A GPT model built from scratch to identify and classify cyberbullying in online conversations.
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Overview
The Better Threads Project is a custom GPT model designed to identify and classify cyberbullying in online conversations. Built from scratch, this project demonstrates expertise in machine learning, natural language processing, and model training.The model was trained on a curated dataset of online conversations and can distinguish between normal discourse and various forms of cyberbullying, making it valuable for content moderation and online safety applications.
Technical Implementation
Architecture: The model is built using transformer architecture, specifically designed for text classification tasks. The implementation includes custom attention mechanisms optimized for detecting subtle patterns in cyberbullying language.Training Process: The model was trained on a diverse dataset containing thousands of labeled examples. Training involved multiple epochs with careful hyperparameter tuning to achieve optimal performance while avoiding overfitting.Fine-tuning: The base model underwent extensive fine-tuning on domain-specific data, including various forms of online harassment, hate speech, and toxic language patterns.
Key Features
•Real-time text classification
•High accuracy in detecting subtle cyberbullying patterns
•Scalable architecture for production deployment
•Custom training pipeline and data preprocessing
Impact & Results
This project showcases advanced machine learning capabilities, including the ability to build and train custom models from the ground up. The model demonstrates practical application of NLP techniques to solve real-world problems in online safety.The implementation required deep understanding of transformer architectures, training methodologies, and optimization techniques, highlighting expertise in both theoretical ML concepts and practical implementation.