Create README.md

README.md

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+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- pubmed
+- embeddings
+- medcpt
+- biomedical
+- retrieval
+- rag
+- medical
+pretty_name: PubMedAbstractsSubsetEmbedded
+---
+
+# PubMed Abstracts Subset with MedCPT Embeddings (float32)
+
+This dataset contains a 10% probabilistic sample of ~24 million PubMed abstracts, enriched with precomputed dense embeddings from the **`ncbi/MedCPT-Article-Encoder`** model. It is derived from public metadata made available via the [National Library of Medicine (NLM)](https://pubmed.ncbi.nlm.nih.gov/) and was used in the paper [*Efficient and Reproducible Biomedical QA using Retrieval-Augmented Generation*](https://arxiv.org/abs/2505.07917).
+
+Each entry includes:
+- `title`: Title of the publication
+- `abstract`: Abstract content
+- `PMID`: PubMed identifier
+- `embedding`: 768-dimensional float32 vector from MedCPT
+
+The original identifier (`id`) was removed to reduce redundancy since the `PMID` serves as a unique key.
+
+---
+
+## 🔍 How to Access
+
+### ▶️ Option 1: Load via Hugging Face `datasets`
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("slinusc/PubMedAbstractsSubsetEmbedded", streaming=True)
+
+for doc in dataset:
+    print(doc["PMID"], doc["embedding"][:5])  # print first 5 dims
+    break
+```
+
+> Each vector is stored as a list of 768 `float32` values (compact, no line breaks).
+
+---
+
+### 💾 Option 2: Git Clone with Git LFS
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/slinusc/PubMedAbstractsSubsetEmbedded
+cd PubMedAbstractsSubsetEmbedded
+```
+
+---
+
+## 📦 Format
+
+Each file is a `.jsonl` (JSON Lines) file, where each line is a valid JSON object:
+
+```json
+{
+  "title": "...",
+  "abstract": "...",
+  "PMID": 36464820,
+  "embedding": [-0.1952, 0.0266, ..., 0.0843]
+}
+```
+
+> The embeddings are 768-dimensional dense vectors, serialized as 32-bit floats.
+
+---
+
+## 📚 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).  
+MedCPT embeddings were generated using the [ncbi/MedCPT-Article-Encoder](https://huggingface.co/ncbi/MedCPT-Article-Encoder) model.
+
+---
+
+## 📣 Citation
+
+If you use this dataset or the included MedCPT embeddings, please cite:
+
+> **Stuhlmann et al. (2025)**  
+> *Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation*  
+> [arXiv:2505.07917](https://arxiv.org/abs/2505.07917)  
+> [https://github.com/slinusc/medical_RAG_system](https://github.com/slinusc/medical_RAG_system)
+
+---
+
+## 🏷️ Version
+
+- `v1.0` – Initial release (2.39M samples, 24 JSONL files, float32 embeddings, ~23 GB total)
+
+---
+
+## 📬 Contact
+
+Maintained by [@slinusc](https://huggingface.co/slinusc).  
+For questions or collaborations, open a discussion on the HF Hub.