    # 加载多头注意力机制
+    attention_model = MultiHeadAttentionLayer(embed_size=embed_size, num_heads=num_heads)
+
+    predictions = []
+    with torch.no_grad():
+        for embedding_tensor in embedding_tensors:
+            attention_output = attention_model(embedding_tensor.unsqueeze(0), embedding_tensor.unsqueeze(0),
+                                               embedding_tensor.unsqueeze(0))
+            outputs = model(attention_output)
+            outputs = torch.mean(outputs, dim=1)
+            _, predicted = torch.max(outputs, 1)  # 获取预测的类别
+            predictions.append(predicted.item())
+
+    return predictions
+
+
+if __name__ == "__main__":
+    # 加载已经训练好的模型
+    model_path = './final_model.pt'
+    model = load_model(model_path)
+
+    # 需要预测的句子，可以输入单个句子或多个句子
+    sentences = ["这是一条待预测的句子",
+                 "他在你面前骂黑鬼 印度屎屁尿背后就会根人家骂你中国猴子，这可能不是种族歧视这是素质太低",
+                 "完美女朋友",
+                 "在美国的亚裔就是一盘散沙。日裔看不起韩裔 韩裔仇视日裔 港澳台裔看不起大陆裔，大陆裔里面又歧视福建裔"]  # 可以替换为单个句子或多个句子
+
+    # BERT模型路径
+    bert_model_path = './bert_model'
+
+    # 对句子进行预测
+    predicted_labels = predict_sentences(sentences, model, bert_model_path)
+
+    # 根据预测的label输出对应的文本
+    for i, label in enumerate(predicted_labels):
+        if label == 1:
+            print(f"句子: '{sentences[i]}' 预测结果: 不良言论")
+        elif label == 0:
+            print(f"句子: '{sentences[i]}' 预测结果: 正常言论")
+        else:
+            print(f"句子: '{sentences[i]}' 未知标签: {label}")