{"id":16493,"date":"2024-10-24T20:51:08","date_gmt":"2024-10-25T03:51:08","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=16493"},"modified":"2024-10-29T05:52:44","modified_gmt":"2024-10-29T12:52:44","slug":"prepare-datasets-fine-tuning-ml-models","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/prepare-datasets-fine-tuning-ml-models\/","title":{"rendered":"Preparing Datasets for Fine-Tuning ML Models: A Comprehensive Guide"},"content":{"rendered":"

Fine-tuning machine learning models starts with having well-prepared datasets. This guide will walk you through how to create these datasets, from gathering data to making instruction files. By the end, you’ll be equipped with practical knowledge and tools to prepare high-quality datasets for your fine-tuning tasks. <\/span><\/p>\n

This post continues the details guides from preparing data for RAG<\/a>, and building end-to-end RAG applications<\/a> with Couchbase vector search.<\/span><\/p>\n

\"\"

High-Level Overview<\/p><\/div>\n

Data collection\/gathering<\/h2>\n

The first step is gathering data from various sources. This involves collecting raw information that will later be cleaned and organized into structured datasets.<\/span>
\n<\/span>
\n<\/span>For an in-depth, step-by-step guide on preparing data for retrieval augmented generation, please refer to our comprehensive blog post:\u00a0<\/span>“Step by Step Guide to Prepare Data for Retrieval Augmented Generation”<\/span>.<\/span><\/p>\n

Our approach to data collection<\/h3>\n

In our approach, we utilized multiple methods to gather all relevant data:<\/span><\/p>\n

    \n
  1. \n
      \n
    1. Web scraping using Scrapy:<\/b>\n