将本地词向量应用到简单的情感分类任务中，并且计算其准确率的pytorch代码

好的，以下是一个简单的示例代码：

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

# 定义情感分类模型
class SentimentClassifier(nn.Module):
    def __init__(self, embedding_dim, hidden_dim, output_dim):
        super().__init__()
        self.embedding = nn.Embedding(num_embeddings=VOCAB_SIZE, embedding_dim=embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, hidden_dim, bidirectional=True)
        self.fc = nn.Linear(hidden_dim*2, output_dim)
        
    def forward(self, text):
        embedded = self.embedding(text)  # text shape: [seq_len, batch_size]
        output, (hidden, cell) = self.rnn(embedded)
        hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)
        return self.fc(hidden)

# 定义训练函数
def train(model, iterator, optimizer, criterion):
    model.train()
    epoch_loss = 0
    epoch_acc = 0
    
    for batch in iterator:
        optimizer.zero_grad()
        text, labels = batch
        predictions = model(text).squeeze(1)
        loss = criterion(predictions, labels)
        acc = binary_accuracy(predictions, labels)
        loss.backward()
        optimizer.step()
        
        epoch_loss += loss.item()
        epoch_acc += acc.item()
        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# 定义评估函数
def evaluate(model, iterator, criterion):
    model.eval()
    epoch_loss = 0
    epoch_acc = 0
    
    with torch.no_grad():
        for batch in iterator:
            text, labels = batch
            predictions = model(text).squeeze(1)
            loss = criterion(predictions, labels)
            acc = binary_accuracy(predictions, labels)

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

# 定义计算准确率函数
def binary_accuracy(preds, y):
    rounded_preds = torch.round(torch.sigmoid(preds))
    correct = (rounded_preds == y).float() 
    acc = correct.sum() / len(correct)
    return acc

# 加载本地词向量
with open('vectors.txt', 'r') as f:
    words = []
    vectors = []
    for line in f:
        word, *vector = line.split()
        words.append(word)
        vectors.append(vector)
    vectors = np.asarray(vectors, dtype=np.float32)
    
# 将词向量转换为 PyTorch tensor
vectors = torch.from_numpy(vectors)

# 定义数据集
TEXT = torchtext.data.Field(tokenize='spacy')
LABEL = torchtext.data.LabelField(dtype=torch.float)
train_data, test_data = torchtext.datasets.IMDB.splits(TEXT, LABEL)

# 构建词汇表
TEXT.build_vocab(train_data, vectors=vectors)
LABEL.build_vocab(train_data)

# 定义超参数
EMBEDDING_DIM = vectors.shape[1]
HIDDEN_DIM = 256
OUTPUT_DIM = 1
BATCH_SIZE = 64

# 构建迭代器
train_iterator, test_iterator = torchtext.data.BucketIterator.splits(
    (train_data, test_data), 
    batch_size=BATCH_SIZE,
    device=device)

# 初始化模型、优化器和损失函数
model = SentimentClassifier(EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM)
optimizer = optim.Adam(model.parameters())
criterion = nn.BCEWithLogitsLoss()

# 训练模型
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, test_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}%')

在这个代码中，我们加载了一个本地词向量文件 "vectors.txt"，并将其应用到一个简单的情感分类任务中。我们使用 PyTorch 的 TorchText 库来构建数据集和迭代器，并且使用 PyTorch 自带的 LSTM 模型来进行情感分类。我们使用 Adam 优化器和二元交叉熵损失函数来训练模型，并且计算每个 epoch 的训练损失、训练准确率、验证损失和验证准确率。