Prepare a dataset for training and validation of a Large Language Model (LLM)

Introduction

Generating a dataset for training a Language Model (LLM) involves several crucial steps to ensure its efficacy in capturing the nuances of language. From selecting diverse text sources to preprocessing to splitting the dataset, each stage requires attention to detail. Additionally, it’s crucial to balance the dataset’s size and complexity to optimize the model’s learning process. By curating a well-structured dataset, one lays a strong foundation for training an LLM capable of understanding and generating natural language with proficiency and accuracy.

This brief guide will walk you through generating a classification dataset to train and validate a Language Model (LLM). While the dataset created here is small but it lays a solid foundation for exploration and further development.