【NLP入门系列四】评论文本分类入门案例

• 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
• 🍖 原作者：K同学啊

博主简介：努力学习的22级本科生一枚 🌟​；探索AI算法，C++，go语言的世界；在迷茫中寻找光芒​🌸
博客主页：羊小猪~~-CSDN博客
内容简介：这一篇是NLP的入门项目，以AG_NEW新闻数据为例。
🌸箴言🌸：去寻找理想的"天空""之城
上一篇内容：【NLP入门系列三】NLP文本嵌入(以Embedding和EmbeddingBag为例)-CSDN博客

​💁​​💁​​💁​​💁​: 如果在conda安装环境，由于nlp的核心包是torchtext，所以如果把握不好就重新创建一虚拟环境(小编的"难忘"经历)

文章目录

• • 1、准备
  • • 数据加载
    • 构建词表
  • 2、生成数据批次和迭代器
  • 3、定义与模型
  • • 模型定义
    • 创建模型
  • 4、创建训练和评估函数
  • • 训练函数
    • 评估函数
    • 创建超参数
  • 5、模型训练
  • 6、结果展示
  • 7、预测

🤔 思路

1、准备

AG News 数据集 （也叫 AG's Corpus or AG News Dataset），这是一个广泛用于自然语言处理（NLP）任务中的文本分类数据集。

---

基本信息：

• 全称：AG News
• 来源：来源于 AG's corpus，由 A. Godin 在 2005 年构建。
• 用途 ：主要用于短文本多类别分类任务
• 语言：英文
• 类别数：4 类新闻主题
• 训练样本数：120,000 条
• 测试样本数：7,600 条

---

类别标签（共 4 类）

标签	含义
1	World (世界)
2	Sports (体育)
3	Business (商业)
4	Science and Technology (科技)

---

数据加载

import torch
import torch.nn as nn 
import torch.nn.functional as F 
from torch.utils.data import Dataset, DataLoader 
import torchtext 
import pandas as pd
from torch.utils.data import Dataset, DataLoader
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator

# 检查设备
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device

device(type='cuda')

# 加载本地数据
train_df = pd.read_csv("./data/train.csv")
test_df = pd.read_csv("./data/test.csv")

# 合并标题和描述数据
train_df["text"] = train_df["Title"] + " " + train_df["Description"]
test_df["text"] = test_df["Title"] + " " + test_df["Description"]

# 查看数据格式
train_df

| | Class Index | Title | Description | text |
| 0 | 3 | Wall St. Bears Claw Back Into the Black (Reuters) | Reuters - Short-sellers, Wall Street's dwindli... | Wall St. Bears Claw Back Into the Black (Reute... |
| 1 | 3 | Carlyle Looks Toward Commercial Aerospace (Reu... | Reuters - Private investment firm Carlyle Grou... | Carlyle Looks Toward Commercial Aerospace (Reu... |
| 2 | 3 | Oil and Economy Cloud Stocks' Outlook (Reuters) | Reuters - Soaring crude prices plus worries\ab... | Oil and Economy Cloud Stocks' Outlook (Reuters... |
| 3 | 3 | Iraq Halts Oil Exports from Main Southern Pipe... | Reuters - Authorities have halted oil export\f... | Iraq Halts Oil Exports from Main Southern Pipe... |
| 4 | 3 | Oil prices soar to all-time record, posing new... | AFP - Tearaway world oil prices, toppling reco... | Oil prices soar to all-time record, posing new... |
| ... | ... | ... | ... | ... |
| 119995 | 1 | Pakistan's Musharraf Says Won't Quit as Army C... | KARACHI (Reuters) - Pakistani President Perve... | Pakistan's Musharraf Says Won't Quit as Army C... |
| 119996 | 2 | Renteria signing a top-shelf deal | Red Sox general manager Theo Epstein acknowled... | Renteria signing a top-shelf deal Red Sox gene... |
| 119997 | 2 | Saban not going to Dolphins yet | The Miami Dolphins will put their courtship of... | Saban not going to Dolphins yet The Miami Dolp... |
| 119998 | 2 | Today's NFL games | PITTSBURGH at NY GIANTS Time: 1:30 p.m. Line: ... | Today's NFL games PITTSBURGH at NY GIANTS Time... |

119999	2	Nets get Carter from Raptors	INDIANAPOLIS -- All-Star Vince Carter was trad...	Nets get Carter from Raptors INDIANAPOLIS -- A...

