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README.md

@@ -53,19 +53,22 @@ A technical report detailing our proposed `LEAF` training procedure will be avai
   
 The table below shows the average BEIR benchmark scores (nDCG@10) for `mdbr-leaf-ir` compared to other retrieval models.
 
-| Model                              | Size | BEIR  Avg. (nDCG@10) |  
-|------------------------------------|------|----------------------|
-| **mdbr-leaf-ir**                   | 23M  | **53.55**            |  
-| snowflake-arctic-embed-s           | 32M  | 51.98                |  
-| bge-small-en-v1.5                  | 33M  | 51.65                |  
-| granite-embedding-small-english-r2 | 47M  | 50.87                |  
-| snowflake-arctic-embed-xs          | 23M  | 50.15                |  
-| e5-small-v2                        | 33M  | 49.04                |  
-| SPLADE++                           | 110M | 48.88                |  
-| MiniLM-L6-v2                       | 23M  | 41.95                |  
-| BM25                               | –    | 41.14                |  
-
-[//]: # (| **mdbr-leaf-ir (asym.)**           | 23M  | **?**      |  )
+`mdbr-leaf-ir` ranks #1 on the BEIR public leaderboard, and when run in asymmetric "**(asym.)**" mode as described [here](#asymmetric-retrieval-setup), the results improve even further.
+
+| Model                              | Size    | BEIR  Avg. (nDCG@10) |  
+|------------------------------------|---------|----------------------|
+| OpenAI text-embedding-3-large      | Unknown | 55.43                |  
+| **mdbr-leaf-ir (asym.)**           | 23M     | **54.03**            |  
+| **mdbr-leaf-ir**                   | 23M     | **53.55**            |  
+| snowflake-arctic-embed-s           | 32M     | 51.98                |  
+| bge-small-en-v1.5                  | 33M     | 51.65                |  
+| OpenAI text-embedding-3-small      | Unknown | 51.08                |  
+| granite-embedding-small-english-r2 | 47M     | 50.87                |  
+| snowflake-arctic-embed-xs          | 23M     | 50.15                |  
+| e5-small-v2                        | 33M     | 49.04                |  
+| SPLADE++                           | 110M    | 48.88                |  
+| MiniLM-L6-v2                       | 23M     | 41.95                |  
+| BM25                               | –       | 41.14                |  
 
 
 # Quickstart  
@@ -115,7 +118,7 @@ for i, query in enumerate(queries):
 
 See full example notebook [here](https://huggingface.co/MongoDB/mdbr-leaf-ir/blob/main/transformers_example.ipynb).
   
-## Asymmetric Retrieval Setup  
+## Asymmetric Retrieval Setup
   
 `mdbr-leaf-ir` is *aligned* to [`snowflake-arctic-embed-m-v1.5`](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5), the model it has been distilled from. This enables flexible architectures in which, for example, documents are encoded using the larger model, while queries can be encoded faster and more efficiently with the compact `leaf` model:
 ```python