Youssef9393/Data-Collection-Web-Scrapping-Fine-Tuning-LLMs

Multilingual medical Question-Answering dataset in Darija, French, English, collected through web scraping from trusted medical sources and designed for fine-tuning LLM , healthcare AI, clinical NLP. It includes real patient–doctor conversations with structured medical annotations to support medical question answering, information extraction ## YeMedQA: A Multilingual Medical Question-Answering Dataset for Fine-Tuning and Evaluation LLMs Healthcare, and Clinical NLP * Lien dataset en hugging face : huggingface.co * lien Model Med_qwen en hugging face : huggingface.co # Overview: **YeMedQA** is a multilingual Question-Answering dataset designed for healthcare NLP applications. It focuses on **patient–doctor medical conversations** in: - Darija - English - French **Keywords:** Medical Question Answering (MedQA), Large Language Models (LLMs), Natural Language Processing (NLP), AI in Healthcare ## Project Structure --> The dataset supports the development of **culturally and linguistically adapted medical AI systems**. ## 🌐 Data Collection YeMedQA was constructed using: ### 1. Web Scraping (Verified Medical Sources) Medical content was collected and curated from trusted healthcare platforms: - www.icliniq.com - www.altibbi.com ### 2. Hugging Face Open Data - Publicly available medical QA datasets (ANR-Maladies) These sources were selected for their: - High medical credibility - Real patient–doctor interactions - Multilingual content availability ### Dataset Splits | Split | Examples | Size (MB) | | :--- | :---: | :---: | | **Train** | 7,460 | 6.95 MB | | **Test** | 829 | 0.77 MB | | **Total** | **8,289** | **7.72 MB** | ## Column: | Feature | Type | Description | | :--- | :--- | :--- | | `id` | `string` | Unique ID | | `question` | `string` | The patient question(e.g., in Darija) | | `context_question` | `string` | Clinical context or patient background | | `answer` | `string` | Responce by Doctor Professional medical | | `article_title` | `string` | Title of the reference medical article | | `language` | `string` | Language of the entry (Darija, FR, EN) | | `urgency` | `string` | Severity level (Low, Medium, High) | | `speciality` | `string` | Medical department (e.g., Cardiology, Immunology …