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bert-ptuning-new-retail-decision

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This project is based on "user comments from e-commerce platforms" and uses the "BERT + P-Tuning (soft template)" method to accurately classify the comment texts

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# 🛒 New Retail Industry Decision Evaluation System Based on BERT + P-Tuning
## 📝 Project Background
Text is a crucial medium for information dissemination. Correctly classifying text data to better organize and utilize this information is of significant research importance. Text classification, a classic task in the field of Natural Language Processing (NLP), is widely applied in scenarios such as public opinion monitoring and sentiment analysis.
### Pain Points and Challenges
Many methods exist for text classification, such as the classic Text-CNN, Text-RNN, and fine-tuning based on pre-trained models like BERT. However, these methods are mostly based on supervised learning requiring large amounts of labeled data. In many real-world scenarios, due to domain specificity and high annotation costs, there is a lack of labeled training data. This prevents models from learning parameters effectively, making them prone to overfitting. Therefore, how to train a high-performing classification model with a small amount of data is a current research hotspot.
### Solution and Application Value
This project uses "e-commerce platform user reviews" as a background and implements accurate classification of review text based on the BERT + P-Tuning (soft template) method.
Enhance User Experience: By gaining a deep understanding of user reviews for different products or services, platforms can quickly respond to user needs and improve their offerings.
Personalized Recommendations: Automatic classification lays the foundation for personalized recommendations, helping users more easily find products that match their preferences.
Reduce Operational Costs: It replaces laborious manual processing work.
Market Feedback Analysis: Through review analysis, e-commerce platforms can quickly obtain market feedback and provide targeted data to merchants, aiding in the formulation of precise operational strategies.
## ⚙️ Environment Setup
This project is implemented based on PyTorch + Transformers. Before running, please ensure Python is installed in your environment and install the following dependencies:
```
pip install torch transformers datasets evaluate matplotlib rich scikit-learn requests
```
### 📂 Project Architecture and Directory Structure
This project adopts a layered architecture, covering the entire process from data processing to model deployment.
```
/
├── bert-base-chinese/      # Path for the BERT pre-trained model
├── checkpoints/            # Path for saving models (model_400, model_best)
├── data/                   # Data storage path
│   ├── train.txt           # Training set
│   ├── dev.txt             # Validation set
│   └── verbalizer.txt      # Label mapping file
├── data_handle/            # Data processing module
│   ├── data_loader.py      # Defines DataLoader, responsible for data loading and batch encapsulation
│   └── data_preprocess.py  # Data preprocessing: format conversion, template concatenation
├── utils/                  # Utility package
│   ├── common_utils.py     # General utilities: loss function (MLM Loss), Logits to ID conversion
│   ├── metric_utils.py     # Evaluation metrics: ClassEvaluator (Acc, Precision, Recall, F1)
│   └── verbalizer.py       # Label mapper: maps Labels to Tokens
├── ptune_config.py         # Project configuration file (hyperparameters, path configurations)
├── train.py                # Model training entry point (includes training and validation logic)
└── inference.py            # Model inference entry point
```
### 💻 Core Code Module Introduction
#### 1.Data Preprocessing (data_handle)
data_preprocess.py:
    Purpose: To convert sample data into a format acceptable by the model.
    Core Function: Imports necessary toolkits and defines the convert_example() method. It converts raw text (e.g., computer\tThis laptop looks great) into a numerical format (Input IDs) that the model can understand and concatenates the Prompt template with the raw input text to construct the model's input data.
data_loader.py:
    Purpose: Defines the data loader, responsible for processing raw text data into a format directly usable for model training and encapsulating it into an efficient iterator.
    Core Functions:
    Load Data: Reads the training and validation sets.
    Process Data: Converts text into model input IDs and label IDs.
    Encapsulate Data: Packages the processed data into a DataLoader for efficient batch reading during training.
#### 2. Configuration Management (ptune_config.py)
    Purpose: To centrally manage common project variables.
    Core Function: Defines the ProjectConfig class, which includes hyperparameters like training file paths, number of training epochs, learning rate, and batch size for easy unified adjustment.
#### 3. Utility Components (utils)
    verbalizer.py: Defines the Verbalizer class to establish the mapping relationship between a Label and its corresponding sub-label/Token.
    common_utils.py:
        mlm_loss(): Defines the Masked Language Model loss function.
        convert_logits_to_ids(): Converts the Logits at the Mask position into specific Token IDs.
    metric_utils.py:
Defines the ClassEvaluator class to implement streaming evaluation (without loading all data at once).
Supports calculation of global Accuracy, Weighted F1, as well as Precision, Recall, and F1 for each specific class.
#### 4. Training and Inference (train.py & inference.py)
train.py: Contains the model2train() and evaluate_model() functions, responsible for the model's iterative training and performance evaluation on the validation set.
inference.py: Defines the inference() function, used to load the best-trained model to make predictions on new data.
### 🚀 Quick Start
#### Clone the project:
```
git clone https://github.com/phoenix-zhou/bert-ptuning-new-retail-decision.git
cd bert-ptuning-new-retail-decision
```
### Prepare the data:
Ensure that train.txt, dev.txt, and verbalizer.txt are present in the data/ directory.
#### Start training:
```
python train.py
```
#### Model inference:
```
python inference.py
```
#### 🔗 Additional Resources
Pre-trained Model Download: The bert-base-chinese model can be downloaded from the following Baidu Netdisk link:
Link: https://pan.baidu.com/s/1UnHXBJUpib1m7-LIE4oNjA?pwd=cywd
Password: cywd
##### Detailed Blog Post: For a detailed explanation of the project steps, please refer to the blog post: https://blog.csdn.net/zhoupenghui168/article/details/162432244

