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README.md

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-license: mit
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+---
+license: mit
+---
+
+# PerMedCQA: Persian Medical Consumer QA Benchmark 🧠🇮🇷
+
+**PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian**
+
+PerMedCQA is the first large-scale, real-world benchmark for Persian-language medical consumer question answering. It contains anonymized medical inquiries from Persian-speaking users paired with professional responses, enabling rigorous evaluation of large language models in low-resource, health-related domains.
+
+---
+
+## 📊 Dataset Overview
+
+- **Total entries**: 68,138 QA pairs  
+- **Source platforms**: DrYab, HiSalamat, GetZoop, Mavara-e-Teb  
+- **Timeframe**: Nov 10, 2022 – Apr 2, 2024  
+- **Languages**: Persian only  
+- **Licensing**: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+---
+
+## 🧬 Metadata & Features
+
+Each example in the dataset includes:
+
+- `instance_id`: Unique ID for each QA pair  
+- `Title`: Short user-submitted title  
+- `Question`: Full Persian-language consumer medical question  
+- `Expert_Answer`: Doctor’s response  
+- `Category`: Medical topic (e.g., “پوست و مو”)  
+- `Specialty`: Expert’s medical field (e.g., “متخصص پوست و مو”)  
+- `Age`: Reported patient age  
+- `Weight`: Reported weight (optional)  
+- `Sex`: Patient gender (`"man"` or `"woman"`)  
+- `dataset_source`: Name of the platform (e.g., DrYab, Getzoop)  
+- `Tag`: ICD‑11 label and rationale  
+- `QuestionType`: Question classification tag (e.g., "Contraindication", "Indication") and reasoning  
+
+---
+
+## 📁 Dataset Structure
+
+```json
+{
+  "Title": "قرمزی پوست نوزاد بعد از استفاده از پماد",
+  "Category": "پوست و مو",
+  "Specialty": "متخصص پوست و مو",
+  "Age": "1",
+  "Weight": "10",
+  "Sex": "man",
+  "dataset_source": "HiSalamat",
+  "instance_id": 32405,
+  "Tag": {
+    "Tag": 23,
+    "Tag_Reasoning": "The question addresses a skin reaction in an infant following the application of a cream, indicating a dermatological condition."
+  },
+  "Question": "سلام خسته نباشید. من واسه پسر ۱ ساله‌ام که جای واکسنش سفت شده بود، پماد موضعی استفاده کردم. ولی الان پوستش خیلی قرمز شده و خارش داره. ممکنه حساسیت داده باشه؟ باید چکار کنم؟",
+  "Expert_Answer": "احتمالا پوست نوزاد به ترکیبات پماد حساسیت نشان داده است. مصرف آن را قطع کنید و در صورت ادامه علائم به متخصص پوست مراجعه کنید.",
+  "QuestionType": {
+    "Explanation": "The user asks about an adverse skin reaction following the use of a topical medication on an infant, which is a case of possible side effects.",
+    "QuestionType_Tag": "SideEffect"
+  }
+}
+```
+
+---
+
+## 📥 How to Load
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("NaghmehAI/PerMedCQA", split="train")
+print(ds[0])
+```
+
+---
+
+## 🚀 Intended Uses
+
+- 🧠 **Evaluation** of multilingual or Persian-specific LLMs in real-world, informal medical domains  
+- 🛠️ **Few-shot or zero-shot** fine-tuning, instruction-tuning  
+- 🌍 **Cultural insights**: Persian language behavior in health-related discourse  
+- ⚠️ **NOT for clinical use**: Informational and research purposes only
+
+---
+
+## ⚙️ Data Processing Pipeline
+
+### Stage 1: Column Transformation (`change_columns.py`)
+- Reads CSV/JSON input and transforms them into structured JSON format
+- Handles single-turn, multi-turn, and multi-expert Q&A data
+- Cleans and formats the text, removing unnecessary whitespace and newlines
+- Creates a chat-style JSON file with `user` and `assistant` roles
+
+### Stage 2: QA Preprocessing (`preprocess_for_qa.py`)
+- Truncates multi-turn dialogues to first Q&A pair
+- Removes:
+  - Empty or invalid messages
+  - Q&A pairs shorter than 3 words
+  - Duplicate Q&A instances
+- Adds:
+  - `dataset_source` and `instance_id` to each item
+- Merges cleaned records into `All_QA_preprocessed.json`
+
+### Dataset Cleaning Results:
+| Dataset       | Step 1 Removed | Step 2 Removed | Step 3 Removed | Final Records |
+|--------------|----------------|----------------|----------------|----------------|
+| Dr_Yab       | 63             | 1083           | 25             | 37,905         |
+| GetZoop      | 1005           | 1352           | 8              | 25,502         |
+| Hi-Salamat   | 9580           | 121            | 1              | 5,220          |
+| Mavara-e-Teb | 0              | 1034           | 92             | 4,789          |
+
+---
+
+## 📚 Citation
+
+If you use **PerMedCQA**, please cite:
+
+```bibtex
+@misc{jamali2025permedcqa,
+  title={PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language},
+  author={Jamali, Naghmeh and Mohammadi, Milad and Baledi, Danial and Rezvani, Zahra and Faili, Heshaam},
+  year={2025},
+  eprint={2505.18331},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2505.18331}
+}
+```
+
+---
+
+## 📬 Contact
+
+For collaboration or questions:
+
+📧 Naghmeh Jamali – naghme.jamali.ai@gmail.com  
+🔗 [LinkedIn / ORCID / Google Scholar – optional]