GLiNER is a Named Entity Recognition (NER) model that uses a bidirectional transformer encoder (BERT-like) to identify any entity type. It offers a practical alternative to traditional NER models (limited to predefined entities) and Large Language Models (LLMs), which are flexible but resource-intensive.
This model is fine-tuned from E3-JSI/gliner-multi-pii-domains-v1 using the TAB dataset—English legal-domain text rich in privacy-sensitive entities.
GLiNER Multi PII Domains (v2) recognizes various personally identifiable information (PII) and legal entities in English legal, regulatory, and compliance text, including but not limited to:
1from gliner import GLiNER
23# Load the model4model = GLiNER.from_pretrained("aksman18/gliner-multi-pii-domains-v2")56text ="""
7On March 12, 2024, John Doe, a 45-year-old attorney at Smith & Wesson LLP,
8registered property ID PR-45678 in Los Angeles County under policy number INS-90210.
9The transaction was recorded under case ID 2024-CIV-789 and regulated by the Data Privacy Act 2020.
10"""1112labels =[13"person","datetime","dem_age","dem_occupation",14"org_commercial","id_property","id_policy","id_case",15"loc_geopolitical","id_legislation"16]1718entities = model.predict_entities(text, labels, threshold=0.4)1920for entity in entities:21print(entity["text"],"=>", entity["label"])
Expected output:
March 12, 2024 => datetime
John Doe => person
45-year-old => dem_age
attorney => dem_occupation
Smith & Wesson LLP => org_commercial
property ID PR-45678 => id_property
Los Angeles County => loc_geopolitical
policy number INS-90210 => id_policy
2024-CIV-789 => id_case
Data Privacy Act 2020 => id_legislation
Example: Extract Entities from a Contract Clause
python
1from gliner import GLiNER
23model = GLiNER.from_pretrained("aksman18/gliner-multi-pii-domains-v2")45text ="""
6This Agreement is entered into between AlphaTech Ltd., registered under Company Registration ID CR-56789,
7and Jane Smith, residing at 42 Baker Street, London. The contract shall be governed by the Data Protection Act 2018
8and subject to tax ID GB-9087654.
9"""1011labels =[12"org_commercial","person","loc_address",13"id_registration","id_legislation","id_tax"14]1516entities = model.predict_entities(text, labels, threshold=0.4)1718for entity in entities:19print(entity["text"],"=>", entity["label"])
Expected output:
AlphaTech Ltd. => org_commercial
CR-56789 => id_registration
Jane Smith => person
42 Baker Street, London => loc_address
Data Protection Act 2018 => id_legislation
GB-9087654 => id_tax
Model Details
Base model: E3-JSI/gliner-multi-pii-domains-v1
Fine-tuned dataset: TAB (legal-domain English)
Architecture: GLiNER Transformer Encoder
Task: Named Entity Recognition (PII and legal entities)
Language: English
Domain: Legal / Regulatory / Compliance
Max sequence length: 384 tokens
Recommended threshold: 0.3–0.5
Optimized for: Contracts, filings, legal documents, compliance records
Acknowledgements
This model builds on E3-JSI/gliner-multi-pii-domains-v1 and the TAB dataset for improved compliance and privacy precision. It leverages the GLiNER architecture for zero-shot, label-agnostic NER.
Special thanks to GLiNER maintainers and open-source dataset contributors advancing privacy-aware NLP research.
