Data from "HateCheck: Functional Tests for Hate Speech Detection Models" (Röttger et al., ACL 2021)

Overview

In this repo, you can find the data from our ACL 2021 paper "HateCheck: Functional Tests for Hate Speech Detection Models".

  • "test_suite_cases.csv" contains the full test suite (3,728 cases in 29 functional tests).
  • "test_suite_annotations.csv" provides detailed annotation outcomes for each case in the test suite.
  • The corresponding "all_" files cover all 3,901 cases that were initially generated, from which 173 were excluded from the test suite due to fewer than four out five annotators agreeing with our gold standard label.
  • "template_placeholders.csv" contains the tokens that the placeholders in the case templates are replaced with for generating the test cases.

"test_suite_cases.csv" and "all_cases.csv"

functionality The shorthand for the functionality tested by the test case.

case_id The unique ID of the test case (assigned to each of the 3,901 cases we initially generated)

test_case The text of the test case.

label_gold The gold standard label (hateful/non-hateful) of the test case. All test cases within a given functionality have the same gold standard label.

target_ident Where applicable, the protected group targeted or referenced by the test case. We cover seven protected groups in the test suite: women, trans people, gay people, black people, disabled people, Muslims and immigrants.

direction For hateful cases, the binary secondary label indicating whether they are directed at an individual as part of a protected group or aimed at the group in general.

focus_words Where applicable, the key word or phrase in a given test case (e.g. "cut their throats").

focus_lemma Where applicable, the corresponding lemma (e.g. "cut sb. throat").

ref_case_id For hateful cases, where applicable, the ID of the simpler hateful case which was perturbed to generate them. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted.

ref_templ_id The equivalent, but for template IDs.

templ_id The unique ID of the template from which the test case was generated (assigned to each of the 866 cases and templates from which we generated the 3,901 initial cases).


"test_suite_annotations.csv" and "all_annotations.csv"

functionality, case_id, templ_id, test_case, label_gold See above.

label_[1:10] The label provided for the test case by a given annotator. We recruited and trained a team of ten annotators. Each test case was annotated by exactly five annotators.

count_label_h The number of annotators who labeled a given test case as hateful.

count_label_nh The number of annotators who labeled a given test case as non-hateful.

label_annot_maj The majority label.

Owner
Paul Röttger
DPhil Student in Social Data Science at the University of Oxford. Interested in NLP and hate speech research.
Paul Röttger
Autonomous Robots Kalman Filters

Autonomous Robots Kalman Filters The Kalman Filter is an easy topic. However, ma

20 Jul 18, 2022
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution

WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution This code belongs to the paper [1] available at https://arx

Fabian Altekrueger 5 Jun 02, 2022
2021 credit card consuming recommendation

2021 credit card consuming recommendation

Wang, Chung-Che 7 Mar 08, 2022
Breast cancer is been classified into benign tumour and malignant tumour.

Breast cancer is been classified into benign tumour and malignant tumour. Logistic regression is applied in this model.

1 Feb 04, 2022
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand Introduction We propose a generalization of leaderboards, bidimensional leader

4 Dec 03, 2022
YOLO-v5 기반 단안 카메라의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adaptive Cruise Control 기능 구현

자율 주행차의 영상 기반 차간거리 유지 개발 Table of Contents 프로젝트 소개 주요 기능 시스템 구조 디렉토리 구조 결과 실행 방법 참조 팀원 프로젝트 소개 YOLO-v5 기반으로 단안 카메라의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adap

14 Jun 29, 2022
This repo implements a 3D segmentation task for an airport baggage dataset.

3D CT Scan Segmentation With Occupancy Network This repo implements a 3D superresolution segmentation task for an airport baggage dataset. Our final p

Christoph Reich 2 Mar 28, 2022
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021 [WIP] The code for CVPR 2021 paper 'Disentangled Cycle Consistency for H

ChongjianGE 94 Dec 11, 2022
PerfFuzz: Automatically Generate Pathological Inputs for C/C++ programs

PerfFuzz Performance problems in software can arise unexpectedly when programs are provided with inputs that exhibit pathological behavior. But how ca

Caroline Lemieux 125 Nov 18, 2022
Repository for training material for the 2022 SDSC HPC/CI User Training Course

hpc-training-2022 Repository for training material for the 2022 SDSC HPC/CI Training Series HPC/CI Training Series home https://www.sdsc.edu/event_ite

sdsc-hpc-training-org 21 Jul 27, 2022
PyoMyo - Python Opensource Myo library

PyoMyo Python module for the Thalmic Labs Myo armband. Cross platform and multithreaded and works without the Myo SDK. pip install pyomyo Documentati

PerlinWarp 81 Jan 08, 2023
K-FACE Analysis Project on Pytorch

Installation Setup with Conda # create a new environment conda create --name insightKface python=3.7 # or over conda activate insightKface #install t

Jung Jun Uk 7 Nov 10, 2022
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
Top #1 Submission code for the first https://alphamev.ai MEV competition with best AUC (0.9893) and MSE (0.0982).

alphamev-winning-submission Top #1 Submission code for the first alphamev MEV competition with best AUC (0.9893) and MSE (0.0982). The code won't run

70 Oct 29, 2022
naked is a Python tool which allows you to strip a model and only keep what matters for making predictions.

naked is a Python tool which allows you to strip a model and only keep what matters for making predictions. The result is a pure Python function with no third-party dependencies that you can simply c

Max Halford 24 Dec 20, 2022
Analyzes your GitHub Profile and presents you with a report on how likely you are to become the next MLH Fellow!

Fellowship Prediction GitHub Profile Comparative Analysis Tool Built with BentoML Table of Contents: Features Disclaimer Technologies Used Contributin

Damir Temir 51 Dec 29, 2022
Context-Sensitive Misspelling Correction of Clinical Text via Conditional Independence, CHIL 2022

cim-misspelling Pytorch implementation of Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence, CHIL 2022. This model (

Juyong Kim 11 Dec 19, 2022
OOD Generalization and Detection (ACL 2020)

Pretrained Transformers Improve Out-of-Distribution Robustness How does pretraining affect out-of-distribution robustness? We create an OOD benchmark

littleRound 57 Jan 09, 2023
Pytorch implementation of forward and inverse Haar Wavelets 2D

Pytorch implementation of forward and inverse Haar Wavelets 2D

Sergei Belousov 9 Oct 30, 2022
PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

1 May 31, 2022