Skip to content

Latest commit

 

History

History
99 lines (75 loc) · 2.94 KB

README.md

File metadata and controls

99 lines (75 loc) · 2.94 KB

BERT-TextCNN: Multi-label Classification & Sentiment Analysis

A dual-task NLP system combining BERT embeddings with TextCNN for:

  • Multi-label Text Classification - Assign multiple category labels to each input
  • Sentiment Analysis - Classify sentiment as Positive / Neutral / Negative

This project integrates and improves upon my previous works:

Dataset

The dataset consists of e-commerce product Q&A data from Taobao and JD.com platforms.

Classification Labels (12 categories):

ID Label Description
0 功效 Efficacy
1 适用人群 Target Users
2 使用方法 Usage Method
3 其他 Other
4 属性 Attributes
5 使用感受 Usage Experience
6 不良反应 Side Effects
7 竞品对比 Competitor Comparison
8 包装 Packaging
9 价格 Price
10 渠道 Channel
11 物流 Logistics

Sentiment Labels (3 classes):

ID Label Description
0 中评 Neutral
1 好评 Positive
2 差评 Negative

Features

Module Task Output
TextCNN_Single Single-label classification 12 categories
TextCNN_Multi Multi-label classification Multiple labels per input
TextCNN_Sentiment Sentiment analysis Positive / Neutral / Negative
online Production inference API Batch prediction support

Quick Start

# Environment setup
conda activate nlp

# Training
cd TextCNN_Single && python train.py
cd TextCNN_Multi && python train.py
cd TextCNN_Sentiment && python train.py

# Inference
cd online && python demo.py

API Usage

# Classification (Multi-label)
from models.classification.functions import predict
label_ids, label_texts = predict(['保湿效果怎么样？', '小孩子可以用吗？'])

# Sentiment Analysis
from models.sentiment.functions import predict
label_ids, label_texts = predict([['是正品吗？', '效果不错']])

Project Structure

├── TextCNN_Single/      # Single-label classification training
├── TextCNN_Multi/       # Multi-label classification training
├── TextCNN_Sentiment/   # Sentiment analysis training
└── online/              # Production inference module
    └── models/
        ├── bert-base-chinese/   # Pre-trained BERT
        ├── classification/      # Classification inference
        └── sentiment/           # Sentiment inference

Tech Stack

  • Model: BERT (bert-base-chinese) + TextCNN
  • Framework: PyTorch, HuggingFace Transformers
  • Environment: Python 3.12, CUDA supported

日本語版 README