We're ready to transform words into word vectors. Embedding words into vectors happens via an embedding table. An embedding table is basically a lookup table. Each row holds the word vector of a word. We index the rows by word-IDs, hence the flow of obtaining a word's word vector is as follows:

  1. word->word-ID: Previously, we obtained a word-ID for each word with Keras' Tokenizer. Tokenizer holds all the vocabulary and maps each vocabulary word to an ID, which is an integer.

  2. word-ID->word vector: A word-ID is an integer and therefore can be used as an index to the embedding table's rows. Each word-ID corresponds to one row, and when we want to get a word's word vector, we first obtain its word-ID and then do a lookup in the embedding table rows with this word-ID.

The following diagram shows how embedding words into word vectors works:

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