Datasets:

aaekay
/

pubmed-embeddings

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

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:

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.