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Enables the model to relate different positions of a single sequence to compute a representation of the sequence.

Tokens are converted into numeric vectors (embeddings) that represent the semantic meaning of the words. build a large language model %28from scratch%29 pdf

Remove noise, handle missing values, and redact sensitive information. Enables the model to relate different positions of

Multiple attention mechanisms operate in parallel, allowing the model to attend to information from different representation subspaces at different positions. 3. Implementing the Architecture handle missing values

Since Transformers process words in parallel, you must add positional information so the model understands the order of words in a sentence. 2. Coding Attention Mechanisms

Attention is the core innovation of the Transformer architecture. It allows the model to "focus" on relevant parts of a sequence when predicting the next word.