Update README.md

README.md

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----
-base_model: BAAI/bge-base-en-v1.5
-library_name: peft
-tags:
-- base_model:adapter:BAAI/bge-base-en-v1.5
-- lora
-- transformers
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
+# BioHiCL-base: Hierarchical Multi-Label Contrastive Biomedical Retriever
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+## Model Card
 
-#### Preprocessing [optional]
+## 🔍 Overview
+BioHiCL-base is a biomedical dense retriever trained with hierarchical MeSH supervision to capture fine-grained semantic relationships between biomedical texts.
 
-[More Information Needed]
+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.
 
+---
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
+## 💡 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  
 
-#### Hardware
+---
 
-[More Information Needed]
+## 🧠 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**: 8192 tokens  
+- **Training data**: Biomedical abstracts annotated with MeSH labels (e.g., BioASQ-derived corpora)  
 
-#### Software
+---
 
-[More Information Needed]
+## ⚙️ 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
 
-## Citation [optional]
+---
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## ⚙️ How It Works
+BioHiCL aligns:
 
-**BibTeX:**
+- Embedding similarity (SimE): cosine similarity between document embeddings  
+- Label similarity (SimL): cosine similarity over weighted MeSH multi-label vectors  
+---
 
-[More Information Needed]
+---
 
-**APA:**
+## 🚀 Usage (BEIR Evaluation)
 
-[More Information Needed]
+```python
+from beir import util
+from beir.datasets.data_loader import GenericDataLoader
+from sentence_transformers import SentenceTransformer
+from beir.retrieval.search.dense import DenseRetrievalExactSearch
+from beir.retrieval.evaluation import EvaluateRetrieval
 
-## Glossary [optional]
+# Dataset
+dataset = "scifact"
+url = "https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip"
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+data_path = util.download_and_unzip(url, "datasets")
+corpus, queries, qrels = GenericDataLoader(data_path).load(split="test")
 
-[More Information Needed]
+# Model
+model_name = "LunaLan07/BioHiCL-base"
+model = SentenceTransformer(model_name)
 
-## More Information [optional]
+# Retrieval
+retriever = DenseRetrievalExactSearch(model, batch_size=16)
+results = retriever.search(corpus, queries, top_k=10, score_function="cos_sim")
 
-[More Information Needed]
+# Evaluation
+ndcg, _map, recall, precision = EvaluateRetrieval.evaluate(
+    qrels, results, k_values=[1, 3, 5, 10]
+)
 
-## Model Card Authors [optional]
+```
 
-[More Information Needed]
+## 📖 Citation
 
-## Model Card Contact
+If you use this model, please cite:
 
-[More Information Needed]
-### Framework versions
+```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}
+}
 
-- PEFT 0.18.0
+```