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Learn how to create and annotate your own dataset using Large Language Models for fine-tuning and evaluation purposes in this 13-minute tutorial. Discover the process of building a sentiment analysis dataset for financial news using Gemini 2.5 Flash Lite, starting with exploring the Yahoo Finance dataset and understanding its structure. Set up your development environment and examine stock news data to identify key patterns and characteristics. Transform raw financial news data into a format suitable for machine learning applications, then leverage LLM capabilities to automatically generate sentiment labels and annotations. Master practical techniques for dataset creation that can be applied to various natural language processing tasks beyond sentiment analysis, including data preprocessing, prompt engineering for consistent annotations, and quality control measures for LLM-generated labels.
