Intermediate · Neural Networks

Encoder-Decoder Transformer

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. A transformer model composed of both an encoder (processing input) and a decoder (generating output), ideal for sequence-to-sequence tasks.

Technical Definition

A transformer model composed of both an encoder (processing input) and a decoder (generating output), ideal for sequence-to-sequence tasks.

How it works

Encoder-decoder transformers, like original Transformer model, are well-suited for tasks like machine translation or text summarization. The encoder processes the entire input sequence to create a rich contextual representation. The decoder then uses this representation, combined with its own attention mechanism over the encoder's output, to generate the target sequence.

Related Concepts

• Attention Mechanism — A technique that lets models dynamically focus on the most relevant parts of the input when producing each output element.
• Machine Translation — The automatic translation of text or speech from one natural language to another using AI.
• Transformer Architecture — A neural network architecture, predominantly used in NLP, that relies heavily on self-attention mechanisms to process sequential data.