使用pytorch微调pkuseg模型

首先，需要安装pkuseg和pytorch的包：

pip install pkuseg torch

接下来，我们需要加载预训练的pkuseg模型：

import pkuseg
import torch

seg = pkuseg.pkuseg()  # 加载默认的模型

然后，我们可以使用pytorch的API来微调pkuseg模型。这里以微调分词模型为例，首先需要定义模型的结构和训练数据：

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

class SegDataset(Dataset):
    def __init__(self, data):
        self.data = data
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        return self.data[idx]

class SegModel(nn.Module):
    def __init__(self, num_labels):
        super(SegModel, self).__init__()
        self.bert = pkuseg.pkuseg(model_name='web_bert')
        self.linear = nn.Linear(768, num_labels)
        
    def forward(self, input_ids):
        output = self.bert(input_ids)
        output = self.linear(output)
        return output

在这个例子中，我们使用了pkuseg的BERT模型，并在其之上添加了一个线性层作为输出。接下来，我们需要定义训练的过程：

def train(model, train_data, num_epochs, batch_size, learning_rate):
    # 定义损失函数和优化器
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
    
    # 将数据划分为batch
    train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
    
    # 开始训练
    for epoch in range(num_epochs):
        total_loss = 0
        for batch in train_loader:
            optimizer.zero_grad()
            input_ids = [model.bert.convert_tokens_to_ids(sent) for sent in batch]
            input_ids = torch.tensor(input_ids)
            labels = [model.bert.label_to_id(sent) for sent in batch]
            labels = torch.tensor(labels)
            outputs = model(input_ids)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        print('Epoch {}/{}: Loss={}'.format(epoch+1, num_epochs, total_loss/len(train_data)))

在训练过程中，我们需要将句子转换为BERT模型可以接受的输入格式，并将标签转换为数字。这里使用了pytorch的自动求导机制来计算梯度，并使用AdamW优化器来更新模型参数。最后，我们可以使用训练好的模型进行分词：

def predict(model, text):
    seg_list = model.bert(text)
    return seg_list

完整的代码示例：

import pkuseg
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset

class SegDataset(Dataset):
    def __init__(self, data):
        self.data = data
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        return self.data[idx]

class SegModel(nn.Module):
    def __init__(self, num_labels):
        super(SegModel, self).__init__()
        self.bert = pkuseg.pkuseg(model_name='web_bert')
        self.linear = nn.Linear(768, num_labels)
        
    def forward(self, input_ids):
        output = self.bert(input_ids)
        output = self.linear(output)
        return output

def train(model, train_data, num_epochs, batch_size, learning_rate):
    # 定义损失函数和优化器
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
    
    # 将数据划分为batch
    train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
    
    # 开始训练
    for epoch in range(num_epochs):
        total_loss = 0
        for batch in train_loader:
            optimizer.zero_grad()
            input_ids = [model.bert.convert_tokens_to_ids(sent) for sent in batch]
            input_ids = torch.tensor(input_ids)
            labels = [model.bert.label_to_id(sent) for sent in batch]
            labels = torch.tensor(labels)
            outputs = model(input_ids)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        print('Epoch {}/{}: Loss={}'.format(epoch+1, num_epochs, total_loss/len(train_data)))

def predict(model, text):
    seg_list = model.bert(text)
    return seg_list

# 加载默认的模型
seg = pkuseg.pkuseg()

# 测试默认模型
text = '今天天气真好'
seg_list = seg.cut(text)
print(seg_list)

# 微调模型
train_data = SegDataset(['今天天气真好', '我爱北京天安门'])
model = SegModel(num_labels=3)
train(model, train_data, num_epochs=10, batch_size=2, learning_rate=1e-3)

# 测试微调后的模型
text = '今天天气真好'
seg_list = predict(model, text)
print(seg_list)