@@ -45,10 +45,18 @@ more allocations or more threads per allocation, which requires bigger ML nodes.
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Autoscaling provides bigger nodes when required. If autoscaling is turned off,
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you must provide suitably sized nodes yourself.
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+ [discrete]
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+ [[elser-benchamrks]]
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+ == Benchmarks
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+
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+ The following sections provide information about how ELSER performs on different
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+ hardwares and compares the model performance to {es} BM25 and other strong
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+ baselines such as Splade or OpenAI.
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+
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[discrete]
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[[elser-hw-benchamrks]]
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- == Hardware benchmarks
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+ === Hardware benchmarks
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Two data sets were utilized to evaluate the performance of ELSER in different
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hardware configurations: `msmarco-long-light` and `arguana`.
@@ -83,6 +91,35 @@ configurations.
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|==================================================================================================================================================================================
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+ [discrete]
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+ [[elser-qualitative-benchmarks]]
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+ === Qualitative benchmarks
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+
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+ The metric that is used to evaluate ELSER's ranking ability is the Normalized
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+ Discounted Cumulative Gain (NDCG) which can handle multiple relevant documents
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+ and fine-grained document ratings. The metric is applied to a fixed-sized list
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+ of retrieved documents which, in this case, is the top 10 documents (NDCG@10).
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+
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+ The table below shows the performance of ELSER compared to {es} BM25 with an
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+ English analyzer broken down by the 12 data sets used for the evaluation. ELSER
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+ has 10 wins, 1 draw, 1 loss and an average improvement in NDCG@10 of 17%.
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+
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+ image::images/ml-nlp-elser-ndcg10-beir.png[alt="ELSER benchmarks",align="center"]
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+ _NDCG@10 for BEIR data sets for BM25 and ELSER - higher values are better)_
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+
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+ The following table compares the average performance of ELSER to some other
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+ strong baselines. The OpenAI results are separated out because they use a
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+ different subset of the BEIR suite.
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+
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+ image::images/ml-nlp-elser-average-ndcg.png[alt="ELSER average performance compared to other baselines",align="center"]
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+ _Average NDCG@10 for BEIR data sets vs. various high quality baselines (higher_
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+ _is better). OpenAI chose a different subset, ELSER results on this set_
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+ _reported separately._
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+
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+ To read more about the evaluation details, refer to
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+ https://www.elastic.co/blog/may-2023-launch-information-retrieval-elasticsearch-ai-model[this blog post].
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+
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[discrete]
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[[download-deploy-elser]]
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== Download and deploy ELSER
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