Embeddings Deep Dive
After tokenization, each token ID is mapped to a high‑dimensional vector called an embedding. Embeddings capture the semantic meaning of a token – similar words have similar vectors.
An embedding is a dense vector representation of a token where distance and direction reflect semantic relationships.
Why Embeddings?
Token IDs (e.g., 532, 124) have no inherent meaning. Embeddings convert these IDs into rich vectors (e.g., 768 numbers) that capture meaning. The model learns these vectors during training.
Semantic Relationships
Embeddings are arranged so that similar words are close in vector space. Famous example: vector(king) – vector(man) + vector(woman) ≈ vector(queen). This shows the model understands analogies.
king - man + woman ≈ queenDimensionality
Embedding dimensions range from 128 (small models) to 4096 (large models). Higher dimensions capture more nuance but require more memory and compute.
Visualizing Embeddings
We can use PCA or t‑SNE to project high‑dimensional embeddings into 2D. Words like "apple", "banana", "orange" cluster together; "car", "truck", "bicycle" cluster separately.
Two Minute Drill
- Embeddings turn token IDs into dense vectors with semantic meaning.
- Similar words have similar vectors (close in space).
- Embeddings enable analogies (king – man + woman ≈ queen).
- Higher dimensions capture more nuance.
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