Upload README.md

README.md

@@ -24,8 +24,9 @@ language:
 1. [Introduction](#introduction)
 2. [Technical Report](#technical-report)
 3. [Highlights](#highlights)
-4. [Quickstart](#quickstart)
-5. [Citation](#citation)
+4. [Benchmarks](#benchmark-comparison)
+5. [Quickstart](#quickstart)
+6. [Citation](#citation)
 
 # Introduction
 
@@ -48,6 +49,24 @@ A technical report detailing our proposed `LEAF` training procedure will be avai
 * **Flexible Architecture Support**: `mdbr-leaf-mt` 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-mt` compress well when truncated (MRL) and can be stored using more efficient types like `int8` and `binary`.  [See below](#mrl) for more information.
 
+## Benchmark Comparison
+  
+The table below shows the scores for `mdbr-leaf-mt` on the MTEB v2 (English) benchmark, compared to other retrieval models.
+
+`mdbr-leaf-mt` ranks #1 on this benchmark for models with <30M parameters.
+
+| Model                              | Size    | MTEB v2 (Eng) |  
+|------------------------------------|---------|---------------|  
+| OpenAI text-embedding-3-large      | Unknown | 66.43         |  
+| OpenAI text-embedding-3-small      | Unknown | 64.56         |  
+| **mdbr-leaf-mt**                   | 23M     | **63.97**     |  
+| gte-small                          | 33M     | 63.22         |  
+| snowflake-arctic-embed-s           | 32M     | 61.59         |  
+| e5-small-v2                        | 33M     | 61.32         |  
+| granite-embedding-small-english-r2 | 47M     | 61.07         |  
+| all-MiniLM-L6-v2                   | 22M     | 59.03         |  
+
+
 # Quickstart  
   
 ## Sentence Transformers