Update model card with paper and repository links

Hi, I'm Niels from the community science team at Hugging Face. I'm opening this PR to improve the documentation for this model.

This PR:
- Adds a link to the associated research paper: [AutoPCR: Automated Phenotype Concept Recognition by Prompting](https://huggingface.co/papers/2507.19315).
- Adds a link to the official GitHub repository for the project.
- Provides a summary of the model's purpose in biomedical phenotype concept recognition (CR).
- Includes the BibTeX citation for the paper.

The metadata has also been updated to include domain-specific tags.

README.md

@@ -3,208 +3,50 @@ base_model: unsloth/Qwen3-30B-A3B-Instruct
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/Qwen3-30B-A3B-Instruct
 - lora
 - sft
 - transformers
-- trl
 - unsloth
+- biomedical
+- phenotype-recognition
 ---
 
-# Model Card for Model ID
+# AutoPCR: Automated Phenotype Concept Recognition by Prompting
 
-<!-- Provide a quick summary of what the model is/does. -->
+AutoPCR is a prompt-based phenotype concept recognition (CR) method designed to automatically generalize to new ontologies and unseen data without ontology-specific training. This repository contains the fine-tuned entity linker component of the system, which is a LoRA adapter for `unsloth/Qwen3-30B-A3B-Instruct`.
 
+- **Repository:** https://github.com/yctao7/AutoPCR
+- **Paper:** [AutoPCR: Automated Phenotype Concept Recognition by Prompting](https://huggingface.co/papers/2507.19315)
 
+## Model Description
 
-## Model Details
+Phenotype concept recognition (CR) is a fundamental task in biomedical text mining. Existing methods often struggle to generalize across diverse text styles or require extensive ontology-specific training. AutoPCR addresses these limitations by using a prompt-based approach and an optional self-supervised training strategy to achieve robust performance across multiple datasets. This model specifically serves as the entity linker within the pipeline to map extracted phenotype mentions to standard ontologies like HPO and MEDIC.
 
-### Model Description
+## Usage
 
-<!-- Provide a longer summary of what this model is. -->
+For detailed instructions on how to use this model within the AutoPCR framework—including environment setup, dictionary building, indexing, and running evaluation experiments—please refer to the [official GitHub repository](https://github.com/yctao7/AutoPCR).
 
+Example command for running HPO evaluation from the source code:
+```bash
+python HPO_evaluation.py --ontology_dict ../dict/HPO -c BIOC-GS -o ../results/bioc-gs.tsv --only_longest
+```
 
+## Citation
 
-- **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
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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. -->
+If you find this work useful, please cite:
 
 **BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.18.0
+```bibtex
+@misc{tao2025autopcr,
+      title={AutoPCR: Automated Phenotype Concept Recognition by Prompting}, 
+      author={Yichao Tao and others},
+      year={2025},
+      eprint={2507.19315},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.19315}, 
+}
+```
+
+## Contact
+Contact: drjieliu@umich.edu