OpenMed/OpenMed NER GenomeDetect ModernMed 149M
NER model for genome detection in medical texts
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is OpenMed/OpenMed NER GenomeDetect ModernMed 149M?
A token-classification model designed to detect named entities related to genomes within modern medical literature, aiding researchers and practitioners in extracting relevant genomic information.
Key differentiator
“This model is uniquely tailored for genome detection in medical texts, offering high precision and recall specifically for this domain.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
Model training and testing have been primarily focused on English texts, with no documented support for other languages.
The model may experience significant slowdowns when processing extensive medical literature due to its specialized nature and complexity.
Integration with the latest versions of the transformers library may introduce incompatibilities, requiring frequent updates or patches.
Fit analysis
Who is it for?
✓ Best for
Teams working on genomics projects who need precise named entity recognition in medical texts
Medical researchers looking to automate the extraction of genomic data from literature
✕ Not a fit for
Projects requiring real-time processing of large volumes of text
Applications that require multi-lingual support beyond English
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Integrations
Next step
Get Started with OpenMed/OpenMed NER GenomeDetect ModernMed 149M
Step-by-step setup guide with code examples and common gotchas.