Relevance Feedback in Informational Retrieval
Relerance feedback: from ancient history to LLMs. Why relevance feedback techniques are good on paper but not popular in neural search, and what we can do about it.
Evgeniya Sukhodolskaya
March 27, 2025
Machine Learning
Explore Machine Learning principles and practices which make modern semantic similarity search possible. Apply Qdrant and vector search capabilities to your ML projects.
Modern Sparse Neural Retrieval: From Theory to Practice
A comprehensive guide to modern sparse neural retrievers: COIL, TILDEv2, SPLADE, and more. Find out how they work and learn how to use them effectively.
Evgeniya Sukhodolskaya
October 23, 2024
Qdrant Summer of Code 2024 - ONNX Cross Encoders in Python
A summary of my work and experience at Qdrant Summer of Code 2024.
Huong (Celine) Hoang
October 14, 2024
Any* Embedding Model Can Become a Late Interaction Model... If You Give It a Chance!
We recently discovered that embedding models can become late interaction models & can perform surprisingly well in some scenarios. See what we learned here.
Kacper Łukawski
August 14, 2024
BM42: New Baseline for Hybrid Search
Introducing BM42 - a new sparse embedding approach, which combines the benefits of exact keyword search with the intelligence of transformers.
Andrey Vasnetsov
July 01, 2024
Layer Recycling and Fine-tuning Efficiency
Learn when and how to use layer recycling to achieve different performance targets.
Yusuf Sarıgöz
August 23, 2022
Fine Tuning Similar Cars Search
Learn how to train a similarity model that can retrieve similar car images in novel categories.
Yusuf Sarıgöz
June 28, 2022
Metric Learning for Anomaly Detection
Practical use of metric learning for anomaly detection. A way to match the results of a classification-based approach with only ~0.6% of the labeled data.
Yusuf Sarıgöz
May 04, 2022
Triplet Loss - Advanced Intro
What are the advantages of Triplet Loss over Contrastive loss and how to efficiently implement it?
Yusuf Sarıgöz
March 24, 2022
Metric Learning Tips & Tricks
Practical recommendations on how to train a matching model and serve it in production. Even with no labeled data.
Andrei Vasnetsov
May 15, 2021