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--- |
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license: cc0-1.0 |
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language: |
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- en |
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tags: |
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- pubmed |
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pretty_name: PubMedAbstractSubset |
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--- |
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# PubMed Abstracts Subset |
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This dataset contains a probabilistic sample of publicly available PubMed metadata sourced from the [National Library of Medicine (NLM)](https://pubmed.ncbi.nlm.nih.gov/). |
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If you're looking for the precomputed embedding vectors (MedCPT) used in our work [*Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation*](https://arxiv.org/abs/2505.07917), they are available in a separate dataset: [slinusc/PubMedAbstractsSubsetEmbedded](https://huggingface.co/datasets/slinusc/PubMedAbstractsSubsetEmbedded). |
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--- |
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## 📄 Description |
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Each entry in the dataset includes: |
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- `title`: Title of the publication |
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- `abstract`: Abstract text |
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- `PMID`: PubMed identifier |
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The dataset is split into 24 `.jsonl` files, each containing approximately 100,000 entries, for a total of ~2.39 million samples. |
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--- |
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## 🔍 How to Access |
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### ▶️ Option 1: Load using Hugging Face `datasets` (streaming) |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("slinusc/PubMedAbstractsSubset", streaming=True) |
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for doc in dataset: |
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print(doc["title"], doc["abstract"]) |
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break |
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``` |
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> Streaming is recommended for large-scale processing and avoids loading the entire dataset into memory. |
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--- |
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### 💾 Option 2: Clone using Git and 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/PubMedAbstractsSubset |
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cd PubMedAbstractsSubset |
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``` |
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> After cloning, run `git lfs pull` if needed to retrieve the full data files. |
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--- |
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## 📦 Format |
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Each file is in `.jsonl` (JSON Lines) format, 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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} |
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``` |
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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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- Used in: |
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**Stuhlmann et al. (2025)**, *Efficient and Reproducible Biomedical QA using RAG*, [arXiv:2505.07917](https://arxiv.org/abs/2505.07917) |
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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 entries, 24 JSONL files) |
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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 issues, please open a discussion or pull request on the Hugging Face dataset page. |
