Update README.md

README.md

@@ -14,7 +14,7 @@ For more technical details, refer to our paper: ...
 - Maximum context length: 512
 - Embedding Dimension: 4096
 
-# How to use with Transformers?
+# How to use with 🤗 Transformers?
 
 ```python
 from typing import List
@@ -61,10 +61,15 @@ embeddings = pool(
 )
 print(embeddings)
 ```
+
 # How to use with Sentence Transformers?
 
 ```python
 from sentence_transformers import SentenceTransformer
+from typing import List
+
+def get_detailed_instruct(queries: List[str]) -> List[str]:
+    return [f"Instruct: Retrieve semantically similar text.\nQuery: {query}" for query in queries]
 
 model = SentenceTransformer("lamarr-llm-development/elbedding", trust_remote_code=True)