Finalize card: drop MultiVectorEncoder from intro and the multi-vector/late-interaction tags, add SentenceTransformer usage example

README.md

@@ -2,13 +2,11 @@
 pretty_name: Example Documents
 tags:
 - sentence-transformers
-- multi-vector
-- late-interaction
 ---
 
 # Example Documents
 
-A small set of example documents across modalities (image, audio, video) for use in [Sentence Transformers](https://www.sbert.net/) retrieval snippets and documentation. These are the kinds of files you pass to `model.encode_document(...)`, for example with a `MultiVectorEncoder`.
+A small set of example documents across modalities (image, audio, video) for use in [Sentence Transformers](https://www.sbert.net/) retrieval snippets and documentation. These are the kinds of files you pass to `model.encode_document(...)`.
 
 ## Contents
 
@@ -30,17 +28,36 @@ A small set of example documents across modalities (image, audio, video) for use
 
 ## Usage
 
-Reference any file by its resolve URL, for example:
+Reference any file by its resolve URL. These documents can be encoded with a multi-vector (late interaction) `MultiVectorEncoder`:
 
 ```python
 from sentence_transformers import MultiVectorEncoder
 
 model = MultiVectorEncoder("vidore/colqwen-omni-v0.1")
+queries = ["What is the Llama 4 model?"]
 documents = [
-    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/mapo_tofu.mp4",
+    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/llama4_hgf.png",
     "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/conversation3.mp3",
 ]
+query_embeddings = model.encode_query(queries)
 document_embeddings = model.encode_document(documents)
+print(model.similarity(query_embeddings, document_embeddings))
+```
+
+or with a single-vector `SentenceTransformer`:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("LCO-Embedding/LCO-Embedding-Omni-3B-2605", trust_remote_code=True)
+queries = ["What is the Llama 4 model?"]
+documents = [
+    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/llama4_hgf.png",
+    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/conversation3.mp3",
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(model.similarity(query_embeddings, document_embeddings))
 ```
 
 ## Credits