A transformer is a deep learning architecture introduced in the paper "Attention Is All You Need" by Vaswani et al. It is primarily used for sequence-to-sequence tasks like machine translation and has since become a fundamental building block for various natural language processing (NLP) tasks. The main components of a transformer are:
Encoder: The encoder is responsible for processing the input sequence and creating a representation that captures the contextual information of each token in the sequence. It consists of multiple layers, each containing two sub-components:
a. Multi-head Self-Attention Mechanism: Self-attention allows each word/token in the sequence to attend to all other words/tokens in the same sequence, giving importance to different parts of the input when creating the representation.
b. Feedforward Neural Networks: After the self-attention mechanism, a simple feedforward neural network is used to further process the attended representations and introduce non-linearity.
Decoder: The decoder takes the encoder's output and generates the target sequence. Like the encoder, it is also composed of multiple layers with two primary sub-components:
a. Masked Multi-head Self-Attention Mechanism: During training, the decoder can only access previously generated tokens (to avoid data leakage). The masked self-attention allows the decoder to focus on the current and past tokens while generating the output sequence.
b. Cross-Attention Mechanism: This allows the decoder to attend to the relevant parts of the encoder's output. It helps the decoder understand the context from the input sequence while generating each token in the output sequence.
Positional Encoding: Since transformers lack any inherent notion of word order (unlike recurrent neural networks), positional encodings are added to the input embeddings. These positional encodings help the model understand the order of words in the sequence.
Residual Connections and Layer Normalization: To facilitate training of deep models, residual connections and layer normalization are used to help in the flow of gradients and stabilize the training process.
Masking: During training, transformers use masking techniques to prevent the model from attending to future tokens in the sequence, which ensures the model's autoregressive property in the decoder.
The combination of these components allows transformers to efficiently capture long-range dependencies and contextual information, making them highly effective for various NLP tasks.