使代码在进行模型训练时界面不卡顿，我们使用多线程或多进程来处理模型训练任务。这样可以避免主线程被阻塞，保持界面的响应性。我们将使用Python的threading模块来实现这一点。

以下是优化后的代码：

import os
import json
import jsonlines
import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from transformers import BertModel, BertTokenizer
import tkinter as tk
from tkinter import filedialog, messagebox, ttk, simpledialog
import logging
from difflib import SequenceMatcher
from datetime import datetime
import threading# 获取项目根目录
PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))# 配置日志
LOGS_DIR = os.path.join(PROJECT_ROOT, 'logs')
os.makedirs(LOGS_DIR, exist_ok=True)def setup_logging():log_file = os.path.join(LOGS_DIR, datetime.now().strftime('%Y-%m-%d_%H-%M-%S_羲和.txt'))logging.basicConfig(level=logging.INFO,format='%(asctime)s - %(levelname)s - %(message)s',handlers=[logging.FileHandler(log_file),logging.StreamHandler()])setup_logging()# 数据集类
class XihuaDataset(Dataset):def __init__(self, file_path, tokenizer, max_length=128):self.tokenizer = tokenizerself.max_length = max_lengthself.data = self.load_data(file_path)def load_data(self, file_path):data = []if file_path.endswith('.jsonl'):with jsonlines.open(file_path) as reader:for i, item in enumerate(reader):try:data.append(item)except jsonlines.jsonlines.InvalidLineError as e:logging.warning(f"跳过无效行 {i + 1}: {e}")elif file_path.endswith('.json'):with open(file_path, 'r') as f:try:data = json.load(f)except json.JSONDecodeError as e:logging.warning(f"跳过无效文件 {file_path}: {e}")return datadef __len__(self):return len(self.data)def __getitem__(self, idx):item = self.data[idx]question = item['question']human_answer = item['human_answers'][0]chatgpt_answer = item['chatgpt_answers'][0]try:inputs = self.tokenizer(question, return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)human_inputs = self.tokenizer(human_answer, return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)chatgpt_inputs = self.tokenizer(chatgpt_answer, return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)except Exception as e:logging.warning(f"跳过无效项 {idx}: {e}")return self.__getitem__((idx + 1) % len(self.data))return {'input_ids': inputs['input_ids'].squeeze(),'attention_mask': inputs['attention_mask'].squeeze(),'human_input_ids': human_inputs['input_ids'].squeeze(),'human_attention_mask': human_inputs['attention_mask'].squeeze(),'chatgpt_input_ids': chatgpt_inputs['input_ids'].squeeze(),'chatgpt_attention_mask': chatgpt_inputs['attention_mask'].squeeze(),'human_answer': human_answer,'chatgpt_answer': chatgpt_answer}# 获取数据加载器
def get_data_loader(file_path, tokenizer, batch_size=8, max_length=128):dataset = XihuaDataset(file_path, tokenizer, max_length)return DataLoader(dataset, batch_size=batch_size, shuffle=True)# 模型定义
class XihuaModel(torch.nn.Module):def __init__(self, pretrained_model_name='F:/models/bert-base-chinese'):super(XihuaModel, self).__init__()self.bert = BertModel.from_pretrained(pretrained_model_name)self.classifier = torch.nn.Linear(self.bert.config.hidden_size, 1)def forward(self, input_ids, attention_mask):outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)pooled_output = outputs.pooler_outputlogits = self.classifier(pooled_output)return logits# 训练函数
def train(model, data_loader, optimizer, criterion, device, progress_var=None):model.train()total_loss = 0.0num_batches = len(data_loader)for