The Ultimate Guide to Training BERT from Scratch: Introduction

A few weeks ago, I trained and deployed my very own question-answering system using Retrieval Augmented Generation (RAG). The goal was to introduce such a system over my study notes and create an agent to help me connect the dots. LangChain truly shines in these specific types of applications: <iframe frameborder="0" height="480" scrolling="no" src="https://cdn.embedly.com/widgets/media.html?src=https%3A%2F%2Fwww.youtube.com%2Fembed%2F3yPBVii7Ct0&display_name=YouTube&url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3D3yPBVii7Ct0&image=http%3A%2F%2Fi.ytimg.com%2Fvi%2F3yPBVii7Ct0%2Fhqdefault.jpg&key=a19fcc184b9711e1b4764040d3dc5c07&type=text%2Fhtml&schema=youtube" title="" width="854"></iframe> As the system's quality blew me away, I couldn’t help but dig deeper to understand the wizardry under the hood. One of the features of the RAG pipeline is its ability to sift through mountains of information and find the context most relevant to a user’s query. It sounds complex but starts with a simple yet powerful process: encoding sentences into information-dense vectors. The most popular way to create these sentence embeddings for free is none other than SBERT, a <a href="https://www.sbert.net/" rel="noopener ugc nofollow" target="_blank">sentence transformer</a> built upon the legendary BERT encoder. And finally, that brings us to the main object of this series: understanding the fascinating world of BERT. What is it? What can you do with it? And the million-dollar question: How can you train your very own BERT model from scratch? We’ll kick things off by demystifying what BERT actually is, delve into its objectives and wide-ranging applications, and then move on to the nitty-gritty — like preparing datasets, mastering tokenization, understanding key metrics, and, finally, the ins and outs of training and evaluating your model. <a href="https://towardsdatascience.com/the-ultimate-guide-to-training-bert-from-scratch-introduction-b048682c795f">Click Here</a>