roberta hate speech dynabench r4 target

Introduction

The R4 Target model is part of the "Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection" research. It focuses on enhancing hate speech detection using dynamically generated datasets. This model uses the RoBERTa architecture and is designed to classify text in English.

Architecture

The model is based on the RoBERTa architecture, which is a robustly optimized BERT approach. It leverages large-scale unsupervised learning of language representations, and it is specifically fine-tuned for hate speech detection tasks.

Training

The training process involves using dynamically generated datasets to improve the model's ability to detect online hate speech. This approach is outlined in the paper "Learning from the Worst," which demonstrates how such datasets can enhance detection capabilities. The model is trained using PyTorch and is compatible with Safetensors for efficient deployment.

Guide: Running Locally

1. Clone the Repository: Start by cloning the model repository from Hugging Face.
2. Install Dependencies: Ensure you have Python and PyTorch installed. Additional dependencies can be managed using a requirements.txt file if provided.
3. Download the Model: Use the Hugging Face Transformers library to load the model.
4. Run Inference: Utilize the model's inference capabilities for text classification tasks.

For optimal performance, especially during training and inference, using a cloud GPU service like AWS, Google Cloud, or Azure is recommended.

License

The R4 Target model is available on Hugging Face under specific licensing terms. Users should review these terms on the model's page to ensure compliance with any usage restrictions or requirements.