toxicity

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

The world's largest social media toxicity dataset.

A Python Implementation of Measures for Order Flow Risk, e.g. VPIN

SFU Opinion and Comments Corpus

Detect text toxicity in a simple way, using React. Based in a Keras model, loaded with Tensorflow.js.

A static code analysis tool for JavaScript and TypeScript.

The toxEval R-package includes a set of functions to analyze, visualize, and organize measured concentration data as it relates to chosen biological effects benchmarks. See https://doi-usgs.github.io/toxEval/ for more details

A Python package to compute HONEST, a score to measure hurtful sentence completions in language models. Published at NAACL 2021.

Smash molecule and obtain significant fragments

Highlight refactors and efforts to keep code base lean

A collection of transformer-based models and developmental scripts presented in the publication "Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms".

Data from "Crowdsourcing of Parallel Corpora: the Case of Style Transfer for Detoxification" paper

Toxic text (comments/posts etc) detection library

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

Datasets used in the tox21 challenge

In silico platform to analize MD trajectories using metrics, clustering and machine&deep learning techniques

Module for predicting toxicity messages in Russian and English

Prediction and mechanistic analysis of Drug-Induced Liver Injury (DILI) based on chemical structure

Offensive Language Identification Dataset for Brazilian Portuguese.