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BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim +2 morePublished Sep 5, 2019
DOI Publisher
Researcher verdict
Context only
Use as context only
Benchmark evidence
Missing
Not verified yet
Time to first repro
A few days
Plan setup time
Risk flags
1
Review before use

Abstract

Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.

Results and benchmarks

Freshness tier: cold
We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.

Implementation

No direct implementation yet

Maintained implementation evidence is not confirmed for this paper yet.

Use the implementation status and reproduction sections for the current action plan.

Implementation evidence summary
Confidence: medium

AI-in-Health/MedLLMsPracticalGuide is the closest maintained adjacent implementation (Matches contextual method/domain keyword: language model). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 2040 GitHub stars.

Reproduction risks
  • Adjacent implementations are not paper-verified
  • Recommended repository is adjacent and not paper-verified.

Reproduction readiness

Time to first repro: days
Last checked: Aug 25, 2026

No repo

No verified implementation available

  • No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

Hardware requirements

  • Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Repositories and ecosystem

Closest related implementations

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Hugging Face artifacts

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Research context

7,331

Citations

61

References

Tasks

Biomedical text mining, Computer science, Named-entity recognition, Relationship extraction, Text mining, Text corpus, Representation (politics), F1 score

Methods

Language model

Domains

Artificial intelligence, Natural language processing, Biochemistry, Genetics and Molecular Biology

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Biomedical text mining Computer science Named-entity recognition Relationship extraction Text mining Text corpus