README: library_name -> sentence-transformers; human-readable tokens in decode snippet

README.md

@@ -11,7 +11,7 @@ tags:
 - inference-free
 - sentence-transformers
 pipeline_tag: feature-extraction
-library_name: transformers
+library_name: sentence-transformers
 ---
 
 <p align="center">
@@ -98,8 +98,8 @@ print(similarities)
 
 # Inspect the top activated tokens of the page image
 decoded = model.decode(document_embeddings[0], top_k=5)
-print([(token, round(weight, 3)) for token, weight in decoded])
-# [('Ġdog', 1.836), ('Ġdogs', 1.664), ('Ġpuppy', 1.586), ('ĠRecords', 1.578), ('ĠBennett', 1.508)]
+print([(token.replace("Ġ", " ").strip(), round(weight, 3)) for token, weight in decoded])
+# [('dog', 1.836), ('dogs', 1.664), ('puppy', 1.586), ('Records', 1.578), ('Bennett', 1.508)]
 ```
 
 Images can be passed as PIL images, local paths, URLs (as above), or together with text as `{"image": ..., "text": ...}`. Plain text documents are also supported: `model.encode_document(["some passage text"])`. The model runs in bfloat16 by default. You can pass `model_kwargs={"torch_dtype": "float32"}` for full precision.