diff --git a/site/en/docs/search_reranking_using_embeddings.ipynb b/site/en/docs/search_reranking_using_embeddings.ipynb
index 9369d058e..c86c02ccc 100644
--- a/site/en/docs/search_reranking_using_embeddings.ipynb
+++ b/site/en/docs/search_reranking_using_embeddings.ipynb
@@ -1113,7 +1113,7 @@
         "id": "ltbB0vDsKQtI"
       },
       "source": [
-        "You will now implement **cosine similarity** as your metric. Here returned embedding vectors will be of unit length and hence their L1 norm (`np.linalg.norm()`) will be ~1. Hence, calculating **cosine similarity** is esentially same as calculating their **dot product score**."
+        "You will now implement **cosine similarity** as your metric. Here returned embedding vectors will be of unit length and hence their L1 norm (`np.linalg.norm()`) will be ~1. Hence, calculating **cosine similarity** is essentially same as calculating their **dot product score**."
       ]
     },
     {