This demo shows the functionality of the Voximplant instant messaging SDK, including silent supervision by a bot.

This is a simple python program which uses a machine learning model to detect toxicity in tweets, developed in Flask.

AntiToxicBot is a bot that detects toxics in a chat using Data Science and Machine Learning technologies. The bot will warn admins about toxic users. Also, the admin can allow the bot to ban toxics.

A supervised learning based tool to identify toxic code review comments

This is a simple python program which uses a machine learning model to detect toxicity in tweets, GUI in Tkinter.

NLP deep learning model for multilingual toxicity detection in text 📚

Telegram bot that detects toxic comments based on Perspective API

Simple Multi-Language HTTP Server Text Toxicity Detector

It is a trained Deep Learning model to predict different level of toxic comments. Toxicity like threats, obscenity, insults, and identity-based hate.

This work focuses on the development of machine learning models, in particular neural networks and SVM, where they can detect toxicity in comments. The topics we will be dealing with: a) Cost-sensitive learning, b) Class imbalance

This library can detect toxicity in the text or string or content and in return provide you the toxicity percentage in text or content with toxic words found in text.

An Explainable Toxicity detector for code review comments. Published in ESEM'2023

The Toxic Comment Detector is a tool powered by Hugging Face’s unitary/toxic-bert model, designed to identify harmful, offensive, or abusive language in real time. Built with a ReactJS frontend and a Flask backend, it provides detailed insights into toxicity levels, enabling safer online environments.

An AI-powered content moderation system using Python and Hugging Face Transformers. Combines rule-based filtering and machine learning to detect and block toxic, profane, and politically sensitive content, built for developers and communities to create safer, positive online spaces.

This repository presents a comprehensive comparison of traditional machine learning, deep learning, encoder-based, and decoder-based large language models (LLMs) for Chinese toxic comment detection.

🤖 Intelligent AI Agent for Real-time Content Moderation 97.5% accuracy | Multi-stage ML pipeline | Production-ready Zero-tier filtering + Embeddings + Fine-tuned BERT + RAG

This is a application to analyse toxicity in social media using BERT and context analysis and aims to reduce toxicity

Clean up your platform with AI-powered toxicity detection. This Flask-based REST API uses Detoxify & PyTorch to detect abusive, hateful, or toxic content in real time.

A reproducible evaluation pipeline for assessing toxic content in language model outputs using Detoxify, developed as part of a 10-week research project.

LLM-inspired BiLSTM pipeline for real-time, multi-label toxicity inference across adversarial discourse modalities.