NLP | 基于LLMs的文本分类任务

比赛链接:讯飞开放平台

来源:DataWhale AI夏令营3(NLP)

Roberta-base(BERT的改进)

①Roberta在预训练的阶段中没有对下一句话进行预测(NSP

②采用了动态掩码 ③使用字符级词级别表征的混合文本编码

论文:https://arxiv.org/pdf/1907.11692.pdf

DataWhale Topline的改进:

  特征1:平均池化MeanPooling(768维) -> 全连接层fc(128维)

  特征2:末隐藏层Last_hidden (768维) -> 全连接层fc(128维)

运行方式:阿里云机器学习平台PAI-交互式建模DSW

镜像选择pytorch:1.12-gpu-py39-cu113-ubuntu20.04

上传代码,解压指令:

unzip [filename]

运行py脚本指令(遇到网络错误重新运行即可):
 

python [python_filename]

① 数据处理模块

导入需要的模块:

from transformers import AutoTokenizer  #文本分词
import pandas as pd
import numpy as np
from tqdm import tqdm  #显示进度条
import torch
from torch.nn.utils.rnn import pad_sequence
#填充序列,保证向量中各序列维度的大小一样

MAX_LENGTH = 128  #定义最大序列长度为128

训练集制作:

def get_train(model_name, model_dict):
    model_index = model_dict[model_name]  # 获取模型索引
    train = pd.read_csv('./dataset/train.csv') #读取训练数据为DataFrame
    train['content'] = train['title'] + train['author'] + train['abstract']  
    #将标题、作者和摘要拼接为训练内容
    tokenizer = AutoTokenizer.from_pretrained(model_name, max_length=MAX_LENGTH, cache_dir=f'./premodels/{model_name}_saved')  # 实例化分词器对象

    # 通过分词器对训练数据进行分词,并获取输入ID、注意力掩码和标记类型ID(这个可有可无)
    input_ids_list, attention_mask_list, token_type_ids_list = [], [], []
    y_train = []  # 存储训练数据的标签
    
    for i in tqdm(range(len(train['content']))):  # 遍历训练数据
        sample = train['content'][i]  # 获取样本内容
        tokenized = tokenizer(sample, truncation='longest_first') 
        #分词处理,【最长优先方式】截断
        input_ids, attention_mask = tokenized['input_ids'], tokenized['attention_mask']  # 获取输入ID和注意力掩码
        input_ids, attention_mask = torch.tensor(input_ids), torch.tensor(attention_mask)  # 转换为PyTorch张量
        try:
            token_type_ids = tokenized['token_type_ids']  # 获取标记类型ID
            token_type_ids = torch.tensor(token_type_ids) # 转换为PyTorch张量
        except:
            token_type_ids = input_ids #异常处理
        input_ids_list.append(input_ids)  # 将输入ID添加到列表中
        attention_mask_list.append(attention_mask)  # 将注意力掩码添加到列表中
        token_type_ids_list.append(token_type_ids)  # 将标记类型ID添加到列表中
        y_train.append(train['label'][i])  # 将训练数据的标签添加到列表中
    # 保存 对下述对象进行填充,保证向量中各序列维度的大小一样,生成张量
   # 输入      ID input_ids_tensor、
   # 注意力掩码 attention_mask_tensor
   # 标记类型ID token_type_ids_tensor
    input_ids_tensor = pad_sequence(input_ids_list, batch_first=True, padding_value=0)
    attention_mask_tensor = pad_sequence(attention_mask_list, batch_first=True, padding_value=0)
    token_type_ids_tensor = pad_sequence(token_type_ids_list, batch_first=True, padding_value=0) 
    x_train = torch.stack([input_ids_tensor, attention_mask_tensor, token_type_ids_tensor], dim=1)  # 将输入张量堆叠为一个张量
    x_train = x_train.numpy()   # 转换为NumPy数组(ndarray)
    np.save(f'./models_input_files/x_train{model_index}.npy', x_train) #保存训练数据
    y_train = np.array(y_train) # 转换为NumPy数组(ndarray)
    np.save(f'./models_input_files/y_train{model_index}.npy', y_train) #保存标签数据

