toxic-comment-classification

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

top 1% solution to toxic comment classification challenge on Kaggle.

An R wrapper for Conversation AI's Perspective API

[AACL 2020] Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis

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

Kaggle: Quora Insincere Questions Classification - detect toxic content to improve online conversations

在Pytorch下，采用LSTM/C-LSTM/CNN等方法，对评论进行多标签分类

This GitHub repository provides an implementation of the paper "MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network" . MAGNET is a state-of-the-art approach for multi-label text classification, leveraging the power of graph neural networks (GNNs) and attention mechanisms.

The Toxic Comment Classification project is an application that uses deep learning to identify toxic comments as toxic, severe toxic, obscene, threat, insult, and identity hate based using various NLP algorithm

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

Набор ноутбуков, в которых решаются различные задачи обработки естественного языка (NLP).

Kaggle Competition: Toxic Comments Classification (Link: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)

From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks

A discord bot capable of detecting toxic messages sent to a server, delete them and warn the author.

Deep Learning for Toxic Comment Classification

Collection of Deep Learning Text Classification Models in Keras; Includes a GPU tutorial.

NLP deep learning model for multilingual toxicity detection in text 📚

The official code to reproduce results from the NACCL2019 paper: White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks

Natural Language Processing: A multi-headed model capable of detecting different types of online discussion toxicity like threats, obscenity, insults, and identity-based hate using Keras RNN LSTM and focal loss to address a hyper-imbalanced dataset.