| --- |
| 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) |
|
|
