Cohere's Wikipedia Embeddings: A Short Primer on Embedding Models and Semantic Search

Learn about Wikipedia embeddings from Cohere! This video explains how Cohere embedded millions of Wikipedia articles and released them for open use. Embeddings represent text as numbers, allowing us to determine how semantically similar two pieces of text are. Using Cohere's embeddings, you can build applications like neural search, query expansion, and more. Check out the code example in Colab to get started with Cohere's embeddings today!

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[wryltxw]

Great content. Hope to see more on what Cohere is doing.

[wryltxw]

Do different models given entirely different embeddings?
Do the embeddings also depend on the size of the training data?

[wryltxw]

Is there a way to see how they convert words into embeddings? Is it by predicting context from word or vice versa?

[wryltxw]

But isn’t that a lot of dot scores to calculate? If we are talking about all of Wikipedia.