: This involves running multiple self-attention operations in parallel, which helps the model capture diverse relationships within the data. 3. Feed-Forward Neural Networks (FFN)
: Calculates a "relevance score" between tokens, allowing the model to understand how much focus one word should have on another (e.g., relating "he" to "Tom"). transformers components
In the final stage of the decoder, the output vectors are transformed into human-readable results. In the final stage of the decoder, the
: Converts these raw scores into a probability distribution, allowing the model to select the most likely next token. : Vectors are added to the embeddings to
This is the "core" of the architecture, allowing the model to focus on different parts of the input sequence simultaneously.
: Vectors are added to the embeddings to provide information about the relative or absolute position of each token in the sequence. 2. The Multi-Head Attention Mechanism
: Normalizes the vector features to keep activations at a consistent scale, preventing vanishing or exploding gradients.