Add library_name to metadata

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

This pull request adds the `library_name: transformers` tag to the model card's metadata. This update allows the Hugging Face Hub to correctly identify the compatible library and enables features like the "Use in Transformers" button. The existing README already provides excellent documentation and code snippets confirming this compatibility.

README.md

@@ -1,5 +1,6 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
 datasets:
 - hotpotqa/hotpot_qa
 - dgslibisey/MuSiQue
@@ -8,18 +9,19 @@ datasets:
 language:
 - en
 - zh
+license: apache-2.0
 metrics:
 - accuracy
 - exact_match
 - f1
 - recall
-base_model:
-- Qwen/Qwen3-4B-Instruct-2507
 pipeline_tag: text-ranking
+library_name: transformers
 tags:
 - Rerank
 - Memory
 ---
+
 # QRRanker: Query-focused and Memory-aware Reranker for Long Context Processing
 
 <p align="center">
@@ -28,8 +30,7 @@ tags:
   <a href="https://huggingface.co/MindscapeRAG/QRRanker"><b>🤗 Models</b></a>
 </p>
 
-QRRanker is a lightweight reranking framework that leverages **Query-focused Retrieval (QR) heads** to produce continuous relevance scores, enabling effective listwise reranking with small-scale models.
-
+QRRanker is a lightweight reranking framework that leverages **Query-focused Retrieval (QR) heads** to produce continuous relevance scores, enabling effective listwise reranking with small-scale models. It was introduced in the paper [Query-focused and Memory-aware Reranker for Long Context Processing](https://huggingface.co/papers/2602.12192).
 
 ## Model Description
 
@@ -290,7 +291,7 @@ def compute_qr_scores(
     
     # Select specific QR heads
     if qr_head_list is not None:
-        head_set = [tuple(map(int, h.split('-'))) for h in qr_head_list.split(',')]
+        head_set = [tuple(map(int, h.split('-'))) for h in qr_head_list.split(',') ]
         indices = torch.tensor(head_set).to(all_head_scores.device)
         layers, heads = indices[:, 0], indices[:, 1]
         all_head_scores = all_head_scores[:, layers, heads, :]
@@ -323,8 +324,11 @@ def rerank_documents(model, tokenizer, question, paragraphs, qr_head_list, devic
         scores: Corresponding relevance scores
     """
     # Build input sequence
-    prompt_prefix = '<|im_start|>user\n'
-    retrieval_instruction = "Here are some retrieved chunks:\n\n"
+    prompt_prefix = '<|im_start|>user
+'
+    retrieval_instruction = "Here are some retrieved chunks:
+
+"
     
     chunk_part = prompt_prefix + retrieval_instruction
     chunk_ranges = []
@@ -336,9 +340,13 @@ def rerank_documents(model, tokenizer, question, paragraphs, qr_head_list, devic
         chunk_part += ' ' + text.strip()
         end = len(chunk_part)
         chunk_ranges.append([start, end])
-        chunk_part += '\n\n'
+        chunk_part += '
+
+'
     
-    query_part = f"Use the retrieved chunks to answer the user's query.\n\nQuery: {question}"
+    query_part = f"Use the retrieved chunks to answer the user's query.
+
+Query: {question}"
     full_seq = chunk_part + query_part
     
     # Tokenize with offset mapping
@@ -449,6 +457,7 @@ python qr_ranker_inference.py \
 | `--use_summary` | flag | False | Use summary field in data |
 
 
+## Citation
 
 If you use our QRRanker, please kindly cite: