Add Sentence Transformers usage

README.md

@@ -1,6 +1,7 @@
 ---
 tags:
 - ColBERT
+- multi-vector
 - PyLate
 - sentence-transformers
 - sentence-similarity
@@ -977,7 +978,7 @@ For production deployment of ColBERT-Zero and other multi-vector models, check o
 ## Model Details
 
 ### Model Description
-- **Model Type:** PyLate model
+- **Model Type:** Multi-vector embedding model
 <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
 - **Document Length:** 519 tokens
 - **Query Length:** 39 tokens
@@ -1004,6 +1005,41 @@ ColBERT(
 ```
 
 ## Usage
+
+### Sentence Transformers
+
+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"
+```
+
+```python
+from sentence_transformers import MultiVectorEncoder
+
+model = MultiVectorEncoder("lightonai/ColBERT-Zero")
+
+query = "Which planet is known as the Red Planet?"
+documents = [
+    "Venus is often called Earth's twin because of its similar size and proximity.",
+    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
+    "Jupiter, the largest planet in our solar system, has a prominent red spot.",
+    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
+]
+
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings[0].shape)
+# (15, 128) (18, 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]])
+```
+
+### PyLate
+
 First install the PyLate library:
 
 ```bash