Semanlink - Train embeddings by using the Two-Tower built-in algorithm

Train embeddings by using the Two-Tower built-in algorithm | Vertex AI

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> The Two-Tower model pairs similar types of objects, such as user profiles, search queries, web documents, answer passages, or images, in the same vector space, so that related items are close to each other. **The Two-Tower model consists of two encoder towers: the query tower and the candidate tower**. These towers embed independent items into a shared embedding space, which lets Matching Engine retrieve similarly matched items.
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> To train a Two-Tower model, Google uses **pairs of relevant items**. Each pair consists of a query document and a candidate document. Documents contain arbitrary customer-defined features including text, numeric, and categorical features. After training, the Two-Tower built-in algorithm exports two TensorFlow SavedModels—a query encoder and a candidate encoder... Given a query item, Matching Engine uses the query encoder to generate a query embedding, and uses the index to find similar candidate embeddings. Matching Engine uses the candidate encoder to index all the items and serve them by using an approximate nearest neighbor solution.

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sl:bookmarkOf : https://cloud.google.com/vertex-ai/docs/matching-engine/train-embeddings-two-tower
sl:creationDate : 2021-11-04
sl:creationTime : 2021-11-04T17:23:31Z

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Bookmark of: https://cloud.google.com/vertex-ai/docs/matching-engine/train-embeddings-two-tower