| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: PubMed Embedding Vectors |
| tags: |
| - pubmed |
| - biomedical |
| - embeddings |
| - qdrant |
| - parquet |
| - retrieval |
| size_categories: |
| - 10M<n<100M |
| configs: |
| - config_name: neuml_pubmedbert_base_embeddings |
| data_files: |
| - split: train |
| path: data/neuml_pubmedbert_base_embeddings/*.parquet |
| - config_name: qwen3_embedding_0_6b |
| data_files: |
| - split: train |
| path: data/qwen3_embedding_0_6b/*.parquet |
| --- |
| |
| # PubMed Embedding Vectors |
|
|
| This dataset contains embedding vectors generated from local PubMed title and abstract text. |
| It is designed for biomedical retrieval and nearest-neighbor research. |
|
|
| The public files intentionally do not include PubMed titles, abstracts, or full text. |
| Rows contain PMIDs, embeddings, hashes, and lightweight metadata so researchers can join |
| against their own authorized PubMed mirror or the official NCBI/PubMed services. |
|
|
| ## Configs |
|
|
| | Config | Model | Dim | Rows | Qdrant collection | |
| | --- | --- | ---: | ---: | --- | |
| | neuml_pubmedbert_base_embeddings | `NeuML/pubmedbert-base-embeddings` | 768 | 28,460,827 | `pubmed_emb_neuml_pubmedbert_base_embeddings_ddbc790c` | |
| | qwen3_embedding_0_6b | `Qwen/Qwen3-Embedding-0.6B` | 1024 | 28,460,827 | `pubmed_emb_qwen_qwen3_embedding_0_6b_cdca07b0` | |
|
|
| ## Columns |
|
|
| - `pmid`: PubMed identifier. |
| - `embedding`: fixed-size embedding vector for the selected config. |
| - `text_sha256`: SHA-256 of the local title+abstract text used for embedding, when available. |
| - `pub_year`, `pub_month`: publication date metadata, when available. |
| - `raw_token_count`, `used_token_count`, `was_truncated`: embedding input token metadata, when available. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| repo_id = "aaekay/pubmed-embedding" |
| ds = load_dataset(repo_id, "qwen3_embedding_0_6b", split="train", streaming=True) |
| row = next(iter(ds)) |
| print(row["pmid"], len(row["embedding"])) |
| ``` |
|
|
| Use the PMID to retrieve citation details from PubMed: |
|
|
| ```python |
| pmid = row["pmid"] |
| url = f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/" |
| ``` |
|
|
| ## Source And Redistribution Notes |
|
|
| - Source records come from a local PubMed baseline/update mirror. |
| - NLM notes that PubMed abstracts may be protected by third-party copyright, so this |
| dataset excludes article titles, abstracts, and full text. |
| - The generated embedding dataset is released as `cc-by-4.0`; upstream PubMed records |
| and embedding models remain subject to their own terms. |
|
|
| Relevant upstream documentation: |
|
|
| - PubMed download page: https://pubmed.ncbi.nlm.nih.gov/download/ |
| - NLM copyright information: https://www.nlm.nih.gov/databases/download.html |
| - Hugging Face large repository guidance: https://huggingface.co/docs/hub/storage-limits |
|
|
| ## Manifest |
|
|
| Export metadata, shard checksums, and source collection details are stored in |
| `metadata/manifest.json`. The public schema is stored in `metadata/schema.json`. |
|
|
