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Cohere's Wikipedia Embeddings: A Short Primer on Embedding Models and Semantic Search
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- Опубликовано: 13 авг 2024
- 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!
🔗 Cohere's Wikipedia Blog: txt.cohere.com...
🔗 Colab in Video: colab.research...
🔗 Cohere's Embedding's Tutorial - In depth: txt.cohere.com...
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Great content. Hope to see more on what Cohere is doing.
Nice video.
Is there a way to see how they convert words into embeddings? Is it by predicting context from word or vice versa?
You can check out this blog post which goes into more detail about their model: txt.cohere.com/multilingual/
Though they're fairly loose on the details!
Do different models given entirely different embeddings?
Do the embeddings also depend on the size of the training data?
1. Most likely, there are possible scenarios where you wind up with similar embeddings - but they are unlikely at best.
2. Yes, they depend on the vocabulary and instances/documents/passages.
Why 768?
Likely tuned during training and found to be the best! They don't provide much specific detail on this point.
But isn’t that a lot of dot scores to calculate? If we are talking about all of Wikipedia.
It is, but it's vectorized with `torch.mm` and so it's no tooooo bad. Though we're only using a sample of the data - and I'd suggested doing some pre-filtered first if you wanted the best performance.