Toxicity Detector
Toxicity Detector – AI-Powered Comment Moderation
Toxicity Detector is a machine learning application built to identify and flag toxic content in user-generated text. It classifies whether an input comment is toxic or non-toxic using neural networks and natural language processing techniques.
Context
Online communities thrive when users feel safe. But moderating content manually doesn’t scale. Toxicity Detector uses deep learning to assist in flagging hate speech, harassment, and offensive language — in real time.
This project was inspired by the need for automated content moderation tools that can keep up with the fast pace and scale of digital communication.
Key Features & Design
- Real-Time Classification – Input any text and instantly get toxicity predictions.
- Interactive Web UI – Built with Gradio for simple and clean user interaction.
- Preprocessing Pipeline – Tokenization, embedding, and text cleaning before prediction.
- Explainable Results – Binary classification with clear toxic vs non-toxic output.
- Deploy-Ready Model – Hosted and live on Hugging Face Spaces.
Built to be lightweight and responsive, the Gradio interface makes testing and using the model seamless — even for non-technical users.
Model Architecture
- Tokens vs Embeddings – Raw text is tokenized, and tokens are mapped to vector embeddings capturing semantic meaning.
- Neural Network Layers – The model processes these embeddings using dense layers to predict toxicity.
- Training Approach – Supervised learning with labeled comment data for binary classification.
Learning Journey
This project helped reinforce some important machine learning and NLP concepts:
- Tokens ≠ Embeddings – Understanding how text gets numerically represented.
- Neural Networks for NLP – Hands-on experience with deep learning applied to language.
- Gradio UI – Fast way to deploy and test ML models with user input.
Resources That Helped
Tech Stack
Python, TensorFlow/Keras, Gradio, Jupyter Notebook, Google Colab, Hugging Face Spaces