Have you ever found yourself stuck with a massive dataset, wondering how to categorize each row efficiently? I recently came across a Reddit post from someone who faced a similar challenge. They had 5000 rows of user comments describing completed tasks and needed to assign categories to each row based on a separate table of categories and descriptions.
The problem is that manual categorization can be tedious and prone to errors. That’s where Large Language Models (LLMs) and AI come in. The Reddit user tried using ChatGPT and Deepseek but encountered some issues. ChatGPT would sometimes make up categories or forget what it was doing, while Deepseek hit token limits quickly and could only process small batches at a time.
So, what’s the best LLM or AI for assigning categories to large datasets? While there’s no one-size-fits-all solution, some popular options include Google’s AutoML, Amazon’s Comprehend, and IBM’s Watson Studio. These platforms offer pre-trained models and customizable workflows to help you categorize your data accurately and efficiently.
When choosing an LLM or AI, consider factors like data quality, category complexity, and scalability. It’s also essential to evaluate the performance of each model and fine-tune it for your specific use case.
By leveraging the power of AI and LLMs, you can automate data categorization and focus on higher-level tasks that drive business value.