Transformer and Natural language processing; A recent development.

Abstract

The convergence of transformers and natural language processing (NLP) represents a watershed moment in the realm of artificial intelligence. Recent NLP advancements have been profoundly shaped by the emergence of transformers, a class of deep learning models renowned for their remarkable proficiency in comprehending, generating, and manipulating human language. This abstract offers a succinct exploration of the symbiotic relationship between transformers and NLP, emphasizing their central role in propelling recent process.

The introduction of transformers heralded a paradigm shift in the realm of NLP, primarily owing to their innovative self-attention mechanism, which empowers them to adeptly capture intricate contextual associations within textual data. Distinguished by their multi-head attention layers and feed-forward networks, the architecture of transformers has ushered in a new era in NLP. Models such as BERT,

GPT-3, and their offshoots have not only redefined but also set the gold standard for a wide array of NLP tasks.