测试集制作:

def get_test(model_name, model_dict):
    model_index = model_dict[model_name]  # 获取模型索引
    test = pd.read_csv('./dataset/testB.csv')  # 从CSV文件中读取测试数据为DataFrame
    test['content'] = test['title'] + ' ' + test['author'] + ' ' + test['abstract']  
    # 将标题、作者和摘要拼接为测试内容
    tokenizer = AutoTokenizer.from_pretrained(model_name, max_length=MAX_LENGTH,cache_dir=f'./premodels/{model_name}_saved')  # 实例化分词器对象
    # 通过分词器对测试数据进行分词,创建输入ID、注意力掩码和标记类型ID列表进行记录(可有可无)
    input_ids_list, attention_mask_list, token_type_ids_list = [], [], []
    
    for i in tqdm(range(len(test['content']))):  # 遍历测试数据
        sample = test['content'][i]  # 获取样本内容
        tokenized = tokenizer(sample, truncation='longest_first')  
        # 分词处理,使用最长优先方式截断
        input_ids, attention_mask = tokenized['input_ids'], tokenized['attention_mask']  # 获取输入ID和注意力掩码
        input_ids, attention_mask = torch.tensor(input_ids), torch.tensor(attention_mask)  # 转换为PyTorch张量
        try:
            token_type_ids = tokenized['token_type_ids']  # 获取标记类型ID
            token_type_ids = torch.tensor(token_type_ids)  # 转换为PyTorch张量
        except:
            token_type_ids = input_ids #异常处理
        input_ids_list.append(input_ids)  # 将输入ID添加到列表中
        attention_mask_list.append(attention_mask)  # 将注意力掩码添加到列表中
        token_type_ids_list.append(token_type_ids)  # 将标记类型ID添加到列表中
    
    # 保存,对输入ID、注意力掩码、标记类型ID进行填充,保证向量中各序列维度的大小一样,生成张量
    input_ids_tensor = pad_sequence(input_ids_list, batch_first=True, padding_value=0) 
    attention_mask_tensor = pad_sequence(attention_mask_list, batch_first=True, padding_value=0)
    token_type_ids_tensor = pad_sequence(token_type_ids_list, batch_first=True, padding_value=0) 
    x_test = torch.stack([input_ids_tensor, attention_mask_tensor, token_type_ids_tensor], dim=1)  # 将输入张量堆叠为一个张量
    x_test = x_test.numpy()  # 转换为NumPy数组
    np.save(f'./models_input_files/x_test{model_index}.npy', x_test)  # 保存测试数据

划分训练集和验证集:

def split_train(model_name, model_dict):
    # 训练集:验证集 = 9 : 1
    split_rate = 0.90

    # 处理样本内容
    model_index = model_dict[model_name]  # 获取模型索引
    train = np.load(f'./models_input_files/x_train{model_index}.npy')  # 加载训练数据
    state = np.random.get_state()  # 获取随机数状态,保证样本间的随机是可重复的
    # 或者也可以设置经典随机种子random_seed=42
    np.random.shuffle(train)  # 随机打乱训练数据,数据洗牌
    val = train[int(train.shape[0] * split_rate):]  # 划分验证集 validation
    train = train[:int(train.shape[0] * split_rate)]  # 划分训练集 train set
    np.save(f'./models_input_files/x_train{model_index}.npy', train)  # 保存训练集
    np.save(f'./models_input_files/x_val{model_index}.npy', val)  # 保存验证集

    train = np.load(f'./models_input_files/y_train{model_index}.npy')  # 加载标签数据
    
    # 处理样本标签
    np.random.set_state(state)  # 恢复随机数状态,让样本标签的随机可重复
    np.random.shuffle(train)  # 随机打乱标签数据
    val = train[int(train.shape[0] * split_rate):]  # 划分验证集 validation
    train = train[:int(train.shape[0] * split_rate)]  # 划分训练集 train set
    np.save(f'./models_input_files/y_train{model_index}.npy', train)  # 保存训练集标签
    np.save(f'./models_input_files/y_val{model_index}.npy', val)  # 保存验证集标签
    
    print('split done.')

