Add Sentence Transformers usage

README.md

@@ -1,5 +1,6 @@
 ---
 tags:
+- multi-vector
 - ColBERT
 - PyLate
 - sentence-transformers
@@ -16,7 +17,7 @@ license: apache-2.0
 
 # BiomedBERT ColBERT
 
-This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
 
 ## Usage (txtai)
 
@@ -45,6 +46,38 @@ ranker = Reranker(embeddings, similarity)
 ranker("query to run")
 ```
 
+## 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("NeuML/biomedbert-base-colbert")
+
+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)
+# (14, 128) (17, 128)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[9.9712, 12.4123, 11.0444, 11.9934]])
+```
+
 ## Usage (PyLate)
 
 Alternatively, the model can be loaded with [PyLate](https://github.com/lightonai/pylate).