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Articles by Thomas Veasey

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Improving information retrieval in the Elastic Stack: Hybrid retrieval

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.

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Improving information retrieval in the Elastic Stack: Introducing Elastic Learned Sparse Encoder, our new retrieval model

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.

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Improving information retrieval in the Elastic Stack: Benchmarking passage retrieval

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.

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Improving information retrieval in the Elastic Stack: Steps to improve search relevance

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.

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Getting the Most Out of Transformers in Elastic

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.

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Aggregate data faster with new the random_sampler aggregation

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.

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Identifying beaconing malware using Elastic

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

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Identifying beaconing malware using Elastic

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

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Designing for Change in Elastic Machine Learning