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OpenMed/OpenMed NER DNADetect SuperMedical 125M

Advanced NLP model for medical named entity recognition and DNA detection.

Status
Not yet determined
Last checked
Unverified
License
Open source
Pricing
Free tier · from $0
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What it is

This Hugging Face model specializes in token classification tasks, particularly suited for identifying entities within medical texts and detecting DNA sequences. It is part of the OpenMed suite and has been downloaded over 163,572 times.

This model stands out due to its specialization in medical named entity recognition and DNA detection, making it particularly valuable for healthcare applications that require precise identification of entities within medical texts.

When it fits — and when it doesn’t

A good fit for

  • Teams working on healthcare applications that require precise entity recognition from medical texts.

  • Researchers analyzing large volumes of biomedical literature for specific named entities and DNA sequences.

Look elsewhere if

  • Projects requiring real-time processing, as this model is designed for batch processing.

  • Applications outside the medical domain where specialized NER models are not necessary.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited language support beyond Python

    The model is primarily designed and supported for use with Python, which can be a barrier for developers working in other languages.

  2. 02

    Performance issues with large datasets

    When processing extensive medical documents or genomic sequences, the tool may experience significant slowdowns due to its computational demands.

  3. 03

    Documentation lacks depth and examples for complex use cases

    The documentation focuses on basic usage but fails to provide detailed guidance on advanced configurations or troubleshooting common issues.

  4. 04

    Dependence on external libraries can lead to version conflicts

    Integration with other Python packages like transformers and PyTorch may cause dependency conflicts, especially when these libraries are updated independently of the model.

What it does well

  • Specializes in medical named entity recognition and DNA sequence detection.

  • High download count indicating community interest and reliability.

  • Part of the OpenMed suite, suggesting a focus on healthcare applications.

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up OpenMed/OpenMed NER DNADetect SuperMedical 125M

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →