FileNotFoundError: [Errno 2] No such file or directory: 'transformer_model.pth'

非常抱歉，这是因为示例代码中缺少了模型文件，你需要先训练一个Transformer模型并将其保存为.pth文件，然后才能执行推理。

以下是一个简单的Transformer模型训练代码示例，你可以参考它训练一个自己的模型：

import torch
import torch.nn.functional as F

# 定义模型
class Transformer(torch.nn.Module):
    def __init__(self, vocab_size, embed_dim, num_heads, hidden_dim, num_layers):
        super(Transformer, self).__init__()
        self.embedding = torch.nn.Embedding(vocab_size, embed_dim)
        self.pos_embedding = torch.nn.Embedding(1000, embed_dim)
        encoder_layer = torch.nn.TransformerEncoderLayer(embed_dim, num_heads, hidden_dim)
        self.encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers)
        self.decoder = torch.nn.Linear(embed_dim, vocab_size)
    
    def forward(self, src, src_mask):
        src_pos = torch.arange(src.size(1), device=src.device).unsqueeze(0).repeat(src.size(0), 1)
        src_embed = self.embedding(src) + self.pos_embedding(src_pos)
        src_embed = src_embed.permute(1, 0, 2)
        output = self.encoder(src_embed, src_key_padding_mask=src_mask)
        output = output.permute(1, 0, 2)
        output = self.decoder(output)
        return F.log_softmax(output, dim=-1)

# 示例数据
train_data = [
    [1, 2, 3, 4, 5],
    [6, 7, 8, 9, 10]
]
train_mask = [
    [0, 0, 0, 1, 1],
    [0, 0, 0, 1, 1]
]
train_target = [
    [2, 3, 4, 5, 1],
    [7, 8, 9, 10, 1]
]

# 初始化模型和优化器
model = Transformer(vocab_size=10000, embed_dim=256, num_heads=8, hidden_dim=512, num_layers=6)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 开始训练
epochs = 10
for epoch in range(epochs):
    for i in range(len(train_data)):
        input_seq = torch.tensor([train_data[i]])
        input_mask = torch.tensor([train_mask[i]])
        target_seq = torch.tensor([train_target[i]])
        
        optimizer.zero_grad()
        output = model(input_seq, input_mask)
        loss = F.nll_loss(output.squeeze(0), target_seq.squeeze(0))
        loss.backward()
        optimizer.step()
        
        print('Epoch: {}, Iteration: {}, Loss: {:.4f}'.format(epoch+1, i+1, loss.item()))

# 保存模型
torch.save(model.state_dict(), 'transformer_model.pth')

在训练完成后，你可以将模型保存为.pth文件，然后在之前的推理示例中加载它来执行推理。