比赛链接:讯飞开放平台
来源:DataWhale AI夏令营3(NLP)
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使用字符级和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]
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
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
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
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
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:
ライト