Transformer deep learning model architecture

The Transformer model is a deep learning architecture that has revolutionized the field of Natural Language Processing (NLP) and beyond. Here’s a brief summary:

Key Features:

1. Self-Attention Mechanism: Unlike traditional models that process data sequentially, Transformers use self-attention to process all elements of the input sequence simultaneously. This allows the model to weigh the importance of different words in a sentence, regardless of their position12.
2. Encoder-Decoder Structure: The Transformer architecture consists of an encoder and a decoder. The encoder processes the input sequence and generates a set of continuous representations, while the decoder uses these representations to produce the output sequence2.
3. Multi-Head Attention: This mechanism allows the model to focus on different parts of the input sequence simultaneously, improving its ability to capture complex relationships within the data2.
4. Feed-Forward Neural Networks: Each layer in the encoder and decoder contains fully connected feed-forward networks that further process the data2.
5. Positional Encoding: Since Transformers do not process data sequentially, they use positional encoding to retain information about the order of the input sequence1.