# BioHiCL-Large: Hierarchical Multi-Label Contrastive Biomedical Retriever

## Model Card

## 🔍 Overview
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.

# ⚠️ Important: Please ensure that the `transformers` version matches exactly (4.57.3), as other versions may lead to compatibility issues or unexpected behavior.

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## 💡 Key Features
- **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**: ~0.3B parameters, suitable for deployment on a single GPU  
- **Domain-adapted retriever**: Fine-tuned from a strong general-purpose bi-encoder  

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## 🧠 Model Details
- **Model type**: Bi-encoder (dense retriever)  
- **Backbone**: BAAI/bge-large-en-v1.5  
- **Parameters**: ~0.3B  
- **Fine-tuning**: LoRA (merged into base model)  
- **Max input length**: 512 tokens  
- **Training data**: Biomedical abstracts annotated with MeSH labels (e.g., BioASQ-derived corpora)  

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## ⚙️ Intended Use
This model is intended for biomedical information retrieval tasks such as:
- Scientific literature search (e.g., PubMed-style retrieval)
- Biomedical document ranking
- Query–abstract semantic matching
- Benchmark evaluation on BEIR biomedical subsets

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## ⚙️ How It Works
BioHiCL aligns:

- Embedding similarity (SimE): cosine similarity between document embeddings  
- Label similarity (SimL): cosine similarity over weighted MeSH multi-label vectors  
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## ⚙️ Requirements
- python >= 3.8  
- transformers == 4.57.3
> ⚠️ Important: Please ensure that the `transformers` version matches exactly (4.57.3), as other versions may lead to compatibility issues or unexpected behavior.
---

## 🚀 Usage (BEIR Evaluation)

```python
from beir import util
from beir.datasets.data_loader import GenericDataLoader
from beir.retrieval.models import SentenceBERT
from beir.retrieval.search.dense import DenseRetrievalExactSearch
from beir.retrieval.evaluation import EvaluateRetrieval


# 1. Download  load the SciFact dataset
dataset = "scifact"
url = "https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/" + dataset + ".zip"

data_path = util.download_and_unzip(url, "datasets")
corpus, queries, qrels = GenericDataLoader(data_path).load(split="test")

# ⚠️ Important: Please ensure that the `transformers` version matches exactly (4.57.3), as other versions may lead to compatibility issues or unexpected behavior.
model_name = "LunaLan07/BioHiCL-large"
model = SentenceBERT(model_name)

retriever = DenseRetrievalExactSearch(model, batch_size=16)

top_k = 10  # top 10 documents per query
results = retriever.search(corpus, queries, top_k=top_k, score_function="cos_sim")

k_values = [1, 3, 5, 10]
ndcg, _map, recall, precision = EvaluateRetrieval.evaluate(qrels, results, k_values=k_values)


```

## 📖 Citation

If you use this model, please cite:

```bibtex
@article{lan2026biohicl,
  title={BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels},
  author={Lan, Mengfei and Zheng, Lecheng and Kilicoglu, Halil},
  booktitle={ACL 2026},
  year={2026}
}

```