monfortbrian/lung_cancer_5K.jsonl
Lung Cancer Dataset 🫁 A curated dataset of prompt–completion pairs designed for fine-tuning Large Language Models (LLMs) on lung cancer diagnostics.The dataset contains 5,000 rows of text pairs prepared for medical AI research, clinical assistants, and healthcare copilots. 📊 Dataset Overview Size: 5,000 prompt–completion pairs Format: JSONL, CSV Domain: Lung Cancer (diagnosis, symptoms, treatment, follow-up) Use Case: Training LLMs for Doctor Copilot and… See the full description on the dataset page: https://huggingface.co/datasets/monfortbrian/lung_cancer_5K.jsonl.
Lung Cancer Dataset 🫁
A curated dataset of prompt–completion pairs designed for fine-tuning Large Language Models (LLMs) on lung cancer diagnostics. The dataset contains 5,000 rows of text pairs prepared for medical AI research, clinical assistants, and healthcare copilots.
📊 Dataset Overview
- Size: 5,000 prompt–completion pairs
- Format:
JSONL,CSV - Domain: Lung Cancer (diagnosis, symptoms, treatment, follow-up)
- Use Case: Training LLMs for Doctor Copilot and clinical diagnostic support
🛠️ Data Source & Preparation
- Collection: Derived from internet-sourced content, carefully paraphrased and curated.
- Processing: Cleaned, normalized, and anonymized to remove sensitive information.
- Structure: Optimized for PEFT / LoRA fine-tuning, with balanced prompt–completion alignment.
🚀 Applications
This dataset is intended for:
- Fine-tuning LLMs for fast, domain-specific medical inference
- Building diagnostic copilots for doctors and healthcare professionals
- Research into AI-assisted cancer detection & treatment planning
📂 File Formats
lung_cancer_dataset.jsonl→ JSON Lines format (recommended for training)lung_cancer_dataset.csv→ CSV format for exploration and preprocessing
⚠️ Disclaimer
This dataset is for research and educational purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment.
💡 Inspiration
This dataset is part of the Doctor Copilot MVP, aiming to create safe, reliable, and efficient AI-assisted medical systems for early detection and better patient outcomes.
