dmis-lab/biobert

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2204 stars · 478 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BioBERT is a pre-trained biomedical language representation model (based on BERT) with code for fine-tuning it on biomedical text mining tasks such as named entity recognition, relation extraction, and question answering. The repository distributes multiple versions of pre-trained weights trained on PubMed and PMC corpora, primarily for TensorFlow 1 with a separate PyTorch port.

Use cases

• fine-tune a pretrained BERT model on biomedical text
• named entity recognition for diseases and drugs in PubMed abstracts
• extract relations between biomedical entities from scientific literature
• biomedical question answering over research text
• download BioBERT pretrained weights for transfer learning
• adapt a language model to domain-specific biomedical corpora

When to choose

• you need a domain-adapted BERT checkpoint for biomedical NLP tasks
• you are reproducing the BioBERT paper's NER, RE, or QA benchmarks
• you want to fine-tune on PubMed/PMC-derived text with TensorFlow 1 or the PyTorch port
• you need a proven baseline model for biomedical text mining research

When to avoid

• you need a modern, actively maintained stack (the code targets TensorFlow 1 and Python <= 3.7)
• your text is general-domain rather than biomedical (plain BERT or newer models fit better)
• you want a no-code entity recognizer rather than a fine-tuning codebase (use the linked BERN tool instead)
• you require a permissive license - the license is non-standard and must be reviewed

Facets

library · maturity maintenance

machine-learning nlp deep-learning transformers bioinformatics healthcare artificial-intelligence python bert biomedical-text-mining pretrained-model named-entity-recognition relation-extraction question-answering pubmed tensorflow fine-tuning research natural-language-processing linux gpu

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markdown · JSON · MCP: product_card(name="dmis-lab/biobert")