Upload README.md

README.md

@@ -29,7 +29,7 @@ language:
 
 # Introduction
 
-`mdbr-leaf-ir` is a compact high-performance text embedding model specifically designed for **information retrieval (IR)** tasks, e.g., the retrieval part of RAGs. 
+`mdbr-leaf-ir` is a compact high-performance text embedding model specifically designed for **information retrieval (IR)** tasks, e.g., the retrieval component of Retrieval-Augmented Generation (RAG) pipelines. 
 
 To enable even greater efficiency, `mdbr-leaf-ir` supports [flexible asymmetric architectures](#asymmetric-retrieval-setup) and is robust to [vector quantization](#vector-quantization) and [MRL truncation](#mrl-truncation).
 
@@ -40,13 +40,32 @@ If you are looking to perform other tasks such as classification, clustering, se
 
 # Technical Report
 
-A technical report detailing our proposed `LEAF` training procedure will be available soon.
+A technical report detailing our proposed `LEAF` training procedure will be available soon (link will be added here).
 
 # Highlights  
 
-* **State-of-the-Art Performance**: `mdbr-leaf-ir` achieves new state-of-the-art results for compact embedding models, **ranking #1** on the public [BEIR benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models with ≤100M parameters.
+* **State-of-the-Art Performance**: `mdbr-leaf-ir` achieves state-of-the-art results for compact embedding models, **ranking #1** on the public [BEIR benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models with ≤100M parameters.
 * **Flexible Architecture Support**: `mdbr-leaf-ir` supports asymmetric retrieval architectures enabling even greater retrieval results. [See below](#asymmetric-retrieval-setup) for more information.
-* **MRL and Quantization Support**: embedding vectors generated by `mdbr-leaf-ir` compress well when truncated (MRL) and can be stored using more efficient types like `int8` and `binary`.  [See below](#mrl) for more information.
+* **MRL and Quantization Support**: embedding vectors generated by `mdbr-leaf-ir` compress well when truncated (MRL) and can be stored using more efficient types like `int8` and `binary`.  [See below](#mrl-truncation) for more information.
+
+## Benchmark Comparison  
+  
+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  | **?**      |  )
+
 
 # Quickstart  
   
@@ -93,7 +112,7 @@ for i, query in enumerate(queries):
   
 ## Transformers Usage  
 
-See [here](https://huggingface.co/MongoDB/mdbr-leaf-ir/blob/main/transformers_example.ipynb).
+See full example notebook [here](https://huggingface.co/MongoDB/mdbr-leaf-ir/blob/main/transformers_example.ipynb).
   
 ## Asymmetric Retrieval Setup