Upload README.md

README.md

@@ -31,7 +31,7 @@ language:
 
 # Introduction
 
-`mdbr-leaf-mt-asym` is a compact high-performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks. 
+`mdbr-leaf-mt-asym` is a high-performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks. 
 
 This model is the asymmetric variant of `mdbr-leaf-mt`, which uses [`MongoDB/mdbr-leaf-mt`](https://huggingface.co/MongoDB/mdbr-leaf-mt) for queries and [`mixedbread-ai/mxbai-embed-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) for documents.
 
@@ -118,7 +118,10 @@ See [here](https://huggingface.co/MongoDB/mdbr-leaf-mt/blob/main/transformers_ex
   
 ## Asymmetric Retrieval Setup
   
-`mdbr-leaf-mt` is *aligned* to [`mxbai-embed-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1), the model it has been distilled from. This enables flexible architectures in which, for example, documents are encoded using the larger model, while queries can be encoded faster and more efficiently with the compact `leaf` model. This generally outperforms the symmetric setup in which both queries and documents are encoded with `leaf`.
+`mdbr-leaf-mt` is *aligned* to [`mxbai-embed-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1), the model it has been distilled from. 
+This enables flexible architectures in which, for example, documents are encoded using the larger model, 
+while queries can be encoded faster and more efficiently with the compact `leaf` model. 
+This usually outperforms the symmetric setup in which both queries and documents are encoded with `leaf`.
 
 To use exclusively the leaf model, use [`mdbr-leaf-mt`](https://huggingface.co/MongoDB/mdbr-leaf-mt).
 
@@ -151,8 +154,8 @@ Good initial values are -1.0 and +1.0.
 from sentence_transformers.quantization import quantize_embeddings
 import torch
 
-query_embeds = model.encode(queries, prompt_name="query")
-doc_embeds = model.encode(documents)
+query_embeds = model.encode_query(queries)
+doc_embeds = model.encode_document(documents)
 
 # Quantize embeddings to int8 using -1.0 and +1.0
 ranges = torch.tensor([[-1.0], [+1.0]]).expand(2, query_embeds.shape[1]).cpu().numpy()
@@ -169,8 +172,8 @@ print(f"* Similarities:\n{similarities}")
 # After quantization:
 # * Embeddings type: int8
 # * Similarities:
-# [[2202032 1422868]
-#  [1421197 1845580]]
+# [[11392 9204]
+#  [8256 10470]]
 ```
 
 # Evaluation
@@ -200,3 +203,7 @@ This model is released under Apache 2.0 License.
 # Contact  
   
 For questions or issues, please open an issue or pull request. You can also contact the MongoDB ML research team at [email protected].  
+
+# Acknowledgments
+
+This model version was created by @tomaarsen - we thank him for his contribution to this project.