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
Run Keras models in the browser, with GPU support using WebGL

**This project is no longer active. Please check out TensorFlow.js.** The Keras.js demos still work but is no longer updated. Run Keras models in the

Leon Chen 4.9k Dec 29, 2022
Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting Note: You can find here the accompanying seq2seq RNN forecas

Guillaume Chevalier 1k Dec 25, 2022
Shitty gaze mouse controller

demo.mp4 shitty_gaze_mouse_cotroller install tensofflow, cv2 run the main.py and as it starts it will collect data so first raise your left eyebrow(bo

16 Aug 30, 2022
SemiNAS: Semi-Supervised Neural Architecture Search

SemiNAS: Semi-Supervised Neural Architecture Search This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian

Renqian Luo 21 Aug 31, 2022
Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Deep Learning to Improve Breast

Li Shen 305 Jan 03, 2023
Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline

Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline. The pipeline accepts english text as input and returns the French translation.

Afropunk Technologist 1 Jan 24, 2022
The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question IntentionClassification Benchmark for Text-to-SQL"

TriageSQL The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text

Yusen Zhang 22 Nov 09, 2022
My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

machine-learning-with-graphs My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs Course materials can be

Marko Njegomir 7 Dec 14, 2022
banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services. This library is developed by Bandit ML and ex-authors of Facebook's app

Bandit ML 51 Dec 22, 2022
Retinal vessel segmentation based on GT-UNet

Retinal vessel segmentation based on GT-UNet Introduction This project is a retinal blood vessel segmentation code based on UNet-like Group Transforme

Kent0n 27 Dec 18, 2022
Neural Tangent Generalization Attacks (NTGA)

Neural Tangent Generalization Attacks (NTGA) ICML 2021 Video | Paper | Quickstart | Results | Unlearnable Datasets | Competitions | Citation Overview

Chia-Hung Yuan 34 Nov 25, 2022
Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22)

Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22) Ok-Topk is a scheme for distributed training with sparse gradients

Shigang Li 9 Oct 29, 2022
Dynamic Capacity Networks using Tensorflow

Dynamic Capacity Networks using Tensorflow Dynamic Capacity Networks (DCN; http://arxiv.org/abs/1511.07838) implementation using Tensorflow. DCN reduc

Taeksoo Kim 8 Feb 23, 2021
Learning to See by Looking at Noise

Learning to See by Looking at Noise This is the official implementation of Learning to See by Looking at Noise. In this work, we investigate a suite o

Manel Baradad Jurjo 82 Dec 24, 2022
Run Effective Large Batch Contrastive Learning on Limited Memory GPU

Gradient Cache Gradient Cache is a simple technique for unlimitedly scaling contrastive learning batch far beyond GPU memory constraint. This means tr

Luyu Gao 198 Dec 29, 2022
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

AdaFocusV2 This repo contains the official code and pre-trained models for AdaFo

79 Dec 26, 2022
This thesis is mainly concerned with state-space methods for a class of deep Gaussian process (DGP) regression problems

Doctoral dissertation of Zheng Zhao This thesis is mainly concerned with state-space methods for a class of deep Gaussian process (DGP) regression pro

Zheng Zhao 21 Nov 14, 2022
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

FusionNet_Pytorch FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics Requirements Pytorch 0.1.11 Pyt

Choi Gunho 102 Dec 13, 2022
Code for "OctField: Hierarchical Implicit Functions for 3D Modeling (NeurIPS 2021)"

OctField(Jittor): Hierarchical Implicit Functions for 3D Modeling Introduction This repository is code release for OctField: Hierarchical Implicit Fun

55 Dec 08, 2022
SSD-based Object Detection in PyTorch

SSD-based Object Detection in PyTorch 서강대학교 현대모비스 SW 프로그램에서 진행한 인공지능 프로젝트입니다. Jetson nano를 이용해 pre-trained network를 fine tuning시켜 차량 및 신호등 인식을 구현하였습니다

Haneul Kim 1 Nov 16, 2021