| # BioHiCL-base: Hierarchical Multi-Label Contrastive Biomedical Retriever |
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| ## Model Card |
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| ## 🔍 Overview |
| BioHiCL-base is a biomedical dense retriever trained with hierarchical MeSH supervision to capture fine-grained semantic relationships between biomedical texts. |
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| 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. |
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| # ⚠️ 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**: ~0.1B parameters, suitable for deployment on a single GPU |
| - **Domain-adapted retriever**: Fine-tuned from a strong general-purpose bi-encoder |
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| --- |
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| ## 🧠 Model Details |
| - **Model type**: Bi-encoder (dense retriever) |
| - **Backbone**: BAAI/bge-base-en-v1.5 |
| - **Parameters**: ~0.1B |
| - **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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| --- |
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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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| --- |
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| ## ⚙️ How It Works |
| BioHiCL aligns: |
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| - 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. |
| --- |
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| ## 🚀 Usage (BEIR Evaluation) |
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| ```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") |
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| # > ⚠️ 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-base" |
| model = SentenceBERT(model_name) |
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| retriever = DenseRetrievalExactSearch(model, batch_size=16) |
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| top_k = 10 # top 10 documents per query |
| results = retriever.search(corpus, queries, top_k=top_k, score_function="cos_sim") |
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| k_values = [1, 3, 5, 10] |
| ndcg, _map, recall, precision = EvaluateRetrieval.evaluate(qrels, results, k_values=k_values) |
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| ``` |
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| ## 📖 Citation |
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| If you use this model, please cite: |
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| ```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} |
| } |
| |
| ``` |
