Finding enough time to study all the modern solutions while keeping your production running is rarely feasible. Dense retrievers, hybrid retrievers, late interaction… How do they work, and where do they fit best? If only we could compare retrievers as easily as products on Amazon! We explored the most popular modern sparse neural retrieval models and broke them down for you. By the end of this article, you’ll have a clear understanding of the current landscape in sparse neural retrieval a...| Qdrant Articles on Qdrant - Vector Database
Introducing BM42 - a new sparse embedding approach, which combines the benefits of exact keyword search with the intelligence of transformers.| qdrant.tech
We’re on a journey to advance and democratize artificial intelligence through open source and open science.| huggingface.co
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Follow @msmarco| microsoft.github.io
What are the advantages of Triplet Loss over Contrastive loss and how to efficiently implement it?| qdrant.tech