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<!-- NAV -->
<nav>
  <div class="inner">
    <div class="logo">🧬 <span>BioRetrieval</span></div>
    <div class="links">
      <a href="#evolution">Evolution</a>
      <a href="#models">Models</a>
      <a href="#benchmarks">Benchmarks</a>
      <a href="#datasets">Datasets</a>
      <a href="#leaderboard">Leaderboard</a>
      <a href="#start">Get Started</a>
    </div>
  </div>
</nav>

<!-- HERO -->
<div class="hero">
  <h1>Biomedical <em>Text Retrieval</em></h1>
  <p>A curated map of papers, models, datasets, and benchmarks for dense retrieval in the biomedical domain.</p>
  <div class="tag-row">
    <span class="tag">15 papers</span>
    <span class="tag">10+ models</span>
    <span class="tag">12 benchmarks</span>
    <span class="tag">7 training datasets</span>
  </div>
</div>

<!-- EVOLUTION TIMELINE -->
<section id="evolution">
  <div class="section-head">
    <h2><span class="icon">📜</span> Evolution of BioRetrieval</h2>
    <p>From domain pretraining to LLM-based retrievers — key milestones that shaped the field.</p>
  </div>
  <div class="timeline">

    <div class="timeline-item">
      <div class="year">2019</div>
      <h3><a href="https://arxiv.org/abs/1901.08746">BioBERT</a></h3>
      <div class="desc">First BERT fine-tuned on PubMed abstracts + PMC full texts. Proved domain pretraining consistently improves biomedical NER, RE, and QA. The baseline everything is measured against.</div>
      <div class="meta">
        <a href="https://hf.co/dmis-lab/biobert-v1.1">🤗 dmis-lab/biobert-v1.1</a>
        <a href="https://arxiv.org/abs/1901.08746">arXiv</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2019</div>
      <h3><a href="https://arxiv.org/abs/1903.10676">SciBERT</a></h3>
      <div class="desc">BERT pretrained on 1.14M scientific papers (18% biomed, 82% CS). Broader scientific vocabulary makes it competitive on retrieval and used as backbone for SLEDGE-Z (TREC-COVID SOTA).</div>
      <div class="meta">
        <a href="https://hf.co/allenai/scibert_scivocab_uncased">🤗 allenai/scibert</a>
        <a href="https://arxiv.org/abs/1903.10676">arXiv</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2020</div>
      <h3><a href="https://arxiv.org/abs/2007.15779">PubMedBERT</a></h3>
      <div class="desc">Showed pretraining from scratch on PubMed beats continual pretraining from general BERT. Introduced the BLURB benchmark. Became the de-facto backbone for biomedical retrieval fine-tuning.</div>
      <div class="meta">
        <a href="https://hf.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract">🤗 microsoft/PubMedBERT</a>
        <a href="https://arxiv.org/abs/2007.15779">arXiv</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2021</div>
      <h3><a href="https://arxiv.org/abs/2010.11784">SapBERT</a></h3>
      <div class="desc">Self-alignment pretraining using UMLS ontology + metric learning. SOTA on medical entity linking without task-specific supervision — the go-to for entity disambiguation and concept normalization.</div>
      <div class="meta">
        <a href="https://hf.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token">🤗 cambridgeltl/SapBERT</a>
        <a href="https://arxiv.org/abs/2010.11784">arXiv</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2021</div>
      <h3><a href="https://arxiv.org/abs/2104.08663">BEIR Benchmark</a></h3>
      <div class="desc">18 datasets, 9 tasks — revealed that dense models trained on MS MARCO generalize poorly to biomedical domains. BM25 often wins out-of-distribution. The standard evaluation framework.</div>
      <div class="meta">
        <a href="https://github.com/beir-cellar/beir">GitHub</a>
        <a href="https://arxiv.org/abs/2104.08663">arXiv · NeurIPS '21</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2022</div>
      <h3><a href="https://arxiv.org/abs/2203.15827">BioLinkBERT</a></h3>