------------------------------------------------------------------------------------------------------------

# 基于 BERT + P-Tuning 的新零售行业决策评价系统

##  项目背景 (Project Background)

文本是信息传播的重要途径和载体，将文本数据正确归类，从而更好地组织、利用这些信息，具有重要的研究意义。文本分类致力于解决上述问题，是自然语言处理 (NLP) 领域的经典任务之一，被广泛应用于舆情监测、情感分析等场景中。

### 痛点与挑战
目前实现文本分类的方法很多，如经典的 Text-CNN、Text-RNN、以及基于 BERT 等预训练模型的 Fine-tuning 等。但是这些方法多为建立在具有大量标注数据下的有监督学习。在很多实际场景中，由于领域特殊性和标注成本高，导致标注训练数据缺乏，模型无法有效地学习参数，从而易出现过拟合现象。**因此，如何通过小样本数据训练得到一个性能较好的分类模型是目前的研究热点。**

### 解决方案与应用价值
本项目以 **"电商平台用户评论"** 为背景，基于 **BERT + P-Tuning (软模版)** 方法实现评论文本的准确分类。
- **提升用户体验**：通过深入了解用户对不同商品或服务的评价，平台能够快速回应用户需求，改进产品和服务。
- **个性化推荐**：自动分类为个性化推荐奠定基础，帮助用户更轻松地找到符合其偏好的商品。
- **降低运营成本**：替代了繁重的人工处理工作。
- **市场反馈分析**：通过评论分析，电商平台还能迅速获取市场反馈，为商家提供有针对性的数据，助力制定精准的运营策略。

---

## ️ 环境准备 (Environment Setup)

本项目基于 `PyTorch` + `Transformers` 实现。在运行前，请确保您的环境中已安装 Python，并安装以下依赖包：

```bash
pip install torch transformers datasets evaluate matplotlib rich scikit-learn requests
```
### 项目架构与目录说明 (Project Architecture)
本项目采用分层架构设计，从数据处理到模型上线全流程覆盖
```
/
├── bert-base-chinese/      # BERT预训练模型存放路径
├── checkpoints/            # 模型保存路径 (model_400, model_best)
├── data/                   # 数据存放路径
│   ├── train.txt           # 训练集
│   ├── dev.txt             # 验证集
│   └── verbalizer.txt      # 标签映射文件
├── data_handle/            # 数据处理模块
│   ├── data_loader.py      # 定义DataLoader，负责数据加载与Batch封装
│   └── data_preprocess.py  # 数据预处理：格式转换、模板拼接
├── utils/                  # 工具包
│   ├── common_utils.py     # 通用工具：损失函数(MLM Loss)、Logits转ID
│   ├── metric_utils.py     # 评估指标：ClassEvaluator (Acc, Precision, Recall, F1)
│   └── verbalizer.py       # 标签映射器：Label到Token的映射
├── ptune_config.py         # 项目配置文件 (超参数、路径配置)
├── train.py                # 模型训练入口 (包含训练与验证逻辑)
└── inference.py            # 模型预测入口
```
### 核心代码模块介绍 (Core Modules)
#### 1. 数据预处理 (data_handle)
data_preprocess.py:
目的: 将样本数据转换为模型接受的输入数据。
核心功能: 导入必备工具包，定义 convert_example() 方法。将原始文本（如 电脑\t这款笔记本外观不错）转换成模型能够理解的数字格式（Input IDs），并将 Prompt模板 与原始输入文本进行拼接，构造模型接受的输入数据。
data_loader.py:
目的: 定义数据加载器，负责将原始文本数据加工成模型可以直接训练的格式，并封装成高效的迭代器。
核心功能:
    加载数据：读取训练集和验证集。
    处理数据：将文本转换为模型输入 ID 和标签 ID。
    封装数据：将处理好的数据打包成 DataLoader，以便在训练时按批次（batch）高效读取。
#### 2. 配置管理 (ptune_config.py)
目的: 集中管理项目常用变量。
核心功能: 定义 ProjectConfig 类，包含训练文件路径、模型训练次数 (Epochs)、学习率、Batch Size 等超参数，方便统一调整。
#### 3. 工具组件 (utils)
verbalizer.py: 定义 Verbalizer 类，用于建立 Label 与其对应子 Label/Token 之间的映射关系。
common_utils.py:
    mlm_loss(): 定义掩码语言模型损失函数。
    convert_logits_to_ids(): 将 Mask 位置的 Logits 转换为具体的 Token ID。
metric_utils.py:
    定义 ClassEvaluator 类，实现流式评估（无需一次性加载所有数据）。
    支持计算全局 Accuracy、Weighted F1，以及每个具体类别的 Precision、Recall 和 F1。
#### 4. 训练与推理 (train.py & inference.py)
train.py: 包含 model2train() 和 evaluate_model() 函数，负责模型的迭代训练及在验证集上的性能评估。
inference.py: 定义 inference() 函数，用于加载训练好的最佳模型对新数据进行预测

### 快速开始 (Quick Start)
#### 克隆项目:
```
git clone https://github.com/phoenix-zhou/bert-ptuning-new-retail-decision.git
cd bert-ptuning-new-retail-decision
```
#### 准备数据: 
确保 data/ 目录下包含 train.txt, dev.txt, verbalizer.txt。
#### 开始训练:
```
python train.py
```

#### 模型预测:
```
python inference.py
```
项目中的预训练模型bert-base-chinese下载地址: 通过网盘分享的文件,链接: https://pan.baidu.com/s/1UnHXBJUpib1m7-LIE4oNjA?pwd=cywd 提取码: cywd

##### 项目相关步骤详解见博客: https://blog.csdn.net/zhoupenghui168/article/details/162432244

bert-ptuning-new-retail-decision

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