Nerone: Italian NER for Sensitive Data
Named Entity Recognition model for extracting and classifying sensitive personal information from Italian documents.
Model Description
Fine-tuned BureauBERTo (Italian BERT variant) for token classification across 101 entity types grouped into 13 categories. BIO tagging is used, for a total of 152 token-level labels (including O).
Entity Types
Personal (9)
| Entity |
Description |
| PERSON |
Full name of an individual |
| AGE |
A person's age |
| GENDER |
Gender / sex |
| MARITAL_STATUS |
Marital status (e.g., coniugato, celibe) |
| PROFESSION |
Job title or occupation |
| BLOOD_TYPE |
Blood group (e.g., A+, 0-) |
| FISCAL_CODE |
Italian personal tax code (codice fiscale) |
| ID_CARD_NUMBER |
Identity card number |
| HEALTH_CARD_NUMBER |
National health card number (tessera sanitaria) |
Geographic (9)
| Entity |
Description |
| ADDRESS |
Full street / postal address |
| COUNTRY |
Country name |
| REGION |
Administrative region |
| PROVINCE |
Province name or two-letter code (e.g., RM) |
| MUNICIPALITY |
City, town or comune |
| ZIP_CODE |
Postal code (CAP) |
| LATITUDE |
Geographic latitude |
| LONGITUDE |
Geographic longitude |
| ALTITUDE |
Elevation above sea level |
Contact (3)
| Entity |
Description |
| PHONE |
Telephone or mobile number |
| EMAIL |
Email address |
| URL |
Web address |
Social (2)
| Entity |
Description |
| HASHTAG |
Social media hashtag (#...) |
| MENTION |
Social media mention (@...) |
Financial (15)
| Entity |
Description |
| MONEY_AMOUNT |
Monetary amount |
| PERCENTAGE |
Percentage value |
| CARD_NUMBER |
Payment card number |
| CVV |
Card verification code |
| CHECK_NUMBER |
Bank cheque number |
| ACCOUNT_NUMBER |
Bank account number |
| IBAN |
International Bank Account Number |
| BIC |
Bank Identifier Code (SWIFT) |
| VAT_NUMBER |
VAT registration number (partita IVA) |
| TAX_TYPE |
Type of tax or levy (e.g., IMU, IRPEF) |
| TAX_CODE |
Tax payment code (codice tributo, model F24) |
| ABI_CODE |
Italian bank identifier (ABI), 5 digits |
| CAB_CODE |
Italian bank branch identifier (CAB), 5 digits |
| ISIN_CODE |
International Securities Identification Number |
| LEI_CODE |
Legal Entity Identifier |
Medical (7)
| Entity |
Description |
| DISEASE |
Disease or medical condition |
| MEDICINE |
Drug / medication name |
| DOSAGE |
Drug dosage (e.g., 1000mg) |
| FORM |
Pharmaceutical form (e.g., compressa, sciroppo) |
| MEDICAL_RECORD |
Medical record / chart identifier |
| DRG_CODE |
Diagnosis-Related Group code |
| HEALTH_DISTRICT_CODE |
Local health authority / district code (ASL) |
Legal / Administrative (8)
| Entity |
Description |
| PASSPORT |
Passport number |
| DRIVER_LICENSE |
Driving licence number |
| LICENSE_PLATE |
Vehicle registration plate |
| LAW |
Reference to a law, decree or regulation |
| COURT |
Court or tribunal name |
| ACT_NUMBER |
Administrative act / deed number |
| PROTOCOL_NUMBER |
Document protocol number |
| PROPERTY_REGIME |
Matrimonial property regime |
Cadastral (4)
| Entity |
Description |
| CADASTRAL_SHEET |
Cadastral sheet (foglio) |
| CADASTRAL_PARCEL |
Cadastral parcel (particella) |
| CADASTRAL_MAP |
Cadastral map reference |
| CADASTRAL_SUB |
Cadastral subordinate (subalterno) |
Technical (8)
| Entity |
Description |
| IP |
IP address |
| IMEI |
Mobile device IMEI |
| MAC |
MAC (hardware) address |
| UUID |
Universally unique identifier |
| VIN |
Vehicle identification number |
| OTP_CODE |
One-time password / code |
| PIN |
Personal identification number |
| BARCODE |
Product barcode (EAN/UPC) |
Codes & Standards (17)
| Entity |
Description |
| ISBN |
Book identifier |
| CIG_CODE |
Tender identifier (Codice Identificativo Gara) |
| CUP_CODE |
Public investment project code (Codice Unico di Progetto) |
| REA_CODE |
Business registry number (Repertorio Economico Amministrativo) |
| SDI_CODE |
E-invoicing recipient code (Sistema di Interscambio) |
| ATC_CODE |
Anatomical Therapeutic Chemical drug code |
| ATECO_CODE |
Italian economic activity classification code |
| ICD_CODE |
International Classification of Diseases code |
| CPV_CODE |
Common Procurement Vocabulary code (EU procurement) |
| NUTS_CODE |
EU territorial unit classification code |
| ISTAT_CODE |
ISTAT territorial / municipality code |
| ISO |
ISO standard reference (e.g., ISO 27001) |
| IEC |
IEC/CEI technical standard reference (e.g., IEC 60950) |
| LOT_NUMBER |
Batch / lot number |
| FLIGHT_NUMBER |
Flight number |
| POD_CODE |
Electricity supply point code (Point of Delivery) |
| PDR_CODE |
Gas delivery point code (Punto di Riconsegna) |
Measurements (11)
| Entity |
Description |
| AREA |
Surface area (e.g., m², ettari) |
| DISTANCE |
Length / distance (e.g., km, m) |
| ENERGY |
Energy quantity (e.g., kWh) |
| FILE_SIZE |
Digital file size (e.g., MB, GB) |
| POWER |
Power (e.g., kW, CV) |
| PRESSURE |
Pressure (e.g., bar, Pa) |
| QUANTITY |
Counted quantity with unit (e.g., 20 pezzi) |
| SPEED |
Speed (e.g., km/h) |
| TEMPERATURE |
Temperature (e.g., °C) |
| VOLUME |
Volume (e.g., L, m³) |
| WEIGHT |
Weight / mass (e.g., kg, g) |
Temporal (7)
| Entity |
Description |
| DATE |
Calendar date |
| DATE_RANGE |
Range between two dates |
| TIME |
Time of day |
| TIME_RANGE |
Range between two times |
| YEAR |
Year |
| DURATION |
Length of time (e.g., 5 giorni) |
| FREQUENCY |
Recurrence frequency (e.g., due volte al giorno) |
Misc (1)
| Entity |
Description |
| ORGANIZATION |
Company, institution or public body |
Dataset
- Total samples: 530,075
- Split: 70% train (371,053) / 15% validation (79,511) / 15% test (79,511)
- Source: Italian administrative documents
- Class weights computed to compensate for label imbalance.
