# Embedding table

> Embedding table: The learned matrix with one row per vocabulary entry. Embedding a token ID means reading its row.

Canonical: https://llmdojo.dev/glossary/embedding-table

The learned matrix with one row per vocabulary entry. Embedding a token ID means reading its row.

Learn it in the lesson: [From token IDs to vectors](https://llmdojo.dev/learn/model-foundations/embeddings/the-embedding-table) (Model Foundations · Embeddings: From Token IDs to Vectors).

## Interview questions about Embedding table

- [What is an embedding table, and what does it mean for two embeddings to be similar?](https://llmdojo.dev/interview/q/what-is-an-embedding-table-and-what-does-it-mean-for-two)
- [An input embedding table is too large for one device. Compare sharding it by vocabulary with sharding it by hidden dimension.](https://llmdojo.dev/interview/q/an-input-embedding-table-is-too-large-for-one-device-compare-sharding)

## Related
- [Embedding](https://llmdojo.dev/glossary/embedding)
- [Cosine similarity](https://llmdojo.dev/glossary/cosine-similarity)
- [Dot product](https://llmdojo.dev/glossary/dot-product)
- [Contextual embedding](https://llmdojo.dev/glossary/contextual-embedding)
- [Semantic search](https://llmdojo.dev/glossary/semantic-search)
- [Glossary of every term](https://llmdojo.dev/glossary)