toxigen_hatebert

Introduction
TOXIGEN_HATEBERT is a model designed for detecting implicit hate speech. It originates from the paper "ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection." The model is tailored for text classification tasks and is built using the BERT architecture. It supports English language processing and is compatible with PyTorch and the Transformers library.

Architecture
The model employs the BERT architecture, which is well-suited for natural language understanding tasks like text classification. It leverages the robust capabilities of BERT to identify subtle forms of hate speech in text.

Training
TOXIGEN_HATEBERT was trained using a large-scale dataset specifically generated for the task of detecting adversarial and implicit hate speech. Details about the training dataset and methodology are available in the associated GitHub repository.

Guide: Running Locally

1. Clone the repository or download the model files from Hugging Face's model hub.
2. Install the necessary dependencies, including PyTorch and Transformers.
3. Load the model using the Hugging Face Transformers library.
4. Prepare your text data for classification and run the inference using the model.

For better performance, especially with large datasets, consider using cloud GPU services like AWS, Google Cloud Platform, or Azure.

License
For information on licensing, please refer to the specific terms and conditions provided in the project's GitHub repository or on the Hugging Face model page.