toxicity-classification

Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers.

Voice safety classifier

A web-app to identify toxic comments in a youtube channel and delete them.

Streams block game

This repository contains the code for the paper: "DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances"

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

A revolutionary AI-powered platform to help you solve doubts instantly, make learning easy, and achieve academic success.

NLP deep learning model for multilingual toxicity detection in text 📚

Module for predicting toxicity messages in Russian and English

Toxformer is an attempt at using transformers to predict the toxicity of molecules from their molecular structure using the T3DB database.

Using Language Models to Identify and Classify Toxicity Inside In-Game Chat

Offensive Language Identification Dataset for Brazilian Portuguese.

This repository makes available a new dataset for toxicity detection in Brazilian Portuguese from the work accepted by the 16th International Conference on Computational Processing of Portuguese (PROPOR 2024). The data collected is from the most popular Brazilian subreddits in 2022.

An AI to Scan for Toxic Tweets

Classifying users on social media, using text embeddings from OpenAI and others

Simple Multi-Language HTTP Server Text Toxicity Detector

Fast text toxicity classification model

Sentio is a sentiment & toxicity analysis web app built with LSTM using TensorFlow and Streamlit. It classifies text comments as Positive, Negative, or Neutral and detects Toxic content in the comments, perfect for analyzing reviews, social media posts, or feedback

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