OpenMed-PII-ClinicDischarge-110M-v1

PII Detection Model | 110M Parameters | Open Source

Model Description

OpenMed-PII-ClinicDischarge-110M-v1 is a transformer-based token classification model fine-tuned for Personally Identifiable Information (PII) detection in text. This model identifies and classifies 54 types of sensitive information including names, addresses, SSNs, medical record numbers, and more.

Key Features

• High Accuracy: Achieves strong F1 scores across diverse PII categories
• Comprehensive Coverage: Detects 50+ entity types spanning personal, financial, medical, and contact information
• Privacy-Focused: Designed for de-identification and compliance with HIPAA, GDPR, and other privacy regulations
• Production-Ready: Optimized for real-world text processing pipelines

Performance

Evaluated on a stratified 2,000-sample test set from NVIDIA Nemotron-PII:

Top 10 PII Models

Best Performing Entities

Challenging Entities

These entity types have lower performance and may benefit from additional post-processing:

Supported Entity Types

This model detects 54 PII entity types organized into categories:

Usage

Quick Start

from transformers import pipeline

# Load the PII detection pipeline
ner = pipeline("ner", model="openmed/OpenMed-PII-ClinicDischarge-110M-v1", aggregation_strategy="simple")

text = """
Patient John Smith (DOB: 03/15/1985, SSN: 123-45-6789) was seen today.
Contact: john.smith@email.com, Phone: (555) 123-4567.
Address: 456 Oak Street, Boston, MA 02108.
"""

entities = ner(text)
for entity in entities:
 print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.3f})")

De-identification Example

def redact_pii(text, entities, placeholder='[REDACTED]'):
 """Replace detected PII with placeholders."""
 # Sort entities by start position (descending) to preserve offsets
 sorted_entities = sorted(entities, key=lambda x: x['start'], reverse=True)
 redacted = text
 for ent in sorted_entities:
 redacted = redacted[:ent['start']] + f"[{ent['entity_group']}]" + redacted[ent['end']:]
 return redacted

# Apply de-identification
redacted_text = redact_pii(text, entities)
print(redacted_text)

Batch Processing

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

model_name = "openmed/OpenMed-PII-ClinicDischarge-110M-v1"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

texts = [
 "Contact Dr. Jane Doe at jane.doe@hospital.org",
 "Patient SSN: 987-65-4321, MRN: 12345678",
]

inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
with torch.no_grad():
 outputs = model(**inputs)
 predictions = torch.argmax(outputs.logits, dim=-1)

Training Details

Dataset

• Source: NVIDIA Nemotron-PII
• Format: BIO-tagged token classification
• Labels: 106 total (53 entity types × 2 BIO tags + O)
• Splits: 50K train / 5K validation / 45K test

Training Configuration

• Max Sequence Length: 384 tokens
• Label Strategy: First token only (label_all_tokens=False)
• Framework: Hugging Face Transformers + Trainer API

Intended Use & Limitations

Intended Use

• De-identification: Automated redaction of PII in clinical notes, medical records, and documents
• Compliance: Supporting HIPAA, GDPR, and privacy regulation compliance
• Data Preprocessing: Preparing datasets for research by removing sensitive information
• Audit Support: Identifying PII in document collections

Limitations

⚠️ Important: This model is intended as an assistive tool, not a replacement for human review.

• False Negatives: Some PII may not be detected; always verify critical applications
• Context Sensitivity: Performance may vary with domain-specific terminology
• Challenging Categories: occupation, time, and sexuality have lower F1 scores
• Language: Primarily trained on English text

Citation

@misc{openmed-pii-2026,
 title = {OpenMed-PII-ClinicDischarge-110M-v1: PII Detection Model},
 author = {OpenMed Science},
 year = {2026},
 publisher = {Hugging Face},
 url = {https://huggingface.co/openmed/OpenMed-PII-ClinicDischarge-110M-v1}
}

Links

• Organization: OpenMed

Base model

emilyalsentzer/Bio_Discharge_Summary_BERT

(10)

Models for extracting PII entities and de-identifying clinical text, with support for HIPAA and GDPR compliance. • 366 items • Updated 22 days ago • 36