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For the second edition of our Vector Space Talks we were joined by none other than Cohere’s Head of Machine Learning Nils Reimers.
Key Takeaways Let’s dive right into the five key takeaways from Nils’ talk:
Content Quality Estimation: Nils explained how embeddings have traditionally focused on measuring topic match, but content quality is just as important. He demonstrated how their model can differentiate between informative and non-informative documents.
Compression-Aware Training: He shared how they’ve tackled the challenge of reducing the memory footprint of embeddings, making it more cost-effective to run vector databases on platforms like Qdrant.