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Cataloged from fastino/gliner2-privacy-filter-PII-multi
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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 β including OpenAI Privacy Filter, NVIDIA GLiNER-PII, and urchade/gliner_multi_pii-v1.
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You can pass any subset of the 42 supported labels β the model conditions on the labels you provide at inference time.
Evaluated on the SPY benchmark (Savkin et al., 2025) with exact-match span-level metrics:
name entities, sometimes confusing common nouns, organisation names, and product names with personal names.For production use, consider:
person / full_name)Apache 2.0
pip install gliner2from gliner2 import GLiNER2
model = GLiNER2.from_pretrained("fastino/gliner2-privacy-filter-PII-multi")
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)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]."@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}}@misc{zaratiana2026gliner2piimultilingualmodelpersonally, title={GLiNER2-PII: A Multilingual Model for Personally Identifiable Information Extraction}, author={Urchade Zaratiana and Ash Lewis and George Hurn-Maloney}, year={2026}, eprint={2605.09973}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2605.09973}, }
@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}}