Modeling Offensive Language as a Distinct Class for Hate Speech Detection

This repository is part of my master's thesis project,"Modeling Offensive Language as a Distinct Class for Hate Speech Detection" (Kim, 2025), supervised by Dr. Antske Fokkens and Dr. Hennie van der Vliet. The project explored how modeling offensive (but not hateful) language as a distinct class impacts the task of detection of hate speech. Using a ternary classification scheme (Hateful, Offensive, Clean), I fine-tuned and evaluated a RoBERTa-base model in the full three-class setup and in binary variants where two classes are merged or the offensive class is removed (Hate vs. Non-hate, Non-clean vs. Clean, and Hate vs. Clean). The code used in this study includes my modifications and extensions of Khurana et al. (2025)'s code.

HateCheck-XR

In the project, to probe model behavior beyond set-internal performance, I revised both HateCheck (Röttger et al., 2021) and an existing extension by Khurana et al. (2025), aligning them with the ternary system by re-annotating them and correcting errors present in the extension. The resulting dataset, HateCheck-XR, is available in this repository, under "dataset" in a csv format.

Folder structure

Project
├─ hs_generalization/  
│  ├─ __init__.py
│  ├─ modes.py
│  ├─ train.py
│  ├─ test.py
│  └─ uitls.py    
├─ tools/
│  └─ run_many.py          
├─ configs/
│  └─ example.json
├─ dataset/
│ ├─ davidson/
│ └─ hatecheck/hatecheck-xr.csv
├─ requirements.txt
└─ README.md

Set-up

Set up the environment like the following:

# Create environment.
conda create -n hs-generalization python=3.9
conda activate hs-generalization

# Install packages.
python setup.py develop
pip install -r requirements.txt

Training

Create a config file and run the following:

python -m hs_generalization.train -c configs\train\example.json

Evaluation

Create a config file and run like the example:

#running based on a single seed/checkpoint
python -m hs_generalization.test -c configs\test\example.json --dataset davidson --eval-mode 3class --train-mode 3class --seed 5 --checkpoint "outputs\davidson\RoBERTa-base\3class\RoBERTa-base_0.pt"

# If you want to run multiple files at once, you can use run_many.py like the following example:
python tools/run_many.py ^
  -c configs/test/test.json ^
  --dataset hatecheck_xr ^
  --eval-mode 3class ^
  --train-mode 3class ^
  --seeds 7 222 550 999 3111 ^
  --ckpt-pattern "outputs/davidson/RoBERTa-base/3class/*.pt" ^
  --hatecheck-csv dataset/hatecheck/hatecheck-xr.csv