数据处理主函数:

if __name__ == '__main__':
    model_dict = {'xlm-roberta-base':1,
                  'roberta-base':2, 
                  'bert-base-uncased':3, 
                  'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext':4, 
                  'dmis-lab/biobert-base-cased-v1.2':5, 
                  'marieke93/MiniLM-evidence-types':6,
                  'microsoft/MiniLM-L12-H384-uncased':7, 
                  'cambridgeltl/SapBERT-from-PubMedBERT-fulltext':8,
                  'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract':9,
                  'microsoft/BiomedNLP-PubMedBERT-large-uncased-abstract':10}
    model_name = 'roberta-base'
    get_train(model_name, model_dict) #读取训练集
    get_test(model_name, model_dict)  #读取测试集
    split_train(model_name, model_dict) #划分训练集和测试集

② 模型训练

导入需要的模块:

import numpy as np
import torch
import torch.nn as nn
from sklearn import metrics
import os
import time
from transformers import AutoModel, AutoConfig
# 导入AutoModel和AutoConfig类,用于加载预训练模型
from tqdm import tqdm  #显示进度条

超参数类(可修改的所有超参数):

class opt:
    seed               = 42 # 随机种子
    batch_size         = 16 # 批处理大小
    set_epoch          = 5  # 训练轮数 
    early_stop         = 5  # 提前停止epoch数
    learning_rate      = 1e-5 # 学习率
    weight_decay       = 2e-6 # 权重衰减,L2正则化
    device             = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 选择设备,GPU或CPU
    gpu_num            = 1 # GPU个数
    use_BCE            = False # 是否使用BCE损失函数
    models             = ['xlm-roberta-base', 'roberta-base', 'bert-base-uncased',  
                          'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext', 'dmis-lab/biobert-base-cased-v1.2', 'marieke93/MiniLM-evidence-types',  
                          'microsoft/MiniLM-L12-H384-uncased','cambridgeltl/SapBERT-from-PubMedBERT-fulltext', 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract',
                          'microsoft/BiomedNLP-PubMedBERT-large-uncased-abstract'] # 模型名称列表
    model_index        = 2 # 根据上面选择使用的模型,这里填对应的模型索引
    model_name         = models[model_index-1] # 使用的模型名称
    continue_train     = False # 是否继续训练
    show_val           = False # 是否显示验证过程

定义模型类:

# 定义模型
class MODEL(nn.Module):
    def __init__(self, model_index):
        super(MODEL, self).__init__()
        # 若是第一次下载权重,则下载至同级目录的./premodels/内,以防占主目录的存储空间
        self.model = AutoModel.from_pretrained(opt.models[model_index-1], cache_dir='./premodels/'+opt.models[model_index-1]+'_saved', from_tf=False) # 加载预训练语言模型
        # 加载模型配置,可以直接获得模型最后一层的维度,而不需要手动修改
        config = AutoConfig.from_pretrained(opt.models[model_index-1], cache_dir='./premodels/'+opt.models[model_index-1]+'_saved') # 获取配置
        last_dim = config.hidden_size # 最后一层的维度
        if opt.use_BCE:out_size = 1 # 损失函数如果使用BCE,则输出大小为1
        else          :out_size = 2 # 否则则使用CE,输出大小为2
        feature_size = 128 # 设置特征的维度大小
        self.fc1 = nn.Linear(last_dim, feature_size) # 全连接层1
        self.fc2 = nn.Linear(last_dim, feature_size) # 全连接层2
        self.classifier = nn.Linear(feature_size, out_size) # 分类器
        self.dropout = nn.Dropout(0.3) # Dropout层

            
    def forward(self, x): #BP
        input_ids, attention_mask, token_type_ids = x[:,0],x[:,1],x[:,2] # 获取输入
        x = self.model(input_ids, attention_mask) # 通过模型
        
        all_token     = x[0] # 全部序列分词的表征向量
        pooled_output = x[1] # [CLS]的表征向量+一个全连接层+Tanh激活函数

        feature1 = all_token.mean(dim=1) # 对全部序列分词的表征向量取均值
        feature1 = self.fc1(feature1)    # 再输入进全连接层,得到feature1
        feature2 = pooled_output      # [CLS]的表征向量+一个全连接层+Tanh激活函数
        feature2 = self.fc2(feature2) # 再输入进全连接层,得到feature2
        feature  = 0.5*feature1 + 0.5*feature2 # 加权融合特征
        feature  = self.dropout(feature) # Dropout

        x  = self.classifier(feature) # 分类
        return x

数据加载:

def load_data():
    #数据集路径
    train_data_path     = f'models_input_files/x_train{model_index}.npy'
    train_label_path    = f'models_input_files/y_train{model_index}.npy'
    val_data_path       = f'models_input_files/x_val{model_index}.npy'# 验证集
    val_label_path      = f'models_input_files/y_val{model_index}.npy'# 验证集标签
    test_data_path      = f'models_input_files/x_test{model_index}.npy'# 测试集输入
    
