Upload README.md

README.md

@@ -1,7 +1,6 @@
 ---
 license: apache-2.0  
-base_model:  
-- microsoft/MiniLM-L6-v2  
+base_model: microsoft/MiniLM-L6-v2  
 tags:  
 - transformers  
 - sentence-transformers  
@@ -24,7 +23,7 @@ language:
 
 `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. 
 
-Enabling 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).
+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).
 
 If you are looking to perform other tasks such as classification, clustering, semantic sentence similarity, summarization, please check out our [`mdbr-leaf-mt`](https://huggingface.co/MongoDB/mdbr-leaf-mt) model.
 
@@ -37,9 +36,9 @@ A technical report detailing our proposed `LEAF` training procedure is [availabl
 
 # 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 for models <30M 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 HERE]</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 are 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/or can be stored using more efficient types like `int8` and `binary`.  [See below](#mrl) for more information.
 
 # Quickstart  
   
@@ -103,9 +102,9 @@ document_embeddings = doc_model.encode(documents)
 # Compute similarities  
 scores = query_model.similarity(query_embeddings, document_embeddings)  
 ```
-Retrieval results from asymmetric mode are usually superior to the [standard mode above](#sentence-transformers).
+Retrieval results in asymmetric mode are often superior to the [standard mode above](#sentence-transformers).
 
-## MRL
+## MRL Truncation
 
 Embeddings have been trained via [MRL](https://arxiv.org/abs/2205.13147) and can be truncated for more efficient storage:
 ```python