Understand Positional Encoding In Transformers

Author(s): Ramakrushna Mohapatra

Originally published on Towards AI.

Understand Positional Encoding In Transformers

If you’ve ever worked with Transformer models like BERT, GPT, or T5, you’ve probably come across a small but crucial concept: Positional Encoding.

This article explains the vital role of positional encoding in Transformers, a mechanism that allows models like BERT and GPT to understand word order in a sentence, contrasting with RNNs that handle sequences inherently. It covers how positional encoding injects order information into parallel-processing models, emphasizing the mathematical basis of sinusoidal functions for effectively encoding position. The article demonstrates the unique structure of these encodings, visual representations, and highlights their relevance in maintaining understanding of sequences and enhancing model generalization to longer sequences.

Read the full blog for free on Medium.

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Published via Towards AI