      <div class="desc">Pretrains via hyperlinked documents in the same context window + Document Relation Prediction. Excels at multi-hop biomedical reasoning (BioASQ, USMLE QA).</div>
      <div class="meta">
        <a href="https://hf.co/michiyasunaga/BioLinkBERT-large">🤗 michiyasunaga/BioLinkBERT-large</a>
        <a href="https://arxiv.org/abs/2203.15827">arXiv · ACL '22</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2023</div>
      <h3><a href="https://arxiv.org/abs/2307.00589">MedCPT / BioCPT</a></h3>
      <div class="desc">Trained on 255M PubMed user click logs via contrastive learning — zero-shot SOTA on 5 biomedical IR tasks. Released as query/article encoder pair + cross-encoder reranker. Click logs as free supervision.</div>
      <div class="meta">
        <a href="https://hf.co/ncbi/MedCPT-Query-Encoder">🤗 Query Encoder (382K↓)</a>
        <a href="https://hf.co/ncbi/MedCPT-Article-Encoder">🤗 Article Encoder</a>
        <a href="https://hf.co/ncbi/MedCPT-Cross-Encoder">🤗 Cross-Encoder</a>
        <a href="https://arxiv.org/abs/2307.00589">arXiv</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2023</div>
      <h3><a href="https://arxiv.org/abs/2311.16075">BioLORD-2023</a></h3>
      <div class="desc">Grounds biomedical concepts in UMLS definitions via multi-phase contrastive learning + LLM self-distillation + weight averaging. SOTA on MedSTS, MedNLI-S, EHR-Rel-B. Multilingual variants available.</div>
      <div class="meta">
        <a href="https://hf.co/FremyCompany/BioLORD-2023">🤗 FremyCompany/BioLORD-2023 (147K↓)</a>
        <a href="https://arxiv.org/abs/2311.16075">arXiv · EMNLP '23</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2024</div>
      <h3><a href="https://arxiv.org/abs/2404.18443">BMRetriever</a></h3>
      <div class="desc">LLM-based retriever: unsupervised contrastive pretraining on PubMed/textbooks/StatPearls, then instruction fine-tuning on 11 datasets. 410M model outperforms baselines 11.7× larger. 2B matches 5B+ models.</div>
      <div class="meta">
        <a href="https://hf.co/BMRetriever/BMRetriever-410M">🤗 410M</a>
        <a href="https://hf.co/BMRetriever/BMRetriever-2B">🤗 2B</a>
        <a href="https://hf.co/BMRetriever/BMRetriever-7B">🤗 7B</a>
        <a href="https://arxiv.org/abs/2404.18443">arXiv · EMNLP '24</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2024</div>
      <h3><a href="https://arxiv.org/abs/2511.08029">BiCA</a></h3>
      <div class="desc">Citation-aware hard negatives: 2-hop citation graphs from PubMed articles for semantic hard-negative mining. Fine-tunes GTE-small/base with only 20K examples — consistent BEIR + LoTTE gains.</div>
      <div class="meta">
        <a href="https://github.com/NiravBhattLab/BiCA">GitHub</a>
        <a href="https://arxiv.org/abs/2511.08029">arXiv</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2025</div>
      <h3><a href="https://arxiv.org/abs/2507.19407">MedTE + MedTEB</a></h3>
      <div class="desc">51-task medical embedding benchmark (classification, clustering, retrieval). MedTE model (GTE-Base fine-tuned on 7 medical corpora) achieves mean 0.578 vs 0.539 next-best. The new comprehensive eval standard.</div>
      <div class="meta">
        <a href="https://hf.co/MohammadKhodadad/MedTE">🤗 MohammadKhodadad/MedTE</a>
        <a href="https://github.com/MohammadKhodadad/MedTEB">GitHub</a>
        <a href="https://arxiv.org/abs/2507.19407">arXiv</a>
      </div>
    </div>

    <div class="timeline-item">
      <div class="year">2025</div>
      <h3><a href="https://arxiv.org/abs/2604.15591">BioHiCL</a></h3>
      <div class="desc">Hierarchical MeSH supervision: depth-weighted contrastive loss + LoRA on BGE models. 0.1B model achieves IR Avg 0.543, beating BMRetriever-1B. Best on NFCorpus and SCIDOCS. Current efficiency SOTA.</div>
      <div class="meta">
        <a href="https://hf.co/LunaLan07/BioHiCL-Base">🤗 BioHiCL-Base</a>
        <a href="https://hf.co/LunaLan07/BioHiCL-Large">🤗 BioHiCL-Large</a>
        <a href="https://arxiv.org/abs/2604.15591">arXiv</a>
      </div>
    </div>

