Transformer models have revolutionized the field of natural language processing. This tutorial will guide you through the basics of Transformer models, their architecture, and how they work.
Overview
- What is a Transformer Model? A deep learning model based on self-attention mechanisms.
- Applications Used in tasks like machine translation, text summarization, and question-answering.
- Key Components Encoder, Decoder, and Attention Mechanism.
Key Components
Encoder
The encoder processes the input sequence and generates context-aware representations. It consists of multiple layers of self-attention and feed-forward networks.
Decoder
The decoder generates the output sequence by attending to the encoder's representations and the previously generated tokens.
Attention Mechanism
The attention mechanism allows the model to focus on different parts of the input sequence when generating each output token.
How It Works
- Input Sequence The input sequence is passed through the encoder to generate context-aware representations.
- Attention The decoder attends to the encoder's representations and the previously generated tokens.
- Output Generation The decoder generates the output sequence using the attention information.
Resources
For further reading, check out our Introduction to Natural Language Processing.
Transformer Architecture