From 9e762e90c0db0557ebc9ae508c3a7fe384401c5f Mon Sep 17 00:00:00 2001
From: Liam Thompson <32779855+leemthompo@users.noreply.github.com>
Date: Mon, 29 Jul 2024 11:16:29 +0200
Subject: [PATCH] Clarify eland-uploaded models is for hugging face

---
 .../retrievers-reranking/semantic-reranking.asciidoc            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc b/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc
index 2d5135b885c93..f25741fca0b8f 100644
--- a/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc
+++ b/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc
@@ -94,7 +94,7 @@ Currently you can:
 
 ** Integrate directly with the <<infer-service-cohere,Cohere Rerank inference endpoint>> using the `rerank` task type
 ** Integrate directly with the <<infer-service-google-vertex-ai,Google Vertex AI inference endpoint>> using the `rerank` task type
-** Upload a model to {es} with {eland-docs}/machine-learning.html#ml-nlp-pytorch[Eland]
+** Upload a model to {es} from Hugging Face with {eland-docs}/machine-learning.html#ml-nlp-pytorch[Eland]
 *** Then set up an <<inference-example-eland,{es} service inference endpoint>> with the `rerank` task type
 . *Create a `rerank` task using the <<put-inference-api,{es} Inference API>>*.
 The Inference API creates an inference endpoint and configures your chosen machine learning model to perform the reranking task.