  </div>
</section>

<!-- MODELS -->
<section id="models">
  <div class="section-head">
    <h2><span class="icon">🧠</span> Models</h2>
    <p>Production-ready retrieval models available on the Hugging Face Hub.</p>
  </div>
  <div class="grid">

    <a class="card" href="https://hf.co/ncbi/MedCPT-Query-Encoder" target="_blank">
      <div class="card-top">
        <h3>MedCPT (Query + Article)</h3>
        <span class="badge badge-model">Model</span>
      </div>
      <div class="desc">Asymmetric bi-encoder from NCBI, trained on 255M PubMed click logs. Separate query/article encoders + cross-encoder reranker. Zero-shot SOTA on biomedical IR.</div>
      <div class="card-footer">
        <span class="pill">382K ↓</span>
        <span class="pill">BERT-base</span>
        <span class="pill">ncbi</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/FremyCompany/BioLORD-2023" target="_blank">
      <div class="card-top">
        <h3>BioLORD-2023</h3>
        <span class="badge badge-model">Model</span>
      </div>
      <div class="desc">UMLS-grounded sentence embeddings via multi-phase contrastive + LLM distillation + weight averaging. SOTA on clinical STS, entity linking, and concept similarity. Multilingual variants available.</div>
      <div class="card-footer">
        <span class="pill">147K ↓</span>
        <span class="pill">MPNet</span>
        <span class="pill">sentence-transformers</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/BMRetriever/BMRetriever-410M" target="_blank">
      <div class="card-top">
        <h3>BMRetriever (410M–7B)</h3>
        <span class="badge badge-model">Model</span>
      </div>
      <div class="desc">LLM-based retriever family. Instruction-formatted queries + last-token pooling. 410M outperforms 5B+ baselines. Models at 410M (GPT-NeoX), 2B (Gemma), 7B (Mistral).</div>
      <div class="card-footer">
        <span class="pill">410M–7B</span>
        <span class="pill">MIT</span>
        <span class="pill">EMNLP '24</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/LunaLan07/BioHiCL-Base" target="_blank">
      <div class="card-top">
        <h3>BioHiCL</h3>
        <span class="badge badge-model">Model</span>
      </div>
      <div class="desc">MeSH hierarchy-supervised BGE model. Depth-weighted contrastive loss + LoRA. 0.1B params achieves IR Avg 0.543, beating BMRetriever-1B. Best efficiency/performance ratio.</div>
      <div class="card-footer">
        <span class="pill">110M</span>
        <span class="pill">BERT</span>
        <span class="pill">2025</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token" target="_blank">
      <div class="card-top">
        <h3>SapBERT</h3>
        <span class="badge badge-model">Model</span>
      </div>
      <div class="desc">Self-alignment on UMLS synonyms via metric learning. Go-to model for medical entity linking and concept normalization. No task-specific labels needed.</div>
      <div class="card-footer">
        <span class="pill">457K ↓</span>
        <span class="pill">PubMedBERT</span>
        <span class="pill">NAACL '21</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/MohammadKhodadad/MedTE" target="_blank">
      <div class="card-top">
        <h3>MedTE</h3>
        <span class="badge badge-model">Model</span>
      </div>
      <div class="desc">GTE-Base fine-tuned on 7 diverse medical corpora (PubMed, MIMIC-IV, ClinicalTrials, bioRxiv/medRxiv). Mean 0.578 on MedTEB — best medical general-purpose embedding model.</div>
      <div class="card-footer">
        <span class="pill">~110M</span>
        <span class="pill">GTE-Base</span>
        <span class="pill">2025</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/michiyasunaga/BioLinkBERT-large" target="_blank">
      <div class="card-top">
        <h3>BioLinkBERT</h3>
        <span class="badge badge-model">Model</span>
      </div>
      <div class="desc">Document-link pretraining on PubMed hyperlinks. Excels at multi-hop biomedical reasoning — top performer on BioASQ QA and USMLE-style questions.</div>
      <div class="card-footer">
        <span class="pill">7.3K ↓</span>
        <span class="pill">BERT-large</span>
        <span class="pill">ACL '22</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract" target="_blank">
      <div class="card-top">
        <h3>PubMedBERT</h3>
        <span class="badge badge-model">Model</span>
      </div>
      <div class="desc">The backbone model for biomedical fine-tuning. Pretrained from scratch on PubMed — not continued from general BERT. Foundation for MedCPT, SapBERT, and many others.</div>
      <div class="card-footer">
        <span class="pill">110M</span>
        <span class="pill">microsoft</span>
        <span class="pill">BLURB</span>
      </div>
    </a>

