结合注意力机制使用bge-reranker-large，怎么写代码

结合注意力机制（Attention Mechanism）使用Bert-Reranker-Large这类预训练模型进行文本排序或信息检索通常涉及以下几个步骤，这里以PyTorch框架为例：

首先，你需要安装必要的库，如transformers和torch：

pip install torch transformers

然后，你可以编写一个简单的Python脚本来加载模型并应用注意力机制。这里是一个基础示例：

import torch
from torch import nn
from transformers import BertModel, BertTokenizer

# 初始化Bert-Reranker-Large模型和分词器
model_name = "bert-base-uncased"  # 或者你的特定版本
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertModel.from_pretrained(model_name)

# 定义注意力层
class Attention(nn.Module):
    def __init__(self, hidden_size):
        super(Attention, self).__init__()
        self.query_layer = nn.Linear(hidden_size, hidden_size, bias=False)
        self.key_layer = nn.Linear(hidden_size, hidden_size, bias=False)
        self.value_layer = nn.Linear(hidden_size, hidden_size, bias=False)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, query, key, value):
        attn_scores = self.query_layer(query) @ self.key_layer(key).transpose(-2, -1)
        attn_weights = self.softmax(attn_scores)
        context = attn_weights @ value
        return context

attention_module = Attention(model.config.hidden_size)

# 使用模型对输入序列进行编码
def encode_queries(tokenized_queries, model, tokenizer):
    inputs = tokenizer(
        tokenized_queries,
        padding=True, truncation=True, max_length=512, return_tensors="pt"
    )
    with torch.no_grad():
        encoded_sequences = model(**inputs)
    return encoded_sequences.last_hidden_state

# 示例：假设你有两组查询
queries_group_1 = ...  # 分词后的查询列表
queries_group_2 = ...

# 对查询进行编码，并添加注意力模块
context_1 = attention_module(encode_queries(queries_group_1, model, tokenizer), *encode_queries(queries_group_1, model, tokenizer))
context_2 = attention_module(encode_queries(queries_group_2, model, tokenizer), *encode_queries(queries_group_2, model, tokenizer))

# 现在你可以基于上下文计算分数或其他任务相关的操作
scores = ...  # 根据需要计算比较两个组查询的相关度得分