    #数据集读取
    #data=torch.tensor([path],allow_pickle=True).tolist())
    train_data          = torch.tensor(np.load(train_data_path  , allow_pickle=True).tolist())
    train_label         = torch.tensor(np.load(train_label_path  , allow_pickle=True).tolist()).long() 
    val_data            = torch.tensor(np.load(val_data_path  , allow_pickle=True).tolist()) 
    val_label           = torch.tensor(np.load(val_label_path  , allow_pickle=True).tolist()).long()
    test_data           = torch.tensor(np.load(test_data_path  , allow_pickle=True).tolist()) 

    #构造训练集、验证集、测试集
    train_dataset       = torch.utils.data.TensorDataset(train_data  , train_label) 
    val_dataset         = torch.utils.data.TensorDataset(val_data  , val_label) 
    test_dataset        = torch.utils.data.TensorDataset(test_data) 
    
    return train_dataset, val_dataset, test_dataset # 返回数据集

模型预训练:

def model_pretrain(model_index, train_loader, val_loader):
    # 超参数设置
    set_epoch          = opt.set_epoch  # 训练轮数
    early_stop         = opt.early_stop # 提前停止epoch数
    learning_rate      = opt.learning_rate # 学习率
    weight_decay       = opt.weight_decay  # 权重衰减
    device             = opt.device  # 设备 
    gpu_num            = opt.gpu_num # GPU个数
    continue_train     = opt.continue_train # 是否继续训练
    model_save_dir     = 'checkpoints' # 模型保存路径
    
    # 是否要继续训练,若是,则加载模型进行训练;若否,则跳过训练,直接对测试集进行推理
    if not continue_train:
        # 判断最佳模型是否已经存在,若存在则直接读取,若不存在则进行训练
        if os.path.exists(f'checkpoints/best_model{model_index}.pth'): 
            best_model = MODEL(model_index)
            best_model.load_state_dict(torch.load(f'checkpoints/best_model{model_index}.pth')) # 加载模型
            return best_model
        else:
            pass
            

    # 模型初始化
    model = MODEL(model_index).to(device) 
    if continue_train:
        model.load_state_dict(torch.load(f'checkpoints/best_model{model_index}.pth')) # 继续训练加载模型

    # 优化器初始化
    if device    != 'cpu' and gpu_num > 1:  # 多张显卡
        optimizer = torch.optim.AdamW(model.module.parameters(), lr=learning_rate, weight_decay=weight_decay)
        optimizer = torch.nn.DataParallel(optimizer, device_ids=list(range(gpu_num))) # 多GPU
    else: # 单张显卡
        optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay) # 单GPU
    
    # 损失函数初始化
    if opt.use_BCE:
        loss_func = nn.BCEWithLogitsLoss() # BCE损失
    else:
        loss_func = nn.CrossEntropyLoss() # 交叉熵损失(CE)

    # 模型训练
    best_epoch         = 0 # 最佳epoch
    best_train_loss    = 100000 # 最佳训练损失
    train_acc_list     = [] # 训练准确率列表
    train_loss_list    = [] # 训练损失列表
    val_acc_list       = [] # 验证准确率列表 
    val_loss_list      = [] # 验证损失列表
    start_time         = time.time() # 训练开始时间

    for epoch in range(set_epoch): # 轮数
        model.train() # 模型切换到训练模式
        train_loss = 0 # 训练损失
        train_acc = 0 # 训练准确率
        for x, y in tqdm(train_loader): # 遍历训练集
            # 训练前先将数据放到GPU上
            x        = x.to(device)
            y        = y.to(device)
            outputs  = model(x) # 前向传播
            
            if opt.use_BCE: # BCE损失
                loss = loss_func(outputs, y.float().unsqueeze(1)) 
            else: # 交叉熵损失
                loss = loss_func(outputs, y)
            train_loss += loss.item() # 累加训练损失
            optimizer.zero_grad() # 清空梯度
            loss.backward() # 反向传播

            if device != 'cpu' and gpu_num > 1: # 多GPU更新
                optimizer.module.step()  
            else:
                optimizer.step() # 单GPU更新
            
