Add Sentence Transformers usage

README.md

@@ -6,6 +6,7 @@ pipeline_tag: sentence-similarity
 tags:
 - SMVE
 - ColBERT
+- multi-vector
 - PyLate
 - sentence-transformers
 - sentence-similarity
@@ -97,6 +98,41 @@ up to 3x faster inference in `bf16` with almost no loss in accuracy and enables
 [Sparse Multi-Vector Encoding (SMVE)](https://www.topk.io/blog/20260311-smve-multi-vector-retrieval) inside [TopK](https://topk.io).
 
 ## 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("topk-io/Iso-ModernColBERT")
+
+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)
+# (12, 128) (18, 128)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[ 9.4844, 10.4180,  9.8516, 10.1953]])
+```
+
+### PyLate
+
 Install PyLate for embeddings and TopK SDK for retrieval.
 ```
 pip install -U pylate topk-sdk