Update the Sentence Transformers section for the v6.0.1 prefix fix

README.md

@@ -1011,7 +1011,7 @@ ColBERT(
 This model can be used with [Sentence Transformers](https://www.sbert.net/) as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
 
 ```bash
-pip install "sentence-transformers>=6.0.0"
+pip install "sentence-transformers>=6.0.1"
 ```
 
 ```python
@@ -1030,12 +1030,12 @@ documents = [
 query_embeddings = model.encode_query(query)
 document_embeddings = model.encode_document(documents)
 print(query_embeddings.shape, document_embeddings[0].shape)
-# (15, 128) (18, 128)
+# torch.Size([16, 128]) torch.Size([19, 128])
 
 # MaxSim late-interaction scoring (higher is more relevant)
 scores = model.similarity(query_embeddings, document_embeddings)
 print(scores)
-# tensor([[11.8153, 12.9256, 12.2893, 12.6730]])
+# tensor([[11.4863, 12.8909, 12.2002, 12.5669]], device='cuda:0')
 ```
 
 ### PyLate

config_sentence_transformers.json

@@ -5,6 +5,12 @@
     "transformers": "4.48.3",
     "pytorch": "2.6.0"
   },
+  "requirements": {
+    "sentence-transformers": {
+      "specifier": ">=6.0.1",
+      "reason": "earlier versions encode this model's queries as \"search_query: ...\" instead of \"[Q] search_query: ...\", dropping the [Q] / [D] markers it was trained with and changing the embeddings."
+    }
+  },
   "prompts": {
     "query": "search_query: ",
     "document": "search_document: "