Add pipeline tag and library name to model card

Hi! I'm Niels from the Hugging Face community team.

This PR improves your model card's metadata by adding:
- `pipeline_tag: feature-extraction`: This helps users find your model when filtering by task on the Hub.
- `library_name: transformers`: This enables an automated code snippet on the model page, showcasing how to use the model with the Transformers library.

The rest of the model card remains unchanged.

README.md

@@ -1,14 +1,16 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen3-Embedding-4B
 language:
 - en
 - zh
-base_model:
-- Qwen/Qwen3-Embedding-4B
+license: apache-2.0
 tags:
 - embedding
 - retriever
 - RAG
+pipeline_tag: feature-extraction
+library_name: transformers
 ---
 
 # Mindscape-Aware RAG (MiA-RAG)
@@ -99,13 +101,17 @@ Use this mode to retrieve narrative text chunks. A **Global Summary** is injecte
 def get_query_prompt(query, summary="", residual=False):
     """Construct input prompt with global summary (Eq. 5 in paper)."""
     task_desc = "Given a search query with the book's summary, retrieve relevant chunks or helpful entities summaries from the given context that answer the query"
-    summary_prefix = "\n\nHere is the summary providing possibly useful global information. Please encode the query based on the summary:\n"
+    summary_prefix = "
+
+Here is the summary providing possibly useful global information. Please encode the query based on the summary:
+"
 
     # Insert PAD token to capture residual embedding before the summary
     middle_token = tokenizer.pad_token if residual else ""
 
     return (
-        f"Instruct: {task_desc}\n"
+        f"Instruct: {task_desc}
+"
         f"Query: {query}{middle_token}{summary_prefix}{summary}{node_delimiter}"
     )