
OpenSearch Neural Search Tutorial: Hybrid Search
The new implementation of hybrid search (from OpenSearch 2.10), allows for the combination and normalization of query relevance scores.

The new implementation of hybrid search (from OpenSearch 2.10), allows for the combination and normalization of query relevance scores.

The second part of OpenSearch Neural Search Plugin Tutorial for version 2.4.0 where additional tools can be found that might be useful

Discover in this blog post the Solr Neural Highlighter Plugin, which uses deep learning to highlight essential text for query answering.

In this blog post, you will learn how to import the Pandas library in AWS Lambda in order to execute python scripts.

What is the most appropriate approach to handle queries when splitting data when evaluating learning to rank models?

How data splitting can be done and why it is important for the offline evaluation of Learning to Rank models?

Does removing constant features affect model performance? Find out with our real-world Learning to Rank application

Query-level features and under-sampled queries, how to handle them? Find it out, with our new Learning to Rank implementations

Explainability and Interpretability of Learning To Rank models are vital in Information Retrieval, in this blog we present Tree SHAP.
We are Sease, an Information Retrieval Company based in London, focused on providing R&D project guidance and implementation, Search consulting services, Training, and Search solutions using open source software like Apache Lucene/Solr, Elasticsearch, OpenSearch and Vespa.
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