Clarify negative scores returned by Elastic Rerank

explore-analyze/machine-learning/nlp/ml-nlp-rerank.md

@@ -51,6 +51,10 @@ Refer to this [Python notebook](https://github.com/elastic/elasticsearch-labs/bl
 
 ::::
 
+::::{note}
+The relevance scores produced during reranking depend on the text similarity model used and can include negative values.
+::::
+
 ### Create an inference endpoint [ml-nlp-rerank-deploy-steps]
 
 1. In {{kib}}, navigate to the **Dev Console**.

solutions/search/ranking/semantic-reranking.md

@@ -102,9 +102,13 @@ To use semantic re-ranking in {{es}}, you need to:
 2. **Create a `rerank` endpoint using the [{{es}} Inference API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put)**. The Inference API creates an inference endpoint and configures your chosen machine learning model to perform the re-ranking task.
 3. **Define a `text_similarity_reranker` retriever in your search request**. The retriever syntax makes it simple to configure both the retrieval and re-ranking of search results in a single API call.
 
-::::{dropdown} Example search request with semantic reranker
+:::::{dropdown} Example search request with semantic reranker
 The following example shows a search request that uses a semantic reranker to reorder the top-k documents based on their semantic similarity to the query.
 
+::::{note}
+The relevance scores produced during reranking depend on the text similarity model used and can include negative values.
+::::
+
 ```console
 POST _search
 {
@@ -122,14 +126,13 @@ POST _search
       "field": "text",
       "inference_id": "elastic-rerank",
       "inference_text": "How often does the moon hide the sun?",
-      "rank_window_size": 100,
-      "min_score": 0.5
+      "rank_window_size": 100
     }
   }
 }
 ```
 
-::::
+:::::