Neural Search in Apache Solr has been contributed to the Open Source community by Sease [1] with the work of Alessandro Benedetti (Apache Lucene/Solr PMC member
How a learning to rank query works in Solr? How we can obtain the required features extraction time from the Solr qTime parameter?
Learning about text ranking using Deep Learning with BERT transformer. From training to neural re-ranking, with code snippets and examples.
How does Artificial Intelligence impact Search? This post explores the state of the art of AI applied to Information Retrieval in Open Source.
Query-level features and under-sampled queries, how to handle them? Find it out, with our new Learning to Rank implementations
This blog post explores the Apache Solr multi-field search limitations with a focus on the sow(split on whitespace) parameter.
This blog post aims to illustrate how to generate the query Id and how to manage the creation of the Training Set
This blog post is about several analysis on a LTR model and its explanation using the open source library SHAP
In this blog post, the elasticsearch _source field is compared with stored fields and docvalues from a performance point of view
The Rated Ranking Evaluator (RRE) is an offline search quality evaluation library for both Apache Solr and Elasticsearch.
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.