  </div>
</section>

<!-- BENCHMARKS -->
<section id="benchmarks">
  <div class="section-head">
    <h2><span class="icon">📊</span> Benchmarks</h2>
    <p>Standard evaluation suites for biomedical retrieval — use these to measure your models.</p>
  </div>
  <div class="table-wrap">
    <table>
      <thead>
        <tr>
          <th>Benchmark</th>
          <th>Task</th>
          <th>Domain</th>
          <th>Scale</th>
          <th>Metric</th>
          <th>Link</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td>NFCorpus</td>
          <td>Ad-hoc search</td>
          <td>Nutrition / Medicine</td>
          <td>323 queries · 3.6K docs</td>
          <td>nDCG@10</td>
          <td><a href="https://hf.co/datasets/BeIR/nfcorpus">🤗 BeIR/nfcorpus</a></td>
        </tr>
        <tr>
          <td>TREC-COVID</td>
          <td>Ad-hoc retrieval</td>
          <td>COVID-19 / CORD-19</td>
          <td>50 queries · 171K docs</td>
          <td>nDCG@10</td>
          <td><a href="https://hf.co/datasets/BeIR/trec-covid">🤗 BeIR/trec-covid</a></td>
        </tr>
        <tr>
          <td>SciFact</td>
          <td>Claim verification</td>
          <td>Scientific claims</td>
          <td>~300 queries · 5K abstracts</td>
          <td>nDCG@10</td>
          <td><a href="https://hf.co/datasets/BeIR/scifact">🤗 BeIR/scifact</a></td>
        </tr>
        <tr>
          <td>BioASQ</td>
          <td>QA retrieval</td>
          <td>Biomedical QA</td>
          <td>Varies annually</td>
          <td>MAP, nDCG</td>
          <td><a href="http://participants-area.bioasq.org/">bioasq.org</a></td>
        </tr>
        <tr>
          <td>SCIDOCS</td>
          <td>Document similarity</td>
          <td>Scientific papers</td>
          <td>1K queries · 25K docs</td>
          <td>nDCG@10</td>
          <td><a href="https://hf.co/datasets/BeIR/scidocs">🤗 BeIR/scidocs</a></td>
        </tr>
        <tr>
          <td>BIOSSES</td>
          <td>Sentence similarity</td>
          <td>Biomedical</td>
          <td>100 sentence pairs</td>
          <td>Pearson r</td>
          <td><a href="https://hf.co/datasets/tabilab/biosses">🤗 tabilab/biosses</a></td>
        </tr>
        <tr>
          <td>PubMedQA</td>
          <td>QA retrieval</td>
          <td>PubMed abstracts</td>
          <td>1K labeled</td>
          <td>Accuracy</td>
          <td><a href="https://hf.co/datasets/qiaojin/PubMedQA">🤗 PubMedQA</a></td>
        </tr>
        <tr>
          <td>MedTEB</td>
          <td>51 medical tasks</td>
          <td>Pan-medical</td>
          <td>Comprehensive</td>
          <td>Multi-metric</td>
          <td><a href="https://github.com/MohammadKhodadad/MedTEB">GitHub</a></td>
        </tr>
        <tr>
          <td>R2MED</td>
          <td>Reasoning retrieval</td>
          <td>Clinical decision</td>
          <td>Multi-type</td>
          <td>nDCG@10</td>
          <td><a href="https://arxiv.org/abs/2505.14558">arXiv</a></td>
        </tr>
      </tbody>
    </table>
  </div>
</section>

<!-- DATASETS -->
<section id="datasets">
  <div class="section-head">
    <h2><span class="icon">🗂️</span> Training Datasets</h2>
    <p>Key corpora and labeled data for training biomedical retrieval models.</p>
  </div>
  <div class="grid">

