Positional encoding is a technique used in transformer models to inject information about the order of input tokens, which is crucial since transformers lack inherent sequence awareness. By adding or concatenating positional encodings to input embeddings, models can effectively capture sequence information without relying on recurrent or convolutional structures.

Positional Encoding

Positional encoding

Transformer models are a type of deep learning architecture that revolutionized natural language processing by enabling the parallelization of data processing, which significantly improves training efficiency and performance. They utilize mechanisms like self-attention and positional encoding to capture contextual relationships in data, making them highly effective for tasks such as translation, summarization, and text generation.

transformer models

input tokens

order of input tokens

sequence awareness

positional encodings

input embeddings

capture sequence information

Recurrent structures

convolutional structures

Self-Attention Mechanism

Attention Mechanism

Understanding the Transformer Model in 'Attention Is All You Need'

Understanding Self-Attention Mechanisms in the Transformer Model

CUSTOMIZE YOUR LEARNING

CUSTOMIZE YOUR LEARNING

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