Transformers Components Online

: 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

The is a deep learning architecture that relies on parallelized attention mechanisms rather than sequential recurrence. Its primary components are organized into an Encoder and a Decoder , which work together to transform input sequences into contextualized representations and subsequently into output sequences. 1. Input Processing: Embedding & Positional Encoding

: Converts these raw scores into a probability distribution, allowing the model to select the most likely next token. transformers components

: These convert discrete tokens (words or characters) into fixed-size vectors that capture initial semantic meaning.

: Normalizes the vector features to keep activations at a consistent scale, preventing vanishing or exploding gradients. : Vectors are added to the embeddings to

: These add the original input of a layer to its output before normalization, providing a "direct path" for gradients to flow backward during training. 5. Linear and Softmax Layers

: Projects the decoder's output into a much larger vector (the size of the model's vocabulary). Its primary components are organized into an Encoder

In the final stage of the decoder, the output vectors are transformed into human-readable results.