README.md

metadata

language:
  - en
license: apache-2.0
library_name: gliner2
tags:
  - named-entity-recognition
  - ner
  - pii
  - anonymisation
  - gliner
  - gliner2
  - token-classification
  - privacy
datasets:
  - synthetic
base_model: fastino/gliner2-large-v1
model-index:
  - name: NERPA
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        metrics:
          - type: precision
            value: 0.93
            name: Micro-Precision
          - type: recall
            value: 0.9
            name: Micro-Recall
pipeline_tag: token-classification

NERPA — Fine-Tuned GLiNER2 for PII Anonymisation

A fine-tuned GLiNER2 Large (340M params) model trained to detect Personally Identifiable Information (PII) in text. Built as a flexible, self-hosted replacement for AWS Comprehend at Overmind.

Why NERPA?

AWS Comprehend is a solid NER service, but it's a black box. The specific problem we hit was date granularity — Comprehend labels both a Date of Birth and an Appointment Date as DATE, but for PII anonymisation these require very different treatment. A DOB must be redacted; an appointment date is often essential debugging context.

GLiNER2 is a bi-encoder model that takes both text and entity label descriptions as input, enabling zero-shot entity detection for arbitrary types. We fine-tuned GLiNER2 Large to:

1. Distinguish fine-grained date types (DATE_OF_BIRTH vs DATE_TIME)
2. Exceed AWS Comprehend accuracy on our PII benchmark

Model	Micro-Precision	Micro-Recall
AWS Comprehend	0.90	0.94
GLiNER2 Large (off-the-shelf)	0.84	0.89
NERPA (this model)	0.93	0.90

Fine-Tuning Details

• Base model: fastino/gliner2-large-v1 (DeBERTa v3 Large backbone, 340M params)
• Training data: 1,210 synthetic snippets generated with Gemini 3 Pro + Python Faker, each containing 2–4 PII entities
• Eval data: 300 held-out snippets (no template overlap with training)
• Strategy: Full weight fine-tuning with differential learning rates:
  • Encoder (DeBERTa v3): 1e-7
  • GLiNER-specific layers: 1e-6
• Batch size: 64
• Convergence: 175 steps

The synthetic data approach effectively distils the "knowledge" of a large LLM into a small, fast specialist model — what we call indirect distillation.

Supported Entity Types

Entity	Description
PERSON_NAME	Person name
DATE_OF_BIRTH	Date of birth
DATE_TIME	Generic date and time
EMAIL	Email address
PHONE	Phone numbers
LOCATION	Address, city, country, postcode, street
AGE	Age of a person
BUSINESS_NAME	Business name
USERNAME	Username
URL	Any URL
BANK_ACCOUNT_DETAILS	IBAN, SWIFT, routing numbers, etc.
CARD_DETAILS	Card number, CVV, expiration
DIGITAL_KEYS	Passwords, PINs, API keys
PERSONAL_ID_NUMBERS	Passport, driving licence, tax IDs
TECHNICAL_ID_NUMBERS	IP/MAC addresses, serial numbers
VEHICLE_ID_NUMBERS	License plates, VINs

Quick Start

Install dependencies

pip install gliner2 torch

Anonymise text (CLI)

# Inline text
python anonymise.py "Dear John Smith, born 15/03/1990. Contact: john@acme.com"

# From file
python anonymise.py --file input.txt --output anonymised.txt

# Show detected entities
python anonymise.py --show-entities "Call me at 020-7946-0958, my IBAN is GB29NWBK60161331926819."

Use in Python

from anonymise import load_model, detect_entities, anonymise

model = load_model(".")  # path to this repo

text = (
    "Dear John Smith, your appointment is on 2025-03-15. "
    "Your date of birth (15/03/1990) has been verified. "
    "Please contact support at help@acme.com or call 020-7946-0958. "
    "Your account IBAN is GB29NWBK60161331926819. Regards, Acme Corp."
)

entities = detect_entities(model, text)
print(anonymise(text, entities))

Output:

Dear [PERSON_NAME], your appointment is on [DATE_TIME].
Your date of birth ([DATE_OF_BIRTH]) has been verified.
Please contact support at [EMAIL] or call [PHONE].
Your account IBAN is [BANK_ACCOUNT_DETAILS]. Regards, Acme Corp.

Entity detection only

If you just need the raw entity offsets (e.g. for your own replacement logic):

entities = detect_entities(model, text)
for e in entities:
    print(f'{e["type"]:25s} [{e["start"]}:{e["end"]}] score={e["score"]:.2f}  "{text[e["start"]:e["end"]]}"')

PERSON_NAME               [5:15]  score=1.00  "John Smith"
DATE_TIME                 [40:50] score=1.00  "2025-03-15"
DATE_OF_BIRTH             [72:82] score=1.00  "15/03/1990"
EMAIL                     [129:142] score=1.00  "help@acme.com"
PHONE                     [151:164] score=1.00  "020-7946-0958"
BANK_ACCOUNT_DETAILS      [187:209] score=1.00  "GB29NWBK60161331926819"

Detect a subset of entities

entities = detect_entities(model, text, entities={
    "PERSON_NAME": "Person name",
    "EMAIL": "Email",
})

How It Works

The inference pipeline in anonymise.py:

1. Chunking — Long texts are split into 3000-character chunks with 100-char overlap to stay within the model's context window.
2. Batch prediction — Chunks are fed through GLiNER2.batch_extract_entities() with include_spans=True to get character-level offsets.
3. Date disambiguation — Both DATE_TIME and DATE_OF_BIRTH are always detected together so the model can choose the best label per span.
4. De-duplication — Overlapping detections from chunk boundaries are merged, keeping the highest-confidence label for each position.
5. Replacement — Detected spans are replaced right-to-left with [ENTITY_TYPE] placeholders.

Notes

• Confidence threshold: Default is 0.25. The model tends to be conservative, so a lower threshold works well for high recall.
• GLiNER2 version: Requires gliner2>=1.2.4. Earlier versions had a bug where entity character offsets mapped to token positions instead of character positions; this is fixed in 1.2.4+.
• Device: Automatically uses CUDA > MPS > CPU.

Acknowledgements

This model is a fine-tuned version of GLiNER2 Large by Fastino AI. We thank the GLiNER2 authors for making their model and library openly available.

Citation

If you use NERPA, please cite both this model and the original GLiNER2 paper:

@misc{nerpa2025,
  title={NERPA: Fine-Tuned GLiNER2 for PII Anonymisation},
  author={Akhat Rakishev},
  year={2025},
  url={https://huggingface.co/OvermindLab/nerpa},
}

@misc{zaratiana2025gliner2efficientmultitaskinformation,
  title={GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface},
  author={Urchade Zaratiana and Gil Pasternak and Oliver Boyd and George Hurn-Maloney and Ash Lewis},
  year={2025},
  eprint={2507.18546},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2507.18546},
}

Built by Akhat Rakishev at Overmind.

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