            if not opt.use_BCE: # 非BCE损失
                _, predicted = torch.max(outputs.data, 1) # 预测结果
            else:
                predicted = (outputs > 0.5).int() # 预测结果
                predicted = predicted.squeeze(1) 
            train_acc   += (predicted == y).sum().item() # 计算训练准确率
            
        average_mode = 'binary'
        # 计算F1、Precision、Recall
        train_f1     = metrics.f1_score(y.cpu(), predicted.cpu(), average=average_mode)
        train_pre    = metrics.precision_score(y.cpu(), predicted.cpu(), average=average_mode)
        train_recall = metrics.recall_score(y.cpu(), predicted.cpu(), average=average_mode)


        train_loss /= len(train_loader) # 平均所有步数的训练损失作为一个epoch的训练损失
        train_acc  /= len(train_loader.dataset) # 平均所有步数训练准确率作为一个epoch的准确率
        train_acc_list.append(train_acc)   # 添加训练准确率
        train_loss_list.append(train_loss) # 添加训练损失

        print('-'*50)
        print('Epoch [{}/{}]\n Train Loss: {:.4f}, Train Acc: {:.4f}'.format(epoch + 1, set_epoch, train_loss, train_acc))
        print('Train-f1: {:.4f}, Train-precision: {:.4f} Train-recall: {:.4f}'.format(train_f1, train_pre, train_recall))

        if opt.show_val: # 显示验证过程
            # 验证
            model.eval() # 模型切换到评估模式
            val_loss = 0 # 验证损失
            val_acc = 0 # 验证准确率
    
            for x, y in tqdm(val_loader): # 遍历验证集
                # 训练前先将数据放到GPU上
                x = x.to(device) 
                y = y.to(device)
                outputs = model(x) # 前向传播
                if opt.use_BCE: # BCE损失
                    loss = loss_func(outputs, y.float().unsqueeze(1))
                else: # 交叉熵损失  
                    loss = loss_func(outputs, y)
                
                val_loss += loss.item() # 累加验证损失
                if not opt.use_BCE: # 非BCE损失
                    _, predicted = torch.max(outputs.data, 1) 
                else:
                    predicted = (outputs > 0.5).int() # 预测结果
                    predicted = predicted.squeeze(1)
                val_acc += (predicted == y).sum().item() # 计算验证准确率

            #计算F1、Precision、Recall
            val_f1     = metrics.f1_score(y.cpu(), predicted.cpu(), average=average_mode)
            val_pre    = metrics.precision_score(y.cpu(), predicted.cpu(), average=average_mode)
            val_recall = metrics.recall_score(y.cpu(), predicted.cpu(), average=average_mode)
    
            val_loss /= len(val_loader) # 平均验证损失
            val_acc /= len(val_loader.dataset) # 平均验证准确率
            val_acc_list.append(val_acc)   # 添加验证准确率
            val_loss_list.append(val_loss) # 添加验证损失
            print('\nVal Loss: {:.4f}, Val Acc: {:.4f}'.format(val_loss, val_acc))
            print('Val-f1: {:.4f}, Val-precision: {:.4f} Val-recall: {:.4f}'.format(val_f1, val_pre, val_recall))

        if train_loss < best_train_loss: # 更新最佳训练损失
            best_train_loss = train_loss
            best_epoch = epoch + 1
            if device == 'cuda' and gpu_num > 1: # 多GPU保存模型
                torch.save(model.module.state_dict(), f'{model_save_dir}/best_model{model_index}.pth')
            else:
                torch.save(model.state_dict(), f'{model_save_dir}/best_model{model_index}.pth') # 单GPU保存模型
        
        # 提前停止判断
        if epoch+1 - best_epoch == early_stop:  
            print(f'{early_stop} epochs later, the loss of the validation set no longer continues to decrease, so the training is stopped early.')
            end_time = time.time()
            print(f'Total time is {end_time - start_time}s.')
            break

    best_model = MODEL(model_index) # 初始化最佳模型
    best_model.load_state_dict(torch.load(f'checkpoints/best_model{model_index}.pth')) # 加载模型参数
    return best_model # 返回最佳模型

