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
Keras implementations of Generative Adversarial Networks.

This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as

Erik Linder-Norén 8.9k Jan 04, 2023
A PyTorch implementation of Implicit Q-Learning

IQL-PyTorch This repository houses a minimal PyTorch implementation of Implicit Q-Learning (IQL), an offline reinforcement learning algorithm, along w

Garrett Thomas 30 Dec 12, 2022
Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection Main requirements torch = 1.0 torchvision = 0.2.0 Python 3 Environm

15 Apr 04, 2022
Reproducing Results from A Hybrid Approach to Targeting Social Assistance

title author date output Reproducing Results from A Hybrid Approach to Targeting Social Assistance Lendie Follett and Heath Henderson 12/28/2021 html_

Lendie Follett 0 Jan 06, 2022
Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt. This is done by

Mehdi Cherti 135 Dec 30, 2022
A 1.3B text-to-image generation model trained on 14 million image-text pairs

minDALL-E on Conceptual Captions minDALL-E, named after minGPT, is a 1.3B text-to-image generation model trained on 14 million image-text pairs for no

Kakao Brain 604 Dec 14, 2022
Auto White-Balance Correction for Mixed-Illuminant Scenes

Auto White-Balance Correction for Mixed-Illuminant Scenes Mahmoud Afifi, Marcus A. Brubaker, and Michael S. Brown York University Video Reference code

Mahmoud Afifi 47 Nov 26, 2022
DrQ-v2: Improved Data-Augmented Reinforcement Learning

DrQ-v2: Improved Data-Augmented RL Agent Method DrQ-v2 is a model-free off-policy algorithm for image-based continuous control. DrQ-v2 builds on DrQ,

Facebook Research 234 Jan 01, 2023
Pytoydl: A toy deep learning framework built upon numpy.

Documents: https://pytoydl.readthedocs.io/zh/latest/ Pytoydl A toy deep learning framework built upon numpy. You can star this repository to keep trac

28 Dec 10, 2022
The final project of "Applying AI to 3D Medical Imaging Data" from "AI for Healthcare" nanodegree - Udacity.

Quantifying Hippocampus Volume for Alzheimer's Progression Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder that result

Omar Laham 1 Jan 14, 2022
This is the official implementation of "One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval".

CORA This is the official implementation of the following paper: Akari Asai, Xinyan Yu, Jungo Kasai and Hannaneh Hajishirzi. One Question Answering Mo

Akari Asai 59 Dec 28, 2022
This repository contains the code needed to train Mega-NeRF models and generate the sparse voxel octrees

Mega-NeRF This repository contains the code needed to train Mega-NeRF models and generate the sparse voxel octrees used by the Mega-NeRF-Dynamic viewe

cmusatyalab 260 Dec 28, 2022
A library for optimization on Riemannian manifolds

TensorFlow RiemOpt A library for manifold-constrained optimization in TensorFlow. Installation To install the latest development version from GitHub:

Oleg Smirnov 83 Dec 27, 2022
Repository for GNSS-based position estimation using a Deep Neural Network

Code repository accompanying our work on 'Improving GNSS Positioning using Neural Network-based Corrections'. In this paper, we present a Deep Neural

32 Dec 13, 2022
Recurrent Conditional Query Learning

Recurrent Conditional Query Learning (RCQL) This repository contains the Pytorch implementation of One Model Packs Thousands of Items with Recurrent C

Dongda 4 Nov 28, 2022
Volsdf - Volume Rendering of Neural Implicit Surfaces

Volume Rendering of Neural Implicit Surfaces Project Page | Paper | Data This re

Lior Yariv 221 Jan 07, 2023
CAST: Character labeling in Animation using Self-supervision by Tracking

CAST: Character labeling in Animation using Self-supervision by Tracking (Published as a conference paper at EuroGraphics 2022) Note: The CAST paper c

15 Nov 18, 2022
Robust & Reliable Route Recommendation on Road Networks

NeuroMLR: Robust & Reliable Route Recommendation on Road Networks This repository is the official implementation of NeuroMLR: Robust & Reliable Route

4 Dec 20, 2022
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Segmentation Transformer Implementation of Segmentation Transformer in PyTorch, a new model to achieve SOTA in semantic segmentation while using trans

Abhay Gupta 161 Dec 08, 2022
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

TANG, shixiang 6 Nov 25, 2022