Add Sentence Transformers usage

README.md

@@ -4,6 +4,8 @@ language:
 - en
 tags:
 - ColBERT
+- multi-vector
+- sentence-transformers
 ---
 <p align="center">
   <img align="center" src="https://github.com/stanford-futuredata/ColBERT/blob/main/docs/images/colbertofficial.png?raw=true" width="430px" />
@@ -34,6 +36,38 @@ These rich interactions allow ColBERT to surpass the quality of _single-vector_
 * [**ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction**](https://arxiv.org/abs/2112.01488) (NAACL'22).
 * [**PLAID: An Efficient Engine for Late Interaction Retrieval**](https://arxiv.org/abs/2205.09707) (CIKM'22).
 
+## 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("colbert-ir/colbertv2.0")
+
+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)
+# (32, 128) (17, 128)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[12.7970, 27.1945, 23.8495, 24.5656]])
+```
+
 ----
 
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