챗봇의 의미론적 질문 답변

import numpy as np
from gensim.models import Word2Vec

# 加载预训练的词向量模型
model = Word2Vec.load("path/to/word2vec.model")

# 将问题进行分词
question = "你叫什么名字"
tokens = question.split(" ")

# 将每个单词转换为词向量
question_embedding = np.zeros((len(tokens), model.vector_size))
for i, token in enumerate(tokens):
    try:
        question_embedding[i] = model[token]
    except KeyError:
        pass

import torch
import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer

class SemanticModel(nn.Module):
    def __init__(self, num_layers, hidden_size, num_heads):
        super().__init__()
        self.embedding = nn.Linear(model.vector_size, hidden_size)
        encoder_layer = TransformerEncoderLayer(hidden_size, num_heads)
        self.transformer_encoder = TransformerEncoder(encoder_layer, num_layers)
        self.output = nn.Linear(hidden_size, 2)
    
    def forward(self, question_embedding):
        x = self.embedding(question_embedding) # 对词向量进行线性映射得到特征向量
        x = self.transformer_encoder(x) # 使用Transformer编码特征向量
        output = self.output(x) # 使用线性层输出回答
        return output

# 定义模型参数
num_layers = 2
hidden_size = 128
num_heads = 4

model = SemanticModel(num_layers, hidden_size, num_heads)
output = model(torch.from_numpy(question_embedding).float().unsqueeze(0))

import torch.nn.functional as F

# 对模型的输出进行softmax归一化
probs = F.softmax(output, dim=-1).squeeze(0)
# 选择概率最高的类别作为回答
answer = torch.argmax(probs).item()