模型推理:

def model_predict(model, model_index, test_loader):
    device = 'cuda'
    model.to(device) # 模型到GPU
    model.eval()  # 切换到评估模式

    test_outputs = None
    with torch.no_grad():  # 禁用梯度计算
        for i, data in enumerate(tqdm(test_loader)):
            data = data[0].to(device) # 测试数据到GPU
            outputs = model(data) # 前向传播
            if i == 0: 
                test_outputs = outputs # 第一个batch直接赋值
            else:
                test_outputs = torch.cat([test_outputs, outputs], dim=0) 
                # 其余batch拼接

            del data, outputs  # 释放不再需要的Tensor

    # 保存预测结果    
    if not opt.use_BCE: 
        test_outputs = torch.softmax(test_outputs, dim=1) # 转换为概率
    torch.save(test_outputs, f'./models_prediction/{model_index}_prob.pth') 
    # 保存概率

模型训练主函数:

def run(model_index):
    # 固定随机种子
    seed = opt.seed  
    torch.seed = seed
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True

    train_dataset, val_dataset, test_dataset = load_data() # 加载数据集
    # 打印数据集信息
    print('-数据集信息:')
    print(f'-训练集样本数:{len(train_dataset)},测试集样本数:{len(test_dataset)}')
    train_labels = len(set(train_dataset.tensors[1].numpy()))
    
    # 查看训练样本类别均衡状况
    print(f'-训练集的标签种类个数为:{train_labels}') 
    numbers = [0] * train_labels
    for i in train_dataset.tensors[1].numpy():
        numbers[i] += 1
    print(f'-训练集各种类样本的个数:')
    for i in range(train_labels):
        print(f'-{i}的样本个数为:{numbers[i]}')

    batch_size   = opt.batch_size # 批处理大小
    # 构建DataLoader
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) 
    val_loader   = torch.utils.data.DataLoader(dataset=val_dataset,   batch_size=batch_size, shuffle=True)
    test_loader  = torch.utils.data.DataLoader(dataset=test_dataset,  batch_size=batch_size, shuffle=False)

    best_model   = model_pretrain(model_index, train_loader, val_loader)

    # 使用验证集评估模型
    model_predict(best_model, model_index, test_loader) # 模型推理

if __name__ == '__main__':
    model_index = opt.model_index # 获取模型索引
    run(model_index) # 运行程序

③ 模型评估

import torch
import pandas as pd
from models_training import MODEL  # 从本地文件models_training.py中导入MODEL类
from tqdm import tqdm
from sklearn.metrics import classification_report
import numpy as np

# 推理
def inference(model_indexs, use_BCE):
    device = 'cuda'  # 设备选择为cuda
    for model_index in model_indexs:
        # 加载模型
        model = MODEL(model_index).to(device)  # 创建MODEL类的实例,并将模型移至设备(device)
        model.load_state_dict(torch.load(f'checkpoints/best_model{model_index}.pth'))  # 加载模型的权重参数
        model.eval()  # 切换到评估模式
        # 加载val数据
        val_data_path = f'models_input_files/x_val{model_index}.npy'  # val数据的路径
        val_data = torch.tensor(np.load(val_data_path, allow_pickle=True).tolist())  # 加载val数据,并转换为Tensor格式
        val_dataset = torch.utils.data.TensorDataset(val_data)  # 创建val数据集
        val_loader  = torch.utils.data.DataLoader(dataset=val_dataset, batch_size=32, shuffle=False)  # 创建val数据的数据加载器
        val_outputs = None  # 初始化val_outputs变量
        with torch.no_grad():  # 禁用梯度计算
            for i, data in enumerate(tqdm(val_loader)):  # 遍历val_loader,显示进度条
                data = data[0].to(device)  # 将数据移至GPU
                outputs = model(data)  # 模型推理,获取输出
                if i == 0:
                    val_outputs = outputs  # 若为第一次迭代,直接赋值给val_outputs
                else:
                    val_outputs = torch.cat([val_outputs, outputs], dim=0)  
                   # 否则在dim=0上拼接val_outputs和outputs

                del data, outputs  # 释放不再需要的Tensor对象

        # 输出预测概率
        if not use_BCE:
            val_outputs = torch.softmax(val_outputs, dim=1)  # 对val_outputs进行softmax操作
        torch.save(val_outputs, f'evaluate_prediction/{model_index}_prob.pth')  # 保存预测概率结果


def run(model_indexs, use_BCE):
    # 读取所有的model_prob.pth,并全加在一起
    avg_pred = None  # 初始化avg_pred变量
    for i in model_indexs:
        pred = torch.load(f'evaluate_prediction/{i}_prob.pth').data  
        # 加载预测概率结果
        if use_BCE:
            # 选取大于0.5的作为预测结果
            pred = (pred > 0.5).int()  # 将大于0.5的值转换为整数(0或1)
            pred = pred.reshape(-1)  # 将预测结果进行形状重塑
        else:
            # 选取最大的概率作为预测结果
            pred = torch.argmax(pred, dim=1)  # 获取最大概率的索引作为预测结果
        pred = pred.cpu().numpy()  # 将预测结果转移到CPU上,并转换为NumPy数组

