Obi/Deid Roberta I2b2
Roberta-based model for de-identification in medical text
Pricing
See website
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
—Overview
What is Obi/Deid Roberta I2b2?
A RoBERTa-based model fine-tuned on the i2b2 dataset for token classification tasks, specifically designed to identify and anonymize protected health information (PHI) in medical documents.
Key differentiator
“This RoBERTa-based model offers specialized de-identification capabilities tailored to medical text, providing high accuracy in identifying and anonymizing protected health information.”
Capability profile
Strength Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
Fit analysis
Who is it for?
✓ Best for
Teams working on healthcare projects that require de-identification of PHI in large volumes of clinical notes
Researchers who need to anonymize medical documents for compliance and confidentiality reasons
✕ Not a fit for
Projects requiring real-time processing of PHI data, as this model is designed for batch processing
Applications outside the healthcare domain where different types of sensitive information may be present
Cost structure
Pricing
Free Tier
None
Starts at
See website
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Next step
Get Started with Obi/Deid Roberta I2b2
Step-by-step setup guide with code examples and common gotchas.