120000 rows × 4 columns

构建词表

# 定义 Dataset
class AGNewsDataset(Dataset):
    def __init__(self, dataframe):
        self.labels = dataframe['Class Index'].tolist()  # 列表数据
        self.texts = dataframe['text'].tolist()

    def __len__(self):
        return len(self.labels)

    def __getitem__(self, idx):
        return self.labels[idx], self.texts[idx]

# 加载数据
train_dataset = AGNewsDataset(train_df)
test_dataset = AGNewsDataset(test_df)

# 构建词表
tokenizer = get_tokenizer("basic_english")  # 英文数据，设置英文分词

def yield_tokens(data_iter):
    for _, text in data_iter:
        yield tokenizer(text)  # 构建词表

# 构建词表，设置索引
vocab = build_vocab_from_iterator(yield_tokens(train_dataset), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])

print("Vocab size:", len(vocab))

Vocab size: 95804

# 查看这些单词所在词典的索引
vocab(['here', 'is', 'an', 'example'])  

[475, 21, 30, 5297]

'''  
标签，原始是字符串类型，现在要转换成 数字 类型
文本数字化，需要一个函数进行转换(vocab)
'''
text_pipline = lambda x : vocab(tokenizer(x))  # 先分词。在数字化
label_pipline = lambda x : int(x) - 1   # 标签转化为数字

# 举例
text_pipline('here is the an example')

[475, 21, 2, 30, 5297]

2、生成数据批次和迭代器

# 采用embeddingbag嵌入方式，故需要构建数据，包括长度、标签、偏移量
''' 
数据格式：长度(~, 1)
标签：一维
偏移量：一维
'''
def collate_batch(batch):
    label_list, text_list, offsets = [], [], [0]
    
    for (_label, _text) in batch:
        # 标签列表，注意字符串转换成数字
        label_list.append(label_pipline(_label))
        
        # 文本列表，注意要转入tensro数据
        temp_text = torch.tensor(text_pipline(_text), dtype=torch.int64)
        text_list.append(temp_text)

        # 偏移量
        offsets.append(temp_text.size(0))
        
    # 全部转变成tensor变量
    label_list = torch.tensor(label_list, dtype=torch.int64)
    text_list = torch.cat(text_list)
    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
    
    return label_list.to(device), text_list.to(device), offsets.to(device)


# 数据加载
batch_size = 16
train_dl = DataLoader(
    train_dataset,
    batch_size=batch_size,
    shuffle=False,
    collate_fn=collate_batch
)

test_dl = DataLoader(
    test_dataset,
    batch_size=batch_size,
    shuffle=False,
    collate_fn=collate_batch
)

3、定义与模型

模型定义

class TextModel(nn.Module):
    def __init__(self, vocab_size, embed_dim, num_class):
        super().__init__()
        
        self.embeddingBag = nn.EmbeddingBag(vocab_size,  # 词典大小
                                            embed_dim,   # 嵌入维度
                                            sparse=False)
        
        self.fc = nn.Linear(embed_dim, num_class)
        self.init_weights()
    
    # 初始化权重
    def init_weights(self):
        initrange = 0.5
        self.embeddingBag.weight.data.uniform_(-initrange, initrange)  # 初始化权重范围
        self.fc.weight.data.uniform_(-initrange, initrange)
        self.fc.bias.data.zero_()  # 偏置置为0
        
    def forward(self, text, offsets):
        embedding = self.embeddingBag(text, offsets)
        return self.fc(embedding)

创建模型

# 查看数据类别
train_df.groupby('Class Index').count()

| | Title | Description | text |
| Class Index | | | |
| 1 | 30000 | 30000 | 30000 |
| 2 | 30000 | 30000 | 30000 |
| 3 | 30000 | 30000 | 30000 |

4	30000	30000	30000

class_num = 4
vocab_len = len(vocab)
embed_dim = 64  # 嵌入到64维度中
model = TextModel(vocab_size=vocab_len, embed_dim=embed_dim, num_class=class_num).to(device=device)

4、创建训练和评估函数

训练函数

def train(model, dataset, optimizer, loss_fn):
    size = len(dataset.dataset)
    num_batch = len(dataset)
    
    train_acc = 0
    train_loss = 0
    
    for _, (label, text, offset) in enumerate(dataset):
        label, text, offset = label.to(device), text.to(device), offset.to(device)
        
        predict_label = model(text, offset)
        loss = loss_fn(predict_label, label)
        