Training
- Base model: colinglab/BureauBERTo
- Learning rate: 4e-5
- Batch size: 32
- Max sequence length: 256
Evaluation Results
Test set (entity-level, micro avg):
| Metric |
Score |
| F1 |
0.932 |
| Precision |
0.914 |
| Recall |
0.950 |
Usage
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
model = AutoModelForTokenClassification.from_pretrained("lcs06/nerone")
tokenizer = AutoTokenizer.from_pretrained("lcs06/nerone")
ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
def extract(text):
for e in ner(text):
span = text[e["start"]:e["end"]].strip(" ,.;:")
print(f"{e['entity_group']:<16} {span}")
Example 1 — Anagraphic document
text = (
"La sottoscritta Giulia Verdi, nata a Torino il 22/07/1990, "
"residente in Corso Vittorio Emanuele 18, 10123 Torino (TO), "
"codice fiscale VRDGLI90L62L219K, email giulia.verdi@example.it."
)
extract(text)
Output:
[
{"entity_group": "PERSON", "score": 1.0, "word": "Giulia Verdi", "start": 16, "end": 28},
{"entity_group": "MUNICIPALITY", "score": 1.0, "word": "Torino", "start": 37, "end": 43},
{"entity_group": "DATE", "score": 1.0, "word": "22/07/1990", "start": 47, "end": 57},
{"entity_group": "ADDRESS", "score": 1.0, "word": "Corso Vittorio Emanuele 18, 10123 Torino (TO)", "start": 72, "end": 117},
{"entity_group": "FISCAL_CODE", "score": 1.0, "word": "VRDGLI90L62L219K", "start": 134, "end": 150},
{"entity_group": "EMAIL", "score": 1.0, "word": "giulia.verdi@example.it", "start": 158, "end": 181}
]
Example 2 — Medical report
text = (
"Paziente: Anna Conti, 45 anni. Diagnosi: bronchite acuta. "
"Terapia: Amoxicillina 1 g ogni 8 ore per 7 giorni."
)
extract(text)
Output:
[
{"entity_group": "PERSON", "score": 1.0, "word": "Anna Conti", "start": 10, "end": 20},
{"entity_group": "AGE", "score": 1.0, "word": "45 anni", "start": 22, "end": 29},
{"entity_group": "DISEASE", "score": 1.0, "word": "bronchite acuta", "start": 41, "end": 56},
{"entity_group": "MEDICINE", "score": 1.0, "word": "Amoxicillina", "start": 66, "end": 78},
{"entity_group": "FREQUENCY", "score": 1.0, "word": "ogni 8 ore", "start": 83, "end": 93},
{"entity_group": "DURATION", "score": 0.93, "word": "7 giorni", "start": 98, "end": 106}
]
Intended Use
Designed for processing Italian administrative and legal documents to identify and classify sensitive personal data. Primary use cases:
- Document anonymization
- GDPR compliance
- Data extraction from public administration documents
Limitations
- Optimized for formal Italian text (administrative, legal, medical documents)
- Performance may degrade on informal text, dialects, or non-standard formatting
- Lower-frequency or harder entity types (e.g. DOSAGE, MEDICAL_RECORD, FREQUENCY) show weaker scores than high-volume types
Acknowledgements
This model is fine-tuned from BureauBERTo, developed by CoLingLab at the University of Pisa. BureauBERTo adapts UmBERTo to Italian bureaucratic and administrative language.
@inproceedings{auriemma2023bureauberto,
title = {{BureauBERTo}: adapting {UmBERTo} to the {Italian} bureaucratic language},
author = {Auriemma, Serena and Madeddu, Mauro and Miliani, Martina and Bondielli, Alessandro and Passaro, Lucia C and Lenci, Alessandro},
booktitle = {Proceedings of the Italia Intelligenza Artificiale - Thematic Workshops (Ital IA 2023)},
series = {CEUR Workshop Proceedings},
volume = {3486},
pages = {240--248},
publisher = {CEUR-WS.org},
year = {2023},
url = {https://ceur-ws.org/Vol-3486/42.pdf}
}
Framework Versions
- Transformers: 4.57.6
- PyTorch: 2.11.0
- Python: 3.13
License
Apache 2.0
