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README.md

@@ -40,13 +40,13 @@ If you are looking to perform other tasks such as classification, clustering, se
 
 # Technical Report
 
-A technical report detailing our proposed `LEAF` training procedure is [available here (TBD)](http://FILL_HERE_ARXIV_LINK).
+A technical report detailing our proposed `LEAF` training procedure will be available soon.
 
 # Highlights  
 
-* **State-of-the-Art Performance**: `mdbr-leaf-ir` achieves new state-of-the-art results for compact embedding models, ranking <span style="color:red">#TBD</span> on the public [BEIR benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models <100M parameters with an average nDCG@10 score of <span style="color:red">[TBD HERE]</span>.
+* **State-of-the-Art Performance**: `mdbr-leaf-ir` achieves new state-of-the-art results for compact embedding models, ranking <span style="color:red">#TBD</span> on the public [BEIR benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models <100M parameters with an average nDCG@10 score of <span style="color:red">[TBD]</span>.
 * **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/or 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) for more information.
 
 # Quickstart