Update README.md

README.md

@@ -1,9 +1,9 @@
-# BioHiCL-base: Hierarchical Multi-Label Contrastive Biomedical Retriever
+# BioHiCL-large: Hierarchical Multi-Label Contrastive Biomedical Retriever
 
 ## Model Card
 
 ## 🔍 Overview
-BioHiCL-base is a biomedical dense retriever trained with hierarchical MeSH supervision to capture fine-grained semantic relationships between biomedical texts.
+BioHiCL-large is a biomedical dense retriever trained with hierarchical MeSH supervision to capture fine-grained semantic relationships between biomedical texts.
 
 Unlike traditional dense retrievers trained with binary relevance signals, BioHiCL models semantic similarity using structured multi-label supervision derived from the MeSH ontology, enabling it to capture partial semantic overlap between documents.
 
@@ -13,17 +13,17 @@ Unlike traditional dense retrievers trained with binary relevance signals, BioHi
 - **Hierarchical supervision**: Leverages MeSH ontology to encode structured biomedical semantics  
 - **Multi-label similarity learning**: Captures graded semantic overlap beyond binary relevance  
 - **Contrastive + regression training**: Aligns embedding similarity with label similarity  
-- **Efficient**: ~0.1B parameters, suitable for deployment on a single GPU  
+- **Efficient**: ~0.3B parameters, suitable for deployment on a single GPU  
 - **Domain-adapted retriever**: Fine-tuned from a strong general-purpose bi-encoder  
 
 ---
 
 ## 🧠 Model Details
 - **Model type**: Bi-encoder (dense retriever)  
-- **Backbone**: BAAI/bge-base-en-v1.5  
-- **Parameters**: ~0.1B  
+- **Backbone**: BAAI/bge-large-en-v1.5  
+- **Parameters**: ~0.3B  
 - **Fine-tuning**: LoRA (merged into base model)  
-- **Max input length**: 8192 tokens  
+- **Max input length**: 512 tokens  
 - **Training data**: Biomedical abstracts annotated with MeSH labels (e.g., BioASQ-derived corpora)  
 
 ---
@@ -63,7 +63,7 @@ data_path = util.download_and_unzip(url, "datasets")
 corpus, queries, qrels = GenericDataLoader(data_path).load(split="test")
 
 # Model
-model_name = "LunaLan07/BioHiCL-base"
+model_name = "LunaLan07/BioHiCL-large"
 model = SentenceTransformer(model_name)
 
 # Retrieval