nlp根据对联的上联自动生成下联的代码

对联生成的代码比较复杂，需要使用深度学习框架和相关的自然语言处理库。以下是一个基于PyTorch的对联生成代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义模型
class Seq2Seq(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(Seq2Seq, self).__init__()
        self.encoder_embedding = nn.Embedding(vocab_size, embedding_dim)
        self.encoder_rnn = nn.GRU(embedding_dim, hidden_dim, batch_first=True)
        self.decoder_embedding = nn.Embedding(vocab_size, embedding_dim)
        self.decoder_rnn = nn.GRU(embedding_dim, hidden_dim, batch_first=True)
        self.decoder_linear = nn.Linear(hidden_dim, vocab_size)

    def forward(self, x, y):
        encoder_embedded = self.encoder_embedding(x)
        _, hidden = self.encoder_rnn(encoder_embedded)
        decoder_embedded = self.decoder_embedding(y)
        decoder_output, _ = self.decoder_rnn(decoder_embedded, hidden.unsqueeze(0))
        output = self.decoder_linear(decoder_output)
        return output

# 定义训练函数
def train(model, train_loader, optimizer, criterion, device):
    model.train()
    train_loss = 0
    for x, y in train_loader:
        x, y = x.to(device), y.to(device)
        optimizer.zero_grad()
        output = model(x, y[:, :-1])
        loss = criterion(output.reshape(-1, output.shape[-1]), y[:, 1:].reshape(-1))
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    return train_loss / len(train_loader)

# 定义生成函数
def generate(model, start_word, max_len, word2id, id2word, device):
    model.eval()
    start_tokens = [word2id[start_word]]
    start_seq = torch.tensor(start_tokens, dtype=torch.long, device=device).reshape(1, -1)
    with torch.no_grad():
        for i in range(max_len):
            output = model(start_seq, start_seq)
            prob = output[:, -1, :].softmax(dim=-1).cpu().numpy()
            next_word_id = np.random.choice(len(word2id), p=prob.reshape(-1))
            start_tokens.append(next_word_id)
            if id2word[next_word_id] == '<EOS>':
                break
            start_seq = torch.tensor([start_tokens], dtype=torch.long, device=device)
    return ''.join([id2word[id] for id in start_tokens])

# 定义数据集和参数
train_data = [("上联1", "下联1"), ("上联2", "下联2"), ...]
vocab = set([word for pair in train_data for word in pair])
word2id = {'<PAD>': 0, '<UNK>': 1, '<EOS>': 2}
id2word = {0: '<PAD>', 1: '<UNK>', 2: '<EOS>'}
for i, word in enumerate(vocab):
    word2id[word] = i + 3
    id2word[i + 3] = word
train_loader = [(torch.tensor([word2id[word] for word in pair[0].split()], dtype=torch.long),
                 torch.tensor([word2id[word] for word in (pair[1] + '<EOS>').split()], dtype=torch.long))
                for pair in train_data]
vocab_size = len(vocab) + 3
embedding_dim = 128
hidden_dim = 256
model = Seq2Seq(vocab_size, embedding_dim, hidden_dim)
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss(ignore_index=0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    train_loss = train(model, train_loader, optimizer, criterion, device)
    print("Epoch [{}/{}], train loss: {:.4f}".format(epoch+1, num_epochs, train_loss))

# 生成对联
start_word = "上联"
max_len = 10
generated_couplet = generate(model, start_word, max_len, word2id, id2word, device)
print("生成的下联为：", generated_couplet)

需要注意的是，在实际的对联生成中，模型的训练和调参需要花费较长的时间和精力，同时需要使用更加复杂的模型和更大的数据集才能获得更好的效果。