v1.0.0

This release includes the complete dataset, scripts, and Docker-based environments for studying GC-content bias in bacterial promoter sequences using Random Forest, Convolutional Neural Networks (CNN), and BERT-based models. The project is designed for reproducible machine learning experiments, leveraging Docker, Weights & Biases (W&B), and a remote storage solution for experiment tracking.