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微调BERT-base模型，构建层次化分类器，Top-3准确率达97.2%，并自动识别出问题的关键类别

1. 具体微调的BERT-base模型是什么模型？

BERT-base模型是一个预训练的Transformer模型，包含12个Transformer块、12个自注意头和隐藏大小为768。该模型在大规模文本数据上进行了预训练，能够捕捉文本的上下文信息和语义特征。

2. 如何微调的，微调步骤？

微调BERT-base模型的步骤如下：

1. 加载预训练模型和分词器：

   from transformers import BertTokenizer, BertForSequenceClassificationmodel_name = 'bert-base-uncased'
   tokenizer = BertTokenizer.from_pretrained(model_name)
   model = BertForSequenceClassification.from_pretrained(model_name, num_labels=10)
   

2. 准备训练数据：

   from torch.utils.data import Dataset, DataLoaderclass TextClassificationDataset(Dataset):def __init__(self, texts, labels, tokenizer, max_length=128):self.texts = textsself.labels = labelsself.tokenizer = tokenizerself.max_length = max_lengthdef __len__(self):return len(self.texts)def __getitem__(self, idx):text = self.texts[idx]label = self.labels[idx]encoding = self.tokenizer.encode_plus(text,add_special_tokens=True,max_length=self.max_length,return_token_type_ids=False,padding='max_length',truncation=True,return_attention_mask=True,return_tensors='pt',)return {'input_ids': encoding['input_ids'].flatten(),'attention_mask': encoding['attention_mask'].flatten(),'label': torch.tensor(label, dtype=torch.long)}# 示例数据
   texts = ["网络连接失败", "无法登录账户", "软件安装失败"]
   labels = [0, 1, 2]  # 0: 网络故障, 1: 账户权限, 2: 软件安装dataset = TextClassificationDataset(texts, labels, tokenizer)
   dataloader = DataLoader(dataset, batch_size=8, shuffle=True)
   

3. 定义训练参数：

   from torch.optim import AdamW
   from transformers import get_scheduleroptimizer = AdamW(model.parameters(), lr=2e-5)
   num_epochs = 3
   num_training_steps = num_epochs * len(dataloader)
   lr_scheduler = get_scheduler(name="linear",optimizer=optimizer,num_warmup_steps=0,num_training_steps=num_training_steps
   )device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
   model.to(device)
   

4. 训练模型：

   from tqdm.auto import tqdmprogress_bar = tqdm(range(num_training_steps))model.train()
   for epoch in range(num_epochs):for batch in dataloader:batch = {k: v.to(<