        # to_evaluate
        # 读取真实标签
        val_label_path = f'models_input_files/y_val{i}.npy'  # 真实标签的路径
        y_true = np.load(val_label_path)  # 加载真实标签

        # 分类报告
        print(f'model_index = {i}:')
        print(classification_report(y_true, pred, digits=4))  
        # 打印分类报告,包括精确度、召回率等指标

        zero_acc = 0; one_acc = 0 # 初始化0类和1类的准确率
        zero_num = 0; one_num= 0  # 初始化0类和1类的样本数量
        for i in range(pred.shape[0]):
            if y_true[i] == 0:
                zero_num += 1  # 统计0类的样本数量
            elif y_true[i] == 1:
                one_num += 1  # 统计1类的样本数量
            if pred[i] == y_true[i]:
                if pred[i] == 0:
                    zero_acc += 1  # 统计0类的正确预测数量
                elif pred[i] == 1:
                    one_acc += 1  # 统计1类的正确预测数量

        zero = np.sum(pred == 0) / pred.shape[0]  # 计算预测为0类的样本占比
        zero_acc /= zero_num  # 计算0类的正确率
        print(f'预测0类占比:{zero}  0类正确率:{zero_acc}')
        one = np.sum(pred == 1) / pred.shape[0]  # 计算预测为1类的样本占比
        one_acc /= one_num  # 计算1类的正确率
        print(f'预测1类占比:{one}  1类正确率:{one_acc}')
        print('-' * 80)


if __name__ == '__main__':
    use_BCE = False  # 是否使用BCE损失函数的标志,这里我只用交叉熵CE,所以是False
    inference([2], use_BCE=use_BCE)  # 进行推理,传入模型索引和use_BCE标志
    model_indexs = [2]  # 模型索引列表
    run(model_indexs, use_BCE=use_BCE)  # 进行运行,传入模型索引和use_BCE标志

④ 测试集推理

import torch
import pandas as pd
import warnings # 过滤警告
warnings.filterwarnings('ignore')

def run(model_indexs, use_BCE):
    # 记录模型数量
    model_num = len(model_indexs)
    # 读取所有的model_prob.pth,并全加在一起
    for i in model_indexs:
        # 加载模型在训练完成后对测试集推理所得的预测文件
        pred = torch.load(f'./models_prediction/{i}_prob.pth', map_location='cpu').data
        # 这里的操作是将每个模型对测试集推理的概率全加在一起
        if i == model_indexs[0]:
            avg_pred = pred
        else:
            avg_pred += pred
        
    # 取平均
    avg_pred /= model_num # 使用全加在一起的预测概率除以模型数量

    if use_BCE:
        # 选取概率大于0.5的作为预测结果
        pred = (avg_pred > 0.5).int()
        pred = pred.reshape(-1)
    else:
        # 后处理 - 根据标签数目的反馈,对预测阈值进行调整
        pred[:, 0][pred[:, 0]>0.001] = 1
        pred[:, 1][pred[:, 1]>0.999] = 1.2
        # 选取最大的概率作为预测结果
        pred = torch.argmax(avg_pred, dim=1)
    pred = pred.cpu().numpy()

    # to_submit
    # 读取test.csv文件
    test = pd.read_csv('./dataset/testB_submit_exsample.csv')

    # 开始写入预测结果
    for i in range(len(pred)):
        test['label'][i] = pred[i]

    print(test['label'].value_counts())
    # 保存为提交文件
    test.to_csv(f'submit.csv',index=False)

if __name__ == '__main__':
    run([2], use_BCE=False)
    # run([1,2,3,4,5,6,7,8,9,10], use_BCE=False)

模型优化的思路:

