Transformers have revolutionized the field of natural language processing (NLP). This tutorial will guide you through the basics of the Transformer architecture, including its components and how it works.
Key Components of Transformer
- Encoder-Decoder Architecture: Transformers use an encoder-decoder architecture, where the encoder processes the input sequence and the decoder generates the output sequence.
- Self-Attention Mechanism: The self-attention mechanism allows the model to weigh the importance of different words in the input sequence.
- Positional Encoding: Since the Transformer architecture does not have a recurrent structure, positional encoding is used to capture the order of words in the sequence.
How Transformers Work
- Input Embedding: The input sequence is converted into a dense vector representation.
- Encoder: The encoder processes the input sequence using the self-attention mechanism and feed-forward neural networks.
- Decoder: The decoder generates the output sequence using the self-attention mechanism and the output from the encoder.
- Output: The output sequence is converted back into the desired format, such as text or another sequence.
Example of Transformer Application
Let's say you want to translate a sentence from English to French. The Transformer model will first convert the English sentence into a vector representation, then process it using the encoder and decoder, and finally generate the French translation.
Further Reading
To dive deeper into the Transformer architecture, you can read our comprehensive guide on Transformer Basics.
Images
Here's an image of a Transformer block, which is the building block of the Transformer architecture.