Add Sentence Transformers usage

README.md

@@ -4,6 +4,8 @@ language:
 - en
 tags:
 - ColBERT
+- multi-vector
+- sentence-transformers
 - RAGatouille
 - passage-retrieval
 ---
@@ -18,24 +20,48 @@ For more information about this model or how it was trained, head over to the [a
 
 ## Usage
 
-### Installation
-
 This model was designed with the upcoming RAGatouille overhaul in mind. However, it's compatible with all recent ColBERT implementations!
 
-To use it, you can either use the Stanford ColBERT library, or RAGatouille. You can install both or either by simply running.
+### Sentence Transformers
 
-```sh
-pip install --upgrade ragatouille
-pip install --upgrade colbert-ai
+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"
 ```
 
-If you're interested in using this model as a re-ranker (it vastly outperforms cross-encoders its size!), you can do so via the [rerankers](https://github.com/AnswerDotAI/rerankers) library:
-```sh
-pip install --upgrade rerankers[transformers]
+```python
+from sentence_transformers import MultiVectorEncoder
+
+model = MultiVectorEncoder("answerdotai/answerai-colbert-small-v1")
+
+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, 96) (17, 96)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[30.5692, 31.4895, 31.3029, 31.3072]])
 ```
 
 ### Rerankers
 
+If you're interested in using this model as a re-ranker (it vastly outperforms cross-encoders its size!), you can do so via the [rerankers](https://github.com/AnswerDotAI/rerankers) library:
+
+```bash
+pip install --upgrade rerankers[transformers]
+```
+
 ```python
 from rerankers import Reranker
 
@@ -47,6 +73,10 @@ ranker.rank(query=query, docs=docs)
 
 ### RAGatouille
 
+```bash
+pip install --upgrade ragatouille
+```
+
 ```python
 from ragatouille import RAGPretrainedModel
 
@@ -62,6 +92,10 @@ results = RAG.search(query)
 
 ### Stanford ColBERT
 
+```bash
+pip install --upgrade colbert-ai
+```
+
 #### Indexing
 
 ```python