使用自然推理完成文本蕴含任务，写完整的python代码

好的，我可以为您提供一个使用自然推理完成文本蕴含任务的Python代码示例。这个示例使用PyTorch框架和GloVe预训练词向量。这里以SNLI数据集为例，您可以根据自己的需要进行修改。

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from torchtext.datasets import SNLI
from torchtext.data import Field, BucketIterator

# 定义Field对象
TEXT = Field(tokenize='spacy', tokenizer_language='en_core_web_sm', include_lengths=True)
LABEL = Field(sequential=False, unk_token=None)

# 加载数据集
train_data, valid_data, test_data = SNLI.splits(TEXT, LABEL)

# 构建词汇表，加载GloVe预训练词向量
TEXT.build_vocab(train_data, vectors="glove.6B.300d")
LABEL.build_vocab(train_data)

# 定义超参数
BATCH_SIZE = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
EMBEDDING_DIM = 300
HIDDEN_DIM = 256
OUTPUT_DIM = len(LABEL.vocab)
N_LAYERS = 2
BIDIRECTIONAL = True
DROPOUT = 0.5
PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]

# 定义模型
class RNN(nn.Module):
    def __init__(self, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout, pad_idx):
        
        super().__init__()
        
        self.embedding = nn.Embedding(len(TEXT.vocab), embedding_dim, padding_idx=pad_idx)
        
        self.rnn = nn.LSTM(embedding_dim,
                           hidden_dim,
                           num_layers=n_layers,
                           bidirectional=bidirectional,
                           dropout=dropout)
        
        self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, text, text_lengths):
        
        embedded = self.dropout(self.embedding(text))
        
        packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths.to('cpu'))
        
        packed_output, (hidden, cell) = self.rnn(packed_embedded)
        
        output, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_output)
        
        hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1))

        return self.fc(hidden)

# 初始化模型、优化器和损失函数
model = RNN(EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS, BIDIRECTIONAL, DROPOUT, PAD_IDX)

optimizer = optim.Adam(model.parameters())

criterion = nn.CrossEntropyLoss()

model = model.to(device)
criterion = criterion.to(device)

# 计算准确率
def categorical_accuracy(preds, y):
    max_preds = preds.argmax(dim=1, keepdim=True)
    correct = max_preds.squeeze(1).eq(y)
    return correct.sum() / torch.FloatTensor([y.shape[0]])

# 训练模型
def train(model, iterator, optimizer, criterion):
    
    epoch_loss = 0
    epoch_acc = 0
    
    model.train()
    
    for batch in iterator:
        
        optimizer.zero_grad()
        
        text, text_lengths = batch.text
        
        predictions = model(text, text_lengths).squeeze(1)
        
        loss = criterion(predictions, batch.label)
        
        acc = categorical_accuracy(predictions, batch.label)
        
        loss.backward()
        
        optimizer.step()
        
        epoch_loss += loss.item()
        epoch_acc += acc.item()
        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# 评估模型
def evaluate(model, iterator, criterion):
    
    epoch_loss = 0
    epoch_acc = 0
    
    model.eval()
    
    with torch.no_grad():
    
        for batch in iterator:

            text, text_lengths = batch.text
            
            predictions = model(text, text_lengths).squeeze(1)
            
            loss = criterion(predictions, batch.label)
            
            acc = categorical_accuracy(predictions, batch.label)

            epoch_loss += loss.item()
            epoch_acc += acc.item()
        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# 训练模型并输出准确率
N_EPOCHS = 5

for epoch in range(N_EPOCHS):

    train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
    valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
    
    print(f'Epoch: {epoch+1:02}')
    print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
    print(f'\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')
    
# 测试模型
test_loss, test_acc = evaluate(model, test_iterator, criterion)

print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')

这个代码示例使用了LSTM模型进行文本蕴含任务，并且在SNLI数据集上进行了训练和测试。您可以根据自己的需要进行修改。