ettin-68m-nemotron-pii model

Light Weight PII Detection Model | Open Source | 68M Parameters | 96.27 F1 Score | Blog Post

Overview

Ettin-68m-nemotron-pii is based on the ettin-encoder-68M model and fine-tuned over the Nemotron PII dataset. This model can detect 50+ PII entities in both structured and unstructured texts across various domains like healthcare, finance, legal, cybersecurity etc. With just 68M parameters, the model achieves a strong F1-score of 96.27.

Key Features

• Achieves strong F1-score of 96.27 with just 68M parameters.
• Outperforms popular LLMs like DeepSeek-V4-Flash (84.89) and GPT-4o-Mini (78.69).
• Detects 50+ PII entities in both structured and unstructured texts.
• Handles text in various domains like healthcare, finance, legal etc.

Model Visualization

Supported PII Entity Types

This model can detect the following 55 PII entity types

PII entity types with description

Usage

Without using Ettin PII Library

# First install Hugging Face transformers library
!pip install transformers

# Initialize and run the PII detection pipeline to extract PII entities
from transformers import pipeline

## Initialize the PII detection pipeline
ner = pipeline("ner", model="kalyan-ks/ettin-68m-nemotron-pii", aggregation_strategy="simple")

input_text = "Kalyan KS is from India. His email id is kalyan.ks@yahoo.com"

## Run the PII detection to extract PII entities
pii_entities = ner(input_text)

## Process the extracted PII entities 
def format_pii_entities(entities, original_text):
    if not entities:
        return []

    merged_entities = []

    entities = sorted(entities, key=lambda x: x['start'])

    current_entity = {
        'start': entities[0]['start'],
        'end': entities[0]['end'],
        'label': entities[0]['entity_group'],
        'text': entities[0]['word']
    }

    for next_ent in entities[1:]:
        is_same_label = next_ent['entity_group'] == current_entity['label']
        is_adjacent = next_ent['start'] <= current_entity['end'] + 1

        if is_same_label and is_adjacent:
            current_entity['end'] = max(current_entity['end'], next_ent['end'])
            current_entity['text'] = original_text[current_entity['start']:current_entity['end']]
        else:
            merged_entities.append(clean_entity(current_entity))
            current_entity = {
                'start': next_ent['start'],
                'end': next_ent['end'],
                'label': next_ent['entity_group'],
                'text': next_ent['word']
            }

    merged_entities.append(clean_entity(current_entity))
    return merged_entities

def clean_entity(ent):

    raw_text = ent['text']
    stripped_text = raw_text.strip()
    leading_spaces = len(raw_text) - len(raw_text.lstrip())

    return {
        'start': ent['start'] + leading_spaces,
        'end': ent['start'] + leading_spaces + len(stripped_text),
        'text': stripped_text,
        'label': ent['label']
    }

# Display the extracted PII entities
formatted_entities = format_pii_entities(pii_entities, input_text)
print(formatted_entities)

# Output
[{'start': 0, 'end': 9, 'text': 'Kalyan KS', 'label': 'first_name'}, {'start': 18, 'end': 23, 'text': 'India', 'label': 'country'}, {'start': 41, 'end': 60, 'text': 'kalyan.ks@yahoo.com', 'label': 'email'}]

With Ettin PII Library

# Install ettin-pii library with UV (or pip install ettin-pii)
!uv add ettin-pii  

from ettin_pii import PIIDetector

# Initialize detector (model="small", "tiny", or "nano")
detector = PIIDetector(model="small", confidence_threshold=0.6)

# 1. PII detection
result = detector.detect("Customer Alex Murphy (SSN: 987-65-4321) contacted us at alex.murphy@corp.org")
print(f"Found {result.num_entities} PII entities")

# 2. PII entity redaction
print(result.redact(strategy="fake"))

For more details regarding ettin-pii library, please refer to the Github Repo.

Evaluation

This model is evaluated on a 10k sample test set from Neomotron PII dataset and achieved the following results

Top Performing PII Entity Types

Challenging PII Entity Types

Limitations

• Language: This model works well only for English language texts.
• Challenging PII Entity Types: Some of the entity types like occupation has low F1 score.

Citation

@misc{ettin-68m-pii-2026,
  title = {ettin-68m-nemotron-pii-2026: PII Detection Model},
  author = {Kalyan KS},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/kalyan-ks/ettin-17m-nemotron-pii}
}