README.md

metadata

title: ToxiGuard AI
emoji: 🛡️
colorFrom: red
colorTo: purple
sdk: streamlit
sdk_version: 1.35.0
app_file: app.py
pinned: false
license: mit

🛡️ ToxiGuard AI — Multi-label Toxic Comment Detection using BERT

ToxiGuard AI is an advanced NLP-based content moderation system that detects multiple categories of toxic comments using transformer-based deep learning models.
The project uses HuggingFace Transformers, PyTorch, and BERT fine-tuning for real-time toxicity classification across six toxicity categories.

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🚀 Project Overview

Online platforms face massive challenges in moderating harmful user-generated content.
ToxiGuard AI solves this problem using a transformer-powered multi-label NLP pipeline capable of detecting:

• Toxic
• Severe Toxic
• Obscene
• Threat
• Insult
• Identity Hate

Unlike traditional text classification systems, this project uses contextual transformer embeddings to understand semantic meaning, contextual toxicity, and subtle hate speech patterns.

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🎯 Problem Statement

Build a multi-label NLP system capable of classifying toxic social media comments into multiple toxicity categories simultaneously using deep learning transformers.

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💡 Why This Project Matters

Content moderation is one of the largest real-world NLP applications today.

Companies actively working on moderation systems include:

• Meta
• Google
• YouTube
• Reddit
• ShareChat
• Koo
• Discord

This project demonstrates:

• Advanced NLP Engineering
• Transformer Fine-tuning
• Multi-label Deep Learning
• PyTorch Training Pipelines
• HuggingFace Ecosystem
• GPU Acceleration
• Model Deployment

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🧠 Model Architecture

The project uses:

BERT (bert-base-uncased)

Architecture:

• Transformer Encoder
• Self-Attention Mechanism
• Multi-label Sigmoid Output Layer

Loss Function:

• BCEWithLogitsLoss

Output Layer:

• 6-neuron sigmoid classification head

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📊 Dataset

Jigsaw Toxic Comment Classification Dataset

Dataset contains:

• 159,571 Wikipedia comments
• Multi-label annotations
• Real-world toxic language

Labels

Label	Description
toxic	General toxic content
severe_toxic	Extremely toxic content
obscene	Obscene language
threat	Threatening statements
insult	Insulting comments
identity_hate	Hate speech targeting identity

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⚙️ Tech Stack

Machine Learning / NLP

• Python
• PyTorch
• HuggingFace Transformers
• Scikit-learn
• NumPy
• Pandas

Visualization / Deployment

• Streamlit
• Matplotlib

Model / Training

• BERT
• GPU Fine-tuning
• Mixed Precision Training (FP16)

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🔬 Project Pipeline

1. Data Preprocessing

• Text cleaning
• Lowercasing
• Special character handling
• Comment length analysis

2. Exploratory Data Analysis

• Label distribution
• Toxicity co-occurrence
• Comment length statistics

3. Traditional NLP Baseline

• TF-IDF Vectorization
• Logistic Regression
• Binary Relevance Classification
• Threshold Optimization

4. Transformer Fine-tuning

• HuggingFace Tokenization
• BERT Fine-tuning
• Multi-label BCE Loss
• Dynamic Padding
• GPU Training

5. Evaluation

• Macro F1 Score
• Micro F1 Score
• ROC-AUC
• Per-label threshold tuning

6. Deployment

• Streamlit Web App
• Real-time Toxicity Detection
• Probability Visualization

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📈 Model Performance

TF-IDF Baseline

Metric	Score
Macro F1	0.61
Micro F1	0.73
Macro ROC-AUC	0.979

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BERT Fine-tuned Model

Metric	Score
Macro F1	0.666
Micro F1	0.801
ROC-AUC	0.992

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🧪 Key Features

✅ Multi-label Toxicity Detection
✅ Transformer-based NLP
✅ Contextual Toxicity Understanding
✅ Real-time Inference
✅ Probability-based Predictions
✅ Threshold Optimization
✅ Streamlit UI Deployment
✅ GPU Accelerated Training

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📂 Project Structure

ToxiGuard-BERT/
│
├── toxiguard-bert/
│   ├── config.json
│   ├── model.safetensors
│   ├── tokenizer.json
│   ├── tokenizer_config.json
│   └── special_tokens_map.json
│
├── app.py
├── utils.py
├── labels.py
├── style.css
├── requirements.txt
├── README.md
│
└── assets/

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▶️ Installation

Clone Repository

git clone https://github.com/MohdFaizan22/ToxiGuard-BERT.git
cd ToxiGuard-BERT

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Create Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate

Linux / Mac

python3 -m venv venv
source venv/bin/activate

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Install Dependencies

pip install -r requirements.txt

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▶️ Run Application

streamlit run app.py

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🖥️ Web App Features

• Real-time toxicity prediction
• Toxicity confidence scores
• Multi-label classification
• Safe vs Toxic detection
• Interactive probability bars
• Modern dark UI

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🧪 Example Predictions

Input

You are an absolute idiot and nobody likes you.

Output

{
 'toxic': 0.998,
 'severe_toxic': 0.42,
 'obscene': 0.71,
 'threat': 0.01,
 'insult': 0.96,
 'identity_hate': 0.02
}

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🧠 Key NLP Concepts Used

• Transformer Architecture
• Self-Attention
• Contextual Embeddings
• Multi-label Classification
• BCEWithLogitsLoss
• Dynamic Padding
• Tokenization
• Threshold Optimization
• Mixed Precision Training

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📌 Future Improvements

• RoBERTa / DeBERTa Upgrade
• Multilingual Toxicity Detection
• Explainable AI Attention Maps
• FastAPI Backend
• Docker Deployment
• ONNX Optimization
• Real-time Moderation Dashboard
• Toxic Span Detection

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📚 Learning Outcomes

Through this project, I learned:

• Transformer Fine-tuning
• Multi-label NLP
• HuggingFace Ecosystem
• PyTorch Deep Learning
• GPU Training Optimization
• NLP Inference Pipelines
• Real-world Content Moderation Systems
• Streamlit Deployment

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🤝 Acknowledgements

• Kaggle Jigsaw Toxic Comment Dataset
• HuggingFace Transformers
• PyTorch
• Streamlit

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