fastino/gliner2-privacy-filter-PII-multi

GLiNER2-PII: Multilingual PII Detection & Masking

GLiNER2-PII is a fine-tune of the GLiNER2 model (205M parameters) for detecting and masking personally identifiable information across 42 entity types and 7 languages.

Trained entirely on a constraint-driven synthetic corpus of 4,910 annotated texts, it achieves the highest span-level F1 (0.477) on the SPY benchmark among four compared systems — including OpenAI Privacy Filter, NVIDIA GLiNER-PII, and urchade/gliner_multi_pii-v1.

📄 Technical Report
🔗 GitHub

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Quick Start

pip install gliner2

from gliner2 import GLiNER2

model = GLiNER2.from_pretrained("fastino/gliner2-pii-v1")

text = "Email john.smith@acme.com or call +1 415 555 0199."
labels = ["email", "phone_number", "person"]

result = model.extract_entities(
    text,
    labels,
    threshold=0.5,
    include_confidence=True,
    include_spans=True,
)

print(result)

You can pass any subset of the 42 supported labels — the model conditions on the labels you provide at inference time.

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Supported PII Labels (42 types)

Group	Labels
Person / names	person, full_name, first_name, middle_name, last_name, date_of_birth
Contact / address	email, phone_number, address, street_address, city, state_or_region, postal_code, country
Government / tax IDs	government_id, national_id_number, passport_number, drivers_license_number, license_number, tax_id, tax_number
Banking / payment	bank_account, account_number, routing_number, iban, payment_card, card_number, card_expiry, card_cvv
Digital identity	username, ip_address, account_id, sensitive_account_id
Secrets / credentials	password, secret, api_key, access_token, recovery_code
Sensitive dates	sensitive_date, document_date, expiration_date, transaction_date

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Benchmark Results (SPY)

Evaluated on the SPY benchmark (Savkin et al., 2025) with exact-match span-level metrics:

Model	Legal P	Legal R	Legal F1	Medical P	Medical R	Medical F1	Avg F1
fastino/gliner2-pii-v1	.346	.750	.473	.369	.686	.480	.477
nvidia/gliner-PII	.343	.452	.390	.368	.465	.411	.400
urchade/gliner_multi_pii-v1	.467	.317	.377	.518	.351	.419	.398
openai/privacy-filter	.242	.656	.354	.287	.692	.406	.380

Key takeaways

• Highest F1 on both legal and medical domains.
• Best recall among GLiNER-based detectors (0.718 avg) — critical for redaction workflows where missed spans are data leaks.
• Consistent performance across domains (< 2-point F1 difference).

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When to Use This Model

Use case	Why GLiNER2-PII
PII redaction / masking	High recall minimises missed sensitive spans
Data governance & GDPR/CCPA compliance	42 fine-grained types enable policy-specific routing
Training-data hygiene	Exact character spans for precise masking before model training
Multi-language pipelines	Trained on EN, FR, ES, DE, IT, PT, NL formats

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Redaction Example

def redact(text, labels, threshold=0.5):
    model = GLiNER2.from_pretrained("fastino/gliner2-pii-v1")
    result = model.extract_entities(
        text, labels, threshold=threshold,
        include_spans=True,
    )
    entities = result.get("entities", {})
    spans = []
    for label, values in entities.items():
        for value in values:
            start = text.find(value)
            if start != -1:
                spans.append((start, start + len(value), label))

    spans.sort(key=lambda s: s[0], reverse=True)
    redacted = text
    for start, end, label in spans:
        redacted = redacted[:start] + f"[{label.upper()}]" + redacted[end:]
    return redacted


text = "Please contact Maria Jensen at maria.jensen@example.dk or +45 20 12 34 56."
labels = ["person", "email", "phone_number"]
print(redact(text, labels))
# "Please contact [PERSON] at [EMAIL] or [PHONE_NUMBER]."

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Training Details

Detail	Value
Base model	GLiNER2 (205M parameters)
Training data	4,910 synthetic annotated texts
PII mentions	129,951 total (mean 26.5 per example)
Generator	GPT-5.4 (temperature 0.01)
Data framework	Constraint-driven generation (same framework as Pioneer Agent)
Languages	English, French, Spanish, German, Italian, Portuguese, Dutch
Label types	42 PII entity types across 7 semantic groups

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Limitations

• Precision (0.35–0.37 on SPY) leaves room for improvement; the model tends to over-predict name entities, sometimes confusing common nouns, organisation names, and product names with personal names.
• Evaluated on a single benchmark (SPY) covering two domains. Broader multilingual and fine-grained evaluation is ongoing.
• Training data is fully synthetic and has not been validated by human annotators.
• Performance on non-European locales and scripts has not been measured.

Improving precision

For production use, consider:

• Per-label confidence thresholds (raise threshold for person / full_name)
• Dictionary-based filtering for common false positives
• Calibration on a small domain-specific development set

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Citation

@misc{fastino2026gliner2pii,
  title   = {GLiNER2-PII: Multilingual PII Extraction via Synthetic Fine-Tuning},
  author  = {{Fastino AI Team}},
  year    = {2026},
  url     = {https://huggingface.co/fastino/gliner2-pii-v1}
}

Related work

@inproceedings{zaratiana-etal-2025-gliner2,
  title     = {GLiNER2: Schema-Driven Multi-Task Learning for Structured Information Extraction},
  author    = {Zaratiana, Urchade and Pasternak, Gil and Boyd, Oliver and Hurn-Maloney, George and Lewis, Ash},
  booktitle = {Proceedings of EMNLP 2025: System Demonstrations},
  year      = {2025}
}

@inproceedings{zaratiana-etal-2024-gliner,
  title     = {GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
  author    = {Zaratiana, Urchade and Tomeh, Nadi and Holat, Pierre and Charnois, Thierry},
  booktitle = {Proceedings of NAACL 2024},
  year      = {2024}
}

@misc{atreja2026pioneeragent,
  title  = {Pioneer Agent: Continual Improvement of Small Language Models in Production},
  author = {Atreja, Dhruv and White, Julia and Nayak, Nikhil and Zhang, Kelton and Princis, Henrijs and Hurn-Maloney, George and Lewis, Ash and Zaratiana, Urchade},
  year   = {2026},
  url    = {https://arxiv.org/abs/2604.09791}
}

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License

Apache 2.0