Transformers Components · Limited & Validated

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

Since Transformers do not process data sequentially like RNNs, they must explicitly "learn" the order of words.

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

Following the attention layers, each position in the encoder and decoder is processed by a .

: Normalizes the vector features to keep activations at a consistent scale, preventing vanishing or exploding gradients. : Converts these raw scores into a probability

: 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").

: 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 Following the attention layers, each position in the

: 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)