        # 求导与反向传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        train_acc += (predict_label.argmax(1) == label).sum().item()
        train_loss += loss.item()
        
    train_acc /= size 
    train_loss /= num_batch
    
    return train_acc, train_loss

评估函数

def test(model, dataset, loss_fn):
    size = len(dataset.dataset)
    batch_size = len(dataset)
    
    test_acc, test_loss = 0, 0
    
    with torch.no_grad():
        for _, (label, text, offset) in enumerate(dataset):
            label, text, offset = label.to(device), text.to(device), offset.to(device)
            
            predict = model(text, offset)
            loss = loss_fn(predict, label) 
            
            test_acc += (predict.argmax(1) == label).sum().item()
            test_loss += loss.item()
    
    test_acc /= size 
    test_loss /= batch_size
    
    return test_acc, test_loss

创建超参数

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.01)  # 动态调整学习率

5、模型训练

import copy

epochs = 10

train_acc, train_loss, test_acc, test_loss = [], [], [], []

best_acc = 0

for epoch in range(epochs):
    model.train()
    epoch_train_acc, epoch_train_loss = train(model, train_dl, optimizer, loss_fn)
    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    
    model.eval()
    epoch_test_acc, epoch_test_loss = test(model, test_dl, loss_fn)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)
    
    if best_acc is not None and epoch_test_acc > best_acc:
        # 动态调整学习率
        scheduler.step()
        best_acc = epoch_test_acc
        best_model = copy.deepcopy(model)  # 保存模型
    
    # 当前学习率
    lr = optimizer.state_dict()['param_groups'][0]['lr']
    
    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
    print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss,  epoch_test_acc*100, epoch_test_loss, lr))
    
# 保存最佳模型到文件
path = './best_model.pth'
torch.save(best_model.state_dict(), path) # 保存模型参数

Epoch: 1, Train_acc:79.9%, Train_loss:0.562, Test_acc:86.9%, Test_loss:0.392, Lr:5.00E-01
Epoch: 2, Train_acc:89.7%, Train_loss:0.313, Test_acc:88.9%, Test_loss:0.346, Lr:5.00E-01
Epoch: 3, Train_acc:91.2%, Train_loss:0.269, Test_acc:89.6%, Test_loss:0.329, Lr:5.00E-01
Epoch: 4, Train_acc:92.0%, Train_loss:0.243, Test_acc:89.8%, Test_loss:0.319, Lr:5.00E-01
Epoch: 5, Train_acc:92.6%, Train_loss:0.224, Test_acc:90.2%, Test_loss:0.315, Lr:5.00E-03
Epoch: 6, Train_acc:93.3%, Train_loss:0.207, Test_acc:90.6%, Test_loss:0.297, Lr:5.00E-03
Epoch: 7, Train_acc:93.4%, Train_loss:0.204, Test_acc:90.7%, Test_loss:0.295, Lr:5.00E-03
Epoch: 8, Train_acc:93.4%, Train_loss:0.203, Test_acc:90.7%, Test_loss:0.294, Lr:5.00E-03
Epoch: 9, Train_acc:93.4%, Train_loss:0.202, Test_acc:90.8%, Test_loss:0.293, Lr:5.00E-03
Epoch:10, Train_acc:93.4%, Train_loss:0.201, Test_acc:90.7%, Test_loss:0.293, Lr:5.00E-03

6、结果展示

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息
plt.rcParams['font.sans-serif']    = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号
plt.rcParams['figure.dpi']         = 100        #分辨率

epoch_length = range(epochs)

plt.figure(figsize=(12, 3))

plt.subplot(1, 2, 1)
plt.plot(epoch_length, train_acc, label='Train Accuaray')
plt.plot(epoch_length, test_acc, label='Test Accuaray')
plt.legend(loc='lower right')
plt.title('Accurary')

plt.subplot(1, 2, 2)
plt.plot(epoch_length, train_loss, label='Train Loss')
plt.plot(epoch_length, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Loss')

plt.show()

​

​

7、预测

model.load_state_dict(torch.load("./best_model.pth"))
model.eval() # 模型评估

# 测试句子
test_sentence = "This is a news about Technology"

# 转换为 token
token_ids = vocab(tokenizer(test_sentence))   # 切割分词--> 词典序列
text = torch.tensor(token_ids, dtype=torch.long).to(device)  # 转化为tensor
offsets = torch.tensor([0], dtype=torch.long).to(device)

# 测试，注意：不需要反向求导
with torch.no_grad():
    output = model(text, offsets)
    predicted_label = output.argmax(1).item()

# 输出结果
class_names = ["World", "Sports", "Business", "Science and Technology"]
print(f"预测类别: {class_names[predicted_label]}")

预测类别: Science and Technology