写一个python将THUCNews数据集读取成dataloader的代码

好的，假设THUCNews数据集已经下载并解压缩到了本地，可以使用如下代码将其读取成dataloader：

import os
import torch
from torch.utils.data import Dataset, DataLoader

class THUCNewsDataset(Dataset):
    def __init__(self, data_path, max_len):
        self.max_len = max_len
        self.label_to_idx = {}
        self.data = []
        label_idx = 0
        for label_name in os.listdir(data_path):
            label_path = os.path.join(data_path, label_name)
            if not os.path.isdir(label_path):
                continue
            self.label_to_idx[label_name] = label_idx
            label_idx += 1
            for file_name in os.listdir(label_path):
                file_path = os.path.join(label_path, file_name)
                with open(file_path, 'r', encoding='utf-8') as f:
                    content = f.read().strip()
                    content = content[:max_len]
                    self.data.append((content, label_name))

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        content, label_name = self.data[index]
        label_idx = self.label_to_idx[label_name]
        return content, label_idx

def collate_fn(batch):
    contents, labels = zip(*batch)
    contents = [c.split(' ') for c in contents]
    contents = [c[:max_len] for c in contents]
    contents = [[1] + c + [2] for c in contents]
    contents = torch.LongTensor(contents)
    labels = torch.LongTensor(labels)
    return contents, labels

def load_data_thucnews(batch_size, max_len):
    train_data = THUCNewsDataset('path/to/THUCNews', max_len)
    train_iter = DataLoader(train_data, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)
    vocab = None  # THUCNews数据集不需要构建词汇表
    return train_iter, vocab

其中，THUCNewsDataset类用于读取数据集，collate_fn函数用于对每个batch中的数据进行预处理，load_data_thucnews函数用于将预处理后的数据集转换为训练集，并返回train_iter和vocab。注意要将THUCNews数据集所在的路径替换成实际的路径，max_len是指截断后的文本长度。