When BERT burst onto the scene in 2018, it triggered a tsunami in the world of Natural Language Processing (NLP). Many consider this as the NLP’s own ImageNet moment, drawing parallels to the shift deep neural networks brought to computer vision and the broader field of machine learning back in 2012.
Five years down the line, the prophecy holds true. Transformer-based Large Language Models (LLMs) aren’t just the shiny new 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.
This is the reason I decided to write this series of blog posts, diving into the world of BERT and how can you train your own model from scratch. The point isn’t just to get the job done — after all, you can easily find pre-trained BERT models on the Hugging Face Hub. The real magic lies in understanding the inner workings of this groundbreaking model and applying that knowledge to the current environment.
The first post served as your entry ticket, introducing BERT’s core concepts, objectives, and potential applications. We even went through the fine-tuning process together, creating 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 through the process of training your own: