The dataset used in this project is the Toxic Comment Classification Challenge from Kaggle. The dataset contains approximately 159,000 comments from Wikipedia talk pages that have been labeled by human annotators as toxic or non-toxic.

The six detectable types are toxic, severe toxic, obscene, threat, insult, and identity hate. The model is based on the pre-trained BERT-Base, English Uncased model and was finetuned on the Toxic Comment Classification Dataset using the Huggingface BERT Pytorch repository.

The competition could be found here: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge · As a group of students with great interests in Natural Language Processing, as well as making online discussion more productive and respectful, we determined to work on this project and aim to build a model that is capable of detecting different types of toxicity like threats, obsenity, insults, and identity-based hate. The dataset we are using consists of comments from Wikipedia’s talk page edits.

This repository contains the code of our paper Data Integration for Toxic Comment Classification: Making More Than 40 Datasets Easily Accessible in One Unified Format accepted for publication at this year's ACL workshop on Online Abuse and Harms ...

In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. You’ll be using a dataset of comments from Wikipedia’s talk page edits.

In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. You’ll be using a dataset of comments from Wikipedia’s talk page edits.

This repo contains code for toxic ... is to detect and classify toxic comments in online conversations using Jigsaw's Toxic Comment Classification dataset. - alessiococchie......

The dataset consists of large number of Wikipedia comments wh ... Fine-tuning FLAN-T5 with PPO and PEFT to generate less toxic text summaries. This notebook leverages Meta AI's hate speech reward model and utilizes RLHF techniques for improved safety. nlp toxic-comment-classification hate-speech-detection toxicity-analysis ppo-pytorch dialogue-summarization generative-ai detoxification reward-model

The dataset contains the comment text and labels for the different types of toxicity.

In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. You’ll be using a dataset of comments from Wikipedia’s talk page edits.

In this model, many toxic comments have been fed to build a Bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model for fulfilling the purpose. ... You can downloaded the dataset from kaggle.

Dataset consists of 1000 English examples gathered from different popular social media platform. Each 500 examples in it are labeled as "toxic" and "non-toxic", respectively.

Dataset has a large number of comments from Wikipedia talk page edits. They have been labeled by human raters for toxic behavior.

Trained on 159,571 Wikipedia talk-page comments from the Jigsaw Toxic Comment Classification Challenge.

In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. You’ll be using a dataset of comments from Wikipedia’s talk page edits.

This project has been developed by my collegues and I on a dataset of toxic comments. Our task was to perform a multi-label classification analysis on about 200'000 comments in order to identify which of them were to be considered as "toxic", "severe toxic", "obscene", "insult", "threat" and/or "identity hate" (6 possible classes of toxicity).

Detecting different types of toxicity like threats, obscenity, insults, and identity-based hate given a text using Toxic Comment dataset from jigsaw on Kaggle. - anandborad/Comment-Toxicity-Hate-Sp...

The dataset consists of thousands of comments collected from various online platforms, each labeled as toxic or non-toxic. The performance of the Comment Toxicity Detection model is evaluated using standard metrics such as accuracy, precision, ...

The problem with this is that people will frequently write things they shouldn’t, and to maintain a positive community this toxic content and the users posting it need to be removed quickly. But they don’t have the resources to hire full-time moderators to review every comment. This problem led the Conversation AI team1, owned by Alphabet, to develop a large open dataset of labeled Wikipedia Talk Page comments, which will be the dataset used for the project.

Comprehensive Toxicity Detection: Classifies comments into six categories of toxicity. Interactive Interface: Gradio-based web app for real-time comment scoring. High Performance: Trained on a large dataset for accurate and reliable detection.