Add Sentence Transformers usage

README.md

@@ -9,6 +9,7 @@ tags:
 - sentence-similarity
 - feature-extraction
 - ColBERT
+- multi-vector
 - PyLate
 widget: []
 license: mit
@@ -21,11 +22,45 @@ All the credits belong to the original authors and we thank Omar Khattab for all
 
 Please refer to the [original repository](https://huggingface.co/colbert-ir/colbertv2.0) and [paper](https://arxiv.org/abs/2112.01488) for more information about the model and to [PyLate repository](https://github.com/lightonai/pylate) for information about usage of the model.
 
+## 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/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]])
+```
+
 ## Model Details
 The model maps query and documents to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
 
 ### Model Description
-- **Model Type:** PyLate model
+- **Model Type:** Multi-vector embedding model
 - **Base model:** [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf -->
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 128 tokens