Add Sentence Transformers usage

README.md

@@ -1,6 +1,7 @@
 ---
 tags:
 - ColBERT
+- multi-vector
 - PyLate
 - sentence-transformers
 - sentence-similarity
@@ -20,7 +21,7 @@ language:
 ## Model Details
 
 ### Model Description
-- **Model Type:** PyLate model
+- **Model Type:** Multi-vector embedding model
 - **Document Length:** 1024 tokens
 - **Query Length:** 32 tokens
 - **Output Dimensionality:** 128 tokens
@@ -61,6 +62,39 @@ We omit MIRACLRetrieval and MrTidyRetrieval in evalution due to our device condi
 | [jina-colbert-v2](https://huggingface.co/jinaai/jina-colbert-v2)                     | 0.5B         | 0.7518        | 0.0888           | 0.6671      | 0.1577    |
 
 ## 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("yjoonjang/colbert-ko-v1")
+
+query = "붉은 행성으로 알려진 행성은 무엇인가요?"
+documents = [
+    "금성은 크기와 근접성이 비슷하여 종종 지구의 쌍둥이라고 불린다.",
+    "화성은 붉은 겉모습 때문에 종종 붉은 행성이라고 불린다.",
+    "태양계에서 가장 큰 행성인 목성에는 뚜렷한 붉은 반점이 있다.",
+    "고리로 유명한 토성은 때때로 붉은 행성으로 오인된다.",
+]
+
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings[0].shape)
+# (32, 128) (19, 128)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[10.9423, 22.7836, 19.7410, 22.4239]])
+```
+
 ### PyLate for reranking
 
 If you only want to use the colbert model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank: