概要
This skill provides a structured framework for training FastText text classification models, focusing on the critical balance between model performance and storage efficiency. It guides users through a systematic experimentation process, from initial baseline testing and parameter sensitivity analysis to advanced quantization and automated tuning. By implementing these best practices, developers can navigate competing constraints such as memory limits and precision requirements, ensuring that resulting models are both highly accurate and production-ready for resource-constrained environments.
