The Final Frontier: Constructing and Training Your BERT Model

This blog post concludes our series on training BERT from scratch. For context and a whole understanding, please confer with Part I, Part II, and Part III of the series.

When BERT burst onto the scene in 2018, it triggered a tsunami on the planet of Natural Language Processing (NLP). Many consider this because the NLP’s own ImageNet moment, drawing parallels to the shift deep neural networks delivered to computer vision and the broader field of machine learning back in 2012.

Five years down the road, the prophecy holds true. Transformer-based Large Language Models (LLMs) aren’t just the shiny recent toy; they’re reshaping the landscape. From transforming how we work to revolutionizing how we access information, these models are core technology behind countless emerging startups aiming to harness their untapped potential.

That is the rationale I made a decision to write down this series of blog posts, diving into the world of BERT and how will you train your personal model from scratch. The purpose isn’t simply to get the job done — in spite of everything, you may easily find pre-trained BERT models on the Hugging Face Hub. The actual magic lies in understanding the inner workings of this groundbreaking model and applying that knowledge to the present environment.

The primary post served as your entry ticket, introducing BERT’s core concepts, objectives, and potential applications. We even went through the fine-tuning process together, making a question-answering system:

The second installment acted as your insider’s guide to the often-overlooked realm of tokenizers — unpacking their role, showing how they convert words into numerical values, and guiding you thru the means of training your personal: