Create README.md

README.md

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+# PubMed Abstracts Subset (10%)
+
+This dataset contains a 10% probabilistic sample of the ~24 million PubMed abstracts available as public metadata from the [National Library of Medicine (NLM)](https://pubmed.ncbi.nlm.nih.gov/). The dataset was originally compiled and released as part of the [MedRAG benchmark](https://arxiv.org/abs/2402.13178), and has been reformatted and republished as part of the study:
+
+**Stuhlmann et al. (2025)**  
+*Efficient and Reproducible Biomedical Question Answering using Retrieval‑Augmented Generation*  
+→ [arXiv:2505.07917](https://arxiv.org/abs/2505.07917)
+
+---
+
+## 📄 Description
+
+Each entry in the dataset includes:
+- `id`: Local unique identifier
+- `title`: Title of the publication
+- `abstract`: Abstract text
+- `PMID`: PubMed identifier
+
+The dataset is split into 24 `.jsonl` files, each containing approximately 100,000 entries, for a total of ~2.39 million samples.
+
+---
+
+## 🔍 How to Access
+
+### ▶️ Option 1: Load using Hugging Face `datasets` (streaming)
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("slinusc/PubMedAbstractsSubset", streaming=True)
+
+for doc in dataset:
+    print(doc["title"], doc["abstract"])
+    break
+```
+
+> Streaming is recommended for large-scale processing and avoids loading the entire dataset into memory.
+
+---
+
+### 💾 Option 2: Clone using Git and Git LFS
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/slinusc/PubMedAbstractsSubset
+cd PubMedAbstractsSubset
+```
+
+> After cloning, run `git lfs pull` if needed to retrieve the full data files.
+
+---
+
+## 📦 Format
+
+Each file is in `.jsonl` (JSON Lines) format, where each line is a valid JSON object:
+
+```json
+{
+  "id": "pubmed23n1166_0",
+  "title": "...",
+  "abstract": "...",
+  "PMID": 36464820
+}
+```
+
+---
+
+## 📚 Source and Licensing
+
+This dataset is derived from public domain PubMed metadata (titles and abstracts), redistributed in accordance with [NLM data usage policies](https://www.nlm.nih.gov/databases/download/data_distrib_main.html).
+  
+- Reformatted and used in:  
+  **Stuhlmann et al. (2025)**, *Efficient and Reproducible Biomedical QA using RAG*, [arXiv:2505.07917](https://arxiv.org/abs/2505.07917)
+
+---
+
+## ✨ Citation
+
+If you use this dataset, please cite:
+
+```
+
+@article{stuhlmann2025efficient,
+  title={Efficient and Reproducible Biomedical Question Answering using Retrieval-Augmented Generation},
+  author={Stuhlmann, Linus and Saxer, Michael Alexander and Fürst, Jonathan},
+  journal={arXiv preprint arXiv:2505.07917},
+  year={2025}
+}
+```
+
+---
+
+## 🏷️ Version
+
+- `v1.0` – Initial release (10% sample, 2.39M entries, 24 JSONL files)
+
+---
+
+## 📬 Contact
+
+Maintained by [@slinusc](https://huggingface.co/slinusc).  
+For questions or issues, please open a discussion or pull request on the Hugging Face dataset page.