OpenMed/OpenMed NER PathologyDetect TinyMed 135M
TinyMed model for pathology detection in medical text
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is OpenMed/OpenMed NER PathologyDetect TinyMed 135M?
This model specializes in Named Entity Recognition (NER) for detecting pathologies in medical texts, aiding in the automated analysis of clinical notes and reports.
Key differentiator
“This model stands out as an efficient, specialized tool for detecting pathologies in medical texts, making it ideal for resource-constrained environments and specific use cases.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Model is fine-tuned for pathology detection in medical texts and may not perform well on other types of NER tasks or non-medical text.
The open-source project has a limited number of contributors, which can lead to slower issue resolution and feature development.
Documentation is lacking detailed examples and explanations for more complex configurations or customizations beyond basic usage scenarios.
The model's efficiency in resource-constrained environments may lead to slower processing times when handling very large volumes of clinical notes and reports.
Fit analysis
Who is it for?
✓ Best for
Teams working on automated extraction of pathologies from medical texts
Projects requiring efficient NER models for resource-constrained environments
✕ Not a fit for
Real-time processing applications with strict latency requirements
Applications needing a wide range of entity types beyond pathology detection
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 PathologyDetect TinyMed 135M
Step-by-step setup guide with code examples and common gotchas.