In a text model, what kind of layer turns each word from a simple number into a special list of numbers that helps the model understand word meanings and relationships?

The layer in a text model that transforms each word from a simple numerical identifier into a special list of numbers that helps the model understand word meanings and relationships is called the Embedding Layer. Initially, each unique word in a model's vocabulary is assigned a unique integer identifier, which is a simple number representing that word without any inherent meaning or relationship to other words. The Embedding Layer takes these discrete integer IDs as input. It then converts each integer ID into a dense, real-valued vector, known as a word embedding or word vector. This word embedding is the "special list of numbers." Each dimension within this vector captures different semantic and syntactic aspects of the word, learned during the model's training process. For example, if the integer ID for "cat" is 123, the Embedding Layer would transform this into a vector like [0.1, -0.5, 0.8, ...]. The crucial property of these word embeddings is that words with similar meanings or that are used in similar contexts will have their corresponding vectors located closer to each other in the multi-dimensional embedding space. This spatial proximity allows the model to grasp relationships and nuances between words, moving beyond simple identifiers to a richer, continuous representation of language. Essentially, the Embedding Layer acts as a trainable lookup table where each word's integer ID maps to its unique, learned embedding vector.