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license: cc-by-4.0 |
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language: |
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- en |
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tags: |
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- pubmed |
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- embeddings |
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- medcpt |
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- biomedical |
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- retrieval |
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- rag |
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- medical |
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pretty_name: PubMedAbstractsSubsetEmbedded |
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--- |
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# PubMed Abstracts Subset with MedCPT Embeddings (float32) |
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This dataset contains a probabilistic sample of ~2.4 million PubMed abstracts, enriched with precomputed dense embeddings (title + abstract), 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). |
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Each entry includes: |
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- `title`: Title of the publication |
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- `abstract`: Abstract content |
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- `PMID`: PubMed identifier |
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- `embedding`: 768-dimensional float32 vector from MedCPT |
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--- |
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## 🔍 How to Access |
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### ▶️ Option 1: Load via Hugging Face `datasets` |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("slinusc/PubMedAbstractsSubsetEmbedded", streaming=True) |
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for doc in dataset: |
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print(doc["PMID"], doc["embedding"][:5]) # print first 5 dims |
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break |
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``` |
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> Each vector is stored as a list of 768 `float32` values (compact, no line breaks). |
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--- |
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### 💾 Option 2: Git Clone with Git LFS |
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```bash |
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git lfs install |
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git clone https://huggingface.co/datasets/slinusc/PubMedAbstractsSubsetEmbedded |
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cd PubMedAbstractsSubsetEmbedded |
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``` |
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--- |
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## 📦 Format |
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Each file is a `.jsonl` (JSON Lines) file, where each line is a valid JSON object: |
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```json |
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{ |
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"title": "...", |
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"abstract": "...", |
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"PMID": 36464820, |
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"embedding": [-0.1952481, ... , 0.2887376] |
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} |
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``` |
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> The embeddings are 768-dimensional dense vectors, serialized as 32-bit floats. |
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--- |
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## 📚 Source and Licensing |
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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). |
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MedCPT embeddings were generated using the [ncbi/MedCPT-Article-Encoder](https://huggingface.co/ncbi/MedCPT-Article-Encoder) model. |
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--- |
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## 📣 Citation |
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If you use this dataset or the included MedCPT embeddings, please cite: |
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> **Stuhlmann et al. (2025)** |
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> *Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation* |
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> [arXiv:2505.07917](https://arxiv.org/abs/2505.07917) |
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> [https://github.com/slinusc/medical_RAG_system](https://github.com/slinusc/medical_RAG_system) |
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--- |
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## 🏷️ Version |
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- `v1.0` – Initial release (2.39M samples, 24 JSONL files, float32 embeddings, ~23 GB total) |
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--- |
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## 📬 Contact |
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Maintained by [@slinusc](https://huggingface.co/slinusc). |
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For questions or collaborations, open a discussion on the HF Hub. |
