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Chapter 2.3 - Creating Token Embeddings

Overview

Token Embeddings are the magic trick that turns completely random ID numbers into rich, meaningful concepts that the AI can understand.

🎯 Why we do it​

Rationale

Right now, our words are just basic ID numbers. To the computer, ID #7586 and ID #21831 are totally unrelated, even if they represent "brown" and "fox". We need a way to give words meaning. Embeddings turn every single ID into a long list of decimal numbers (a vector). In this new format, words that mean similar things will have very similar lists of numbers!

🛠️ How we do it​

Methodology

We use a lookup table called an Embedding Layer. Think of it like a giant spreadsheet. Every row in the spreadsheet corresponds to a specific word ID. The columns in the spreadsheet contain the decimal numbers that represent the word's "meaning".

📥 Input​

Input Format
  • Description: A grid of simple integer IDs, shaped as (Batch Size, Sequence Length). We will use the first two words from our master sentence ("The quick").
  • Example:
input_ids = tensor([[464, 2068]]) # "The quick"

📤 Output​

Resulting State
  • Description: A 3D block of decimal numbers, shaped as (Batch Size, Sequence Length, Embedding Size). Every single ID has been swapped out for its personal row of decimal numbers.
  • Example:
# 464 ("The") turned into [0.12, -0.45, 0.88]
# 2068 (" quick") turned into [-0.01, 0.99, -0.22]
tensor([[[ 0.12, -0.45,  0.88],   
         [-0.01,  0.99, -0.22]]])

The Spreadsheet Lookup:

Token IDMeaning Val 1Meaning Val 2Meaning Val 3
464 (The)0.12-0.450.88
2068 (quick)-0.010.99-0.22

⚙️ Working​

Under the Hood
  1. The computer takes the first ID (e.g., 464).
  2. It goes to row 464 in the Embedding Spreadsheet.
  3. It copies the entire row of decimals and replaces the ID with them.
graph TD
    A[Simple ID Grid] -->|Go to Spreadsheet| B{Embedding Layer}
    B -->|Swap ID for row of decimals| C[3D Block of Meanings]