    <a class="card" href="https://hf.co/datasets/MedRAG/pubmed" target="_blank">
      <div class="card-top">
        <h3>MedRAG/pubmed</h3>
        <span class="badge badge-dataset">Dataset</span>
      </div>
      <div class="desc">PubMed abstracts corpus — the core pretraining data for biomedical models. Used by BMRetriever, MedTE, and most domain-adapted models.</div>
      <div class="card-footer">
        <span class="pill">pretraining</span>
        <span class="pill">abstracts</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/datasets/MedRAG/textbooks" target="_blank">
      <div class="card-top">
        <h3>MedRAG/textbooks</h3>
        <span class="badge badge-dataset">Dataset</span>
      </div>
      <div class="desc">Medical textbook passages — high-quality, structured biomedical knowledge. Core fine-tuning data for BMRetriever and RAG applications.</div>
      <div class="card-footer">
        <span class="pill">fine-tuning</span>
        <span class="pill">textbooks</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/datasets/MedRAG/statpearls" target="_blank">
      <div class="card-top">
        <h3>MedRAG/statpearls</h3>
        <span class="badge badge-dataset">Dataset</span>
      </div>
      <div class="desc">StatPearls clinical reference articles — continuously updated clinical content used for retriever fine-tuning and medical Q&A.</div>
      <div class="card-footer">
        <span class="pill">clinical</span>
        <span class="pill">fine-tuning</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/datasets/BMRetriever/biomed_retrieval_dataset" target="_blank">
      <div class="card-top">
        <h3>BMRetriever Training Mix</h3>
        <span class="badge badge-training">Training</span>
      </div>
      <div class="desc">11-task instruction mixture for biomedical retrieval fine-tuning — query-document pairs spanning medical QA, entity linking, and scientific claim verification.</div>
      <div class="card-footer">
        <span class="pill">instruction</span>
        <span class="pill">11 tasks</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/datasets/FremyCompany/BioLORD-Dataset" target="_blank">
      <div class="card-top">
        <h3>BioLORD Dataset</h3>
        <span class="badge badge-dataset">Dataset</span>
      </div>
      <div class="desc">UMLS concept definition pairs for contrastive learning. Powers BioLORD-2023's clinical concept embeddings and medical entity similarity.</div>
      <div class="card-footer">
        <span class="pill">UMLS</span>
        <span class="pill">contrastive</span>
      </div>
    </a>

    <a class="card" href="https://hf.co/datasets/allenai/cord19" target="_blank">
      <div class="card-top">
        <h3>CORD-19</h3>
        <span class="badge badge-dataset">Dataset</span>
      </div>
      <div class="desc">COVID-19 Open Research Dataset — 400K+ research papers. The corpus behind TREC-COVID, used for pandemic-era retrieval research and benchmarking.</div>
      <div class="card-footer">
        <span class="pill">400K+ papers</span>
        <span class="pill">COVID-19</span>
      </div>
    </a>

