Remove stale modules.json and add Sentence Transformers usage

README.md

@@ -1,5 +1,11 @@
 ---
 license: apache-2.0
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- multi-vector
+- ColBERT
 ---
 
 <br><br>
@@ -24,7 +30,41 @@ You can learn more about the models in our [blog post](https://www.mixedbread.ai
 
 ## Quickstart
 
-We recommend using the [RAGatouille](https://github.com/bclavie/RAGatouille) for using our ColBERT model.
+### Using Sentence Transformers
+
+This checkpoint also loads 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("mixedbread-ai/mxbai-colbert-large-v1")
+
+query = "Who wrote 'To Kill a Mockingbird'?"
+documents = [
+    "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
+    "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.",
+    "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.",
+    "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.",
+]
+
+query_embeddings = model.encode_query([query])
+document_embeddings = model.encode_document(documents)
+print(query_embeddings[0].shape, document_embeddings[0].shape)
+# (128, 128) (34, 128)
+
+# MaxSim late-interaction scoring
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[119.3276,  95.9337, 114.1252,  76.5609]])
+```
+
+### Using RAGatouille
+
+This model can also be used with [RAGatouille](https://github.com/bclavie/RAGatouille):
 
 ```sh
 pip install ragatouille
@@ -77,7 +117,6 @@ The result looks like this:
 ]
 ```
 
-
 ## Evaluation
 
 ### 1. Reranking Performance

modules.json

@@ -1,8 +0,0 @@
-[
-  {
-    "idx": 0,
-    "name": "0",
-    "path": "",
-    "type": "sentence_transformers.models.Transformer"
-  }
-]