超参数调整、最大序列长度调整、损失函数更改、模型参数冻结

特征工程、模型集成、对比学习、提示学习サ

ChatGML2-6B

LLMs:自回归模型

Pretrained => prompt、finetune => RLHF 强化对齐学习

LoRA低秩适应:冻结预训练好的模型权重参数,在冻结原模型参数的情况下,通过往模型中加入额外的网络层,并只训练这些新增的网络层参数。

「instruction --> 」「input: X」「output: Y」

P-tuning v2:在原有的大型语言模型上添加一些新的参数,这些新的参数可以帮助模型更好地理解和处理特定的任务。

微调应用:垂直领域、个性化

在阿里云Pytorch环境中,克隆代码、下载chatglm2-6b模型,

安装依赖,并且运行训练脚本。

xfg_train.sh

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --model_name_or_path chatglm2-6b \ 本地模型的目录
    --stage sft \ 微调方法
    --use_v2 \ 使用glm2模型微调,默认值true
    --do_train \ 是否训练,默认值true
    --dataset paper_label \ 数据集名字
    --finetuning_type lora \ 
    --lora_rank 8 \  LoRA 微调中的秩大小
    --output_dir ./output/label_xfg \ 输出lora权重存放目录
    --per_device_train_batch_size 4 \ 用于训练的批处理大小
    --gradient_accumulation_steps 4 \ 梯度累加次数
    --lr_scheduler_type cosine \
    --logging_steps 10 \ 日志输出间隔
    --save_steps 1000 \ 断点保存间隔
    --learning_rate 5e-5 \ 学习率
    --num_train_epochs 4.0 \ 训练轮数
    --fp16 是否使用 fp16 半精度 默认值:False

导入数据

import pandas as pd
train_df = pd.read_csv('./csv_data/train.csv')
testB_df = pd.read_csv('./csv_data/testB.csv')

制作数据集

res = [] #存储数据样本

for i in range(len(train_df)):# 遍历训练数据的每一行
    paper_item = train_df.loc[i] # 获取当前行的数据
    # 创建一个字典,包含LoRA的指令、输入和输出信息
    tmp = {
    "instruction": "Please judge whether it is a medical field paper according to the given paper title and abstract, output 1 or 0, the following is the paper title and abstract -->",
    "input": f"title:{paper_item[1]},abstract:{paper_item[3]}",
    "output": str(paper_item[5])
  }
    res.append(tmp) # 将字典添加到结果列表中


import json #用于保存数据集

# 将制作好的数据集保存到data目录下
with open('./data/paper_label.json', mode='w', encoding='utf-8') as f:
    json.dump(res, f, ensure_ascii=False, indent=4)

修改data/data_info.json

{
  "paper_label": {
    "file_name": "paper_label.json"
  }
}

加载训练好的LoRA权重,进行预测

from peft import PeftModel
from transformers import AutoTokenizer, AutoModel, GenerationConfig, AutoModelForCausalLM

# 定义预训练模型的路径
model_path = "../chatglm2-6b"
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# 加载 label lora权重
model = PeftModel.from_pretrained(model, './output/label_xfg').half()
model = model.eval()

# 使用加载的模型和分词器进行聊天,生成回复
response, history = model.chat(tokenizer, "你好", history=[])
response

预测函数:

def predict(text):
    # 使用加载的模型和分词器进行聊天,生成回复
    response, history = model.chat(tokenizer, f"Please judge whether it is a medical field paper according to the given paper title and abstract, output 1 or 0, the following is the paper title and abstract -->{text}", history=[],
    temperature=0.01)
    return response

预测,导出csv

from tqdm import tqdm #预测过程的进度条

label = [] #存储预测结果


for i in tqdm(range(len(testB_df))): # 遍历测试集中的每一条样本
    test_item = testB_df.loc[i]      # 测试集中的每一条样本
    # 构建预测函数的输入:prompt
    test_input = f"title:{test_item[1]},author:{test_item[2]},abstract:{test_item[3]}"
    label.append(int(predict(test_input)))# 预测结果存入lable列表

testB_df['label'] = label # 把label列表存入testB_df

# task1虽然只需要label,但需要有一个keywords列,用个随意的字符串代替
testB_df['Keywords'] = ['tmp' for _ in range(2000)]

# 制作submit,提交submit
submit = testB_df[['uuid', 'Keywords', 'label']]
submit.to_csv('submit.csv', index=False)

提交结果:

NLP | 基于LLMs的文本分类任务_第1张图片

 ライト

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