Next Sentence Prediction using BERT

In the world of Natural Language Processing (NLP), predicting relationships between sentences is critical for tasks like text completion, document analysis, and conversational AI. One technique to accomplish this is Next Sentence Prediction (NSP), an approach that uses a model to predict if two sentences follow each other naturally. This blog post dives into NSP using BERT, a popular transformer model, guiding you step-by-step through a project to implement NSP from scratch. By the end, you’ll know how to set up NSP, preprocess data, fine-tune the model, and create a simple interactive tool.

Introduction

Next Sentence Prediction (NSP) is a key component in Natural Language Processing (NLP), essential for deciphering the connections between text segments. NSP acts as a supporting task during NLP model training, aiding models in capturing the essence of language continuity, particularly in extensive texts. The advent of Google’s Bidirectional Encoder Representations from Transformers (BERT) in 2018 has made NSP fundamental to contemporary language model training.

The NSP feature of BERT significantly contributes to its proficiency in language comprehension and production. This blog post will explore the workings of NSP within BERT, its training process, and its advantages for subsequent NLP tasks.