In this article, we’ll discuss Bidirectional Encoder Representations from Transformers (BERT), a model designed to understand language. While BERT is similar to models like GPT, BERT aims to understand text rather than generate it. This is useful for a variety of tasks, such as rating the positivity of a product review or predicting whether the answer to a question is correct.
Before we dive into BERT, we’ll briefly discuss the Transformer architecture, which is the direct inspiration for BERT. With that understanding, we’ll dive deeper into BERT and discuss how it’s built and trained to solve problems by leveraging a general understanding of language. Finally, we’ll build a BERT model ourselves from scratch and use it to predict whether product reviews are positive or negative.
Who is this useful for? Anyone who wants to gain a complete understanding of the state of the art of ai.
How far along is this post? The early parts of this article are accessible to readers of all skill levels, while the later sections, dealing with implementation from scratch, are quite advanced. Supplementary resources are provided as needed.
Prerequisites: I highly recommend understanding the fundamental ideas about…
