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README.md

@@ -44,7 +44,7 @@ A technical report detailing our proposed `LEAF` training procedure will be avai
 
 # Highlights  
 
-* **State-of-the-Art Performance**: `mdbr-leaf-mt` achieves new state-of-the-art results for compact embedding models, ranking <span style="color:red">#TBD</span> on the [public MTEB v2 (Eng) benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models <30M parameters with an average score of <span style="color:red">[TBD HERE]</span>.
+* **State-of-the-Art Performance**: `mdbr-leaf-mt` achieves new state-of-the-art results for compact embedding models, **ranking #1** on the [public MTEB v2 (Eng) benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models with ≤30M parameters.
 * **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.