In this blog we introduce hybrid retrieval and explore two concrete implementations in Elasticsearch. We explore improving Elastic Learned Sparse EncodeR’s performance by combining it with BM25 using Reciprocal Rank Fusion and Weighted Sum of Scores.

Deep learning has transformed how people retrieve information. We’ve created a retrieval model that works with a variety of text with streamlined processes to deploy it. Learn about the model’s performance, its architecture, and how it was trained.

In this blog post, we'll examine benchmark solutions to compare retrieval methods. We use a collection of data sets to benchmark BM25 against two dense models and illustrate the potential gain using fine-tuning strategies with one of those models.

In this first blog post, we will list and explain the differences between the primary building blocks available in the Elastic Stack to do information retrieval.

In this blog, we will briefly talk about how we fine-tuned a transformer model meant for a masked language modeling (MLM) task, to make it suitable for a classification task.

Aggregate billions of documents in milliseconds instead of minutes with Elastic. Learn more about how the new random_sampler aggregation gives you statistically robust results at a lower cost.

In this blog, we walk users through identifying beaconing malware in their environment using our beaconing identification framework.

In this blog, we walk users through identifying beaconing malware in their environment using our beaconing identification framework.