Update README.md

README.md

@@ -8660,7 +8660,7 @@ GemmaEmbed is a dense-vector embedding model, trained especially for retrieval.
 
 # Results summary
 
-Results compared to BGE-EN-ICL on each Task
+Results comparing with BGE-EN-ICL and NV-Embed-v2 on each task in [MTEB](https://huggingface.co/spaces/mteb/leaderboard):
 
 Model | Total (56) |Classification (12) | Classification Pair (3) | STS (10) |Clustering (11) | Reranking (4) | Retrieval (15) | Summary (1)
 -- | -- | -- | -- | -- | -- | -- | -- | --
@@ -8681,4 +8681,4 @@ We use the [BGE-EN-ICL training data](https://huggingface.co/datasets/cfli/bge-f
 * Seungyeon Kim
 * Andrew McCallum
 * Rob Fergus
-* Manzil Zaheer
+* Manzil Zaheer