  </div>
</section>

<!-- LEADERBOARD -->
<section id="leaderboard">
  <div class="section-head">
    <h2><span class="icon">🏆</span> Training Recipes Leaderboard</h2>
    <p>Ranked by result quality — the best published approaches for training a biomedical retriever.</p>
  </div>
  <div class="table-wrap">
    <table>
      <thead>
        <tr>
          <th>#</th>
          <th>Model</th>
          <th>Params</th>
          <th>Training Recipe</th>
          <th>Best Result</th>
          <th>Paper</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td class="lb-rank">1</td>
          <td>BioHiCL-Base</td>
          <td>0.1B</td>
          <td>BGE + MeSH hierarchy contrastive (depth-weighted) + LoRA</td>
          <td class="lb-score">IR Avg 0.543, NFCorpus 0.379</td>
          <td><a href="https://arxiv.org/abs/2604.15591">2604.15591</a></td>
        </tr>
        <tr>
          <td class="lb-rank">2</td>
          <td>BMRetriever-2B</td>
          <td>2B</td>
          <td>LLM + unsupervised contrastive on PubMed/textbooks + instruction FT</td>
          <td class="lb-score">Matches 5B+ across 11 tasks</td>
          <td><a href="https://arxiv.org/abs/2404.18443">2404.18443</a></td>
        </tr>
        <tr>
          <td class="lb-rank">3</td>
          <td>MedTE</td>
          <td>~0.1B</td>
          <td>GTE-Base + self-supervised contrastive on 7 medical corpora</td>
          <td class="lb-score">MedTEB mean 0.578</td>
          <td><a href="https://arxiv.org/abs/2507.19407">2507.19407</a></td>
        </tr>
        <tr>
          <td class="lb-rank">4</td>
          <td>BiCA-Base</td>
          <td>~0.1B</td>
          <td>GTE-Base + 2-hop citation hard negatives, 20K examples</td>
          <td class="lb-score">Consistent BEIR + LoTTE ↑</td>
          <td><a href="https://arxiv.org/abs/2511.08029">2511.08029</a></td>
        </tr>
        <tr>
          <td class="lb-rank">5</td>
          <td>MedCPT</td>
          <td>~0.1B</td>
          <td>PubMedBERT + 255M click-log contrastive (retriever + reranker)</td>
          <td class="lb-score">Zero-shot SOTA on 5 bio IR tasks</td>
          <td><a href="https://arxiv.org/abs/2307.00589">2307.00589</a></td>
        </tr>
        <tr>
          <td class="lb-rank">6</td>
          <td>BioLORD-2023</td>
          <td>~0.1B</td>
          <td>PubMedBERT + UMLS definitions contrastive + LLM distillation + WA</td>
          <td class="lb-score">SOTA MedSTS, EHR-Rel-B</td>
          <td><a href="https://arxiv.org/abs/2311.16075">2311.16075</a></td>
        </tr>
      </tbody>
    </table>
  </div>
</section>

<!-- GET STARTED -->
<section id="start">
  <div class="section-head">
    <h2><span class="icon">🚀</span> Get Started</h2>
    <p>Recommended learning path for biomedical text retrieval.</p>
  </div>
  <div class="path-list">

    <div class="path-step">
      <div class="num">1</div>
      <div class="content">
        <h3>Understand the evaluation landscape</h3>
        <p>Read the <a href="https://arxiv.org/abs/2104.08663">BEIR paper</a> to understand why domain generalization is hard. Run BM25 as your baseline on <a href="https://hf.co/datasets/BeIR/nfcorpus">NFCorpus</a> — it's surprisingly competitive and sets a meaningful floor.</p>
      </div>
    </div>

    <div class="path-step">
      <div class="num">2</div>
      <div class="content">
        <h3>Try a zero-shot retriever</h3>
        <p>Use <a href="https://hf.co/ncbi/MedCPT-Query-Encoder">MedCPT</a> — the cleanest example of domain-specific contrastive pretraining. Separate query + article encoders make it intuitive. Evaluate on BEIR biomedical subsets.</p>
      </div>
    </div>

    <div class="path-step">
      <div class="num">3</div>
      <div class="content">
        <h3>Scale up with LLM retrievers</h3>
        <p>Deploy <a href="https://hf.co/BMRetriever/BMRetriever-410M">BMRetriever-410M</a> for production — it outperforms models 11× larger. Use instruction-formatted queries with last-token pooling. The eval code is clean and well-documented.</p>
      </div>
    </div>

    <div class="path-step">
      <div class="num">4</div>
      <div class="content">
        <h3>Comprehensive evaluation</h3>
        <p>Benchmark on <a href="https://github.com/MohammadKhodadad/MedTEB">MedTEB</a> — 51 medical embedding tasks, much broader than BEIR biomedical subsets alone. This is the new comprehensive standard (2025).</p>
      </div>
    </div>

    <div class="path-step">
      <div class="num">5</div>
      <div class="content">
        <h3>Fine-tune your own retriever</h3>
        <p>Use <a href="https://arxiv.org/abs/2511.08029">BiCA's</a> citation-graph hard negatives for cheap, effective training data. Or <a href="https://arxiv.org/abs/2604.15591">BioHiCL's</a> MeSH hierarchy supervision — 0.1B params matching 1B+ models.</p>
      </div>
    </div>

  </div>
</section>

<!-- FOOTER -->
<footer>
  <p>Built with 🧬 as a resource hub for biomedical text retrieval research.</p>
  <p>All linked resources are maintained by their respective authors. <a href="https://huggingface.co/lvwerra">@lvwerra</a></p>
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