Transformers in NLP: BERT and Sentence Transformers

In the ever-evolving landscape of Natural Language Processing (NLP), transformers have emerged as a revolutionary force. These models have addressed a fundamental challenge in NLP — understanding the intricacies of human language. In this Medium post, we embark on a journey to explore the pivotal roles of BERT and Sentence Transformers in achieving this transformation.

The Transformers Revolution: Unraveling Context in Language Understanding

Traditionally, NLP models struggled to grasp the contextual nuances within language. This challenge stemmed from the limitations of earlier models, such as Recurrent Neural Networks (RNNs), which processed language sequentially. Enter transformers, introduced in 2017, which brought about a paradigm shift by excelling in capturing context effectively.

Advantages Over RNN-Based Models

Transformers bring several distinct advantages over their predecessors:

1. Parallelism for Speed

Transformers process text in parallel, leading to significant speed improvements, crucial for handling large-scale language processing tasks efficiently.

2. Capturing Long-range Dependencies

Unlike RNNs, which struggle with capturing long-range dependencies in language, transformers employ attention mechanisms to excel in…