---
title: pubmedbert-base-embeddings
description: This is a PubMedBERT-base model fined-tuned using sentence-transformers. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of PubMed title-abstract pairs along with similar title
tags:
- bert
- en
- feature-extraction
- literature
- nlp
- sentence-similarity
- text-embeddings-inference
license: apache-2.0
source_url: https://huggingface.co/NeuML/pubmedbert-base-embeddings
---

# pubmedbert-base-embeddings

> **Mirrored metadata.** This card is reproduced on Atlas for discovery. The artifact itself is not hosted here — follow the source link for the files, and refer to the upstream licence for terms of use.

This is a PubMedBERT-base model fined-tuned using sentence-transformers. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of PubMed title-abstract pairs along with similar title

Upstream identifier: `NeuML/pubmedbert-base-embeddings`.

**Source:** https://huggingface.co/NeuML/pubmedbert-base-embeddings

**Licence:** apache-2.0

_This model is part of the Atlas demo catalogue. See the repository history for provenance._