---
base_model: BenjaminOcampo/model-hatebert__trained-in-ishate__seed-0
datasets:
- ISHate
language:
- en
library_name: transformers
license: bsl-1.0
metrics:
- f1
- accuracy
tags:
- hate-speech-detection
- implicit-hate-speech
---

This model card documents the demo paper "PEACE: Providing Explanations and
Analysis for Combating Hate Expressions" accepted at the 27th European
Conference on Artificial Intelligence: https://www.ecai2024.eu/calls/demos.

# The Model
This model is a hate speech detector fine-tuned specifically for detecting
implicit hate speech. It is based on the paper "PEACE: Providing Explanations
and Analysis for Combating Hate Expressions" by Greta Damo, Nicolás Benjamín
Ocampo, Elena Cabrio, and Serena Villata, presented at the 27th European
Conference on Artificial Intelligence.

# Training Parameters and Experimental Info
The model was trained using the ISHate dataset, focusing on implicit data.
Training parameters included:
- Batch size: 32
- Weight decay: 0.01
- Epochs: 4
- Learning rate: 2e-5

For detailed information on the training process, please refer to the [model's
paper](https://aclanthology.org/2023.findings-emnlp.441/).

# Datasets
The model was trained on the [ISHate dataset](https://huggingface.co/datasets/BenjaminOcampo/ISHate), specifically
the training part of the dataset which focuses on implicit hate speech.

# Evaluation Results
The model's performance was evaluated using standard metrics, including F1 score
and accuracy. For comprehensive evaluation results, refer to the linked paper.

Authors:
- [Greta Damo](https://grexit-d.github.io/damo.greta.github.io/)
- [Nicolás Benjamín Ocampo](https://www.nicolasbenjaminocampo.com/)
- [Elena Cabrio](https://www-sop.inria.fr/members/Elena.Cabrio/)
- [Serena Villata](https://webusers.i3s.unice.fr/~villata/Home.html)