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OpenMed/
OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1

Name: OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1
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OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1

Spanish PII Detection Model | 340M Parameters | Open Source

Model Description

OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1 is a transformer-based token classification model fine-tuned for Personally Identifiable Information (PII) detection in Spanish text. This model identifies and classifies 54 types of sensitive information including names, addresses, social security numbers, medical record numbers, and more.

Key Features

Spanish-Optimized: Specifically trained on Spanish text for optimal performance
High Accuracy: Achieves strong F1 scores across diverse PII categories
Comprehensive Coverage: Detects 55+ entity types spanning personal, financial, medical, and contact information
Privacy-Focused: Designed for de-identification and compliance with GDPR and other privacy regulations
Production-Ready: Optimized for real-world text processing pipelines

Performance

Evaluated on the Spanish subset of AI4Privacy dataset:

Metric	Score
Micro F1	0.8799
Precision	0.8768
Recall	0.8829
Macro F1	0.8957
Weighted F1	0.8791
Accuracy	0.9896

Top 10 Spanish PII Models

Rank	Model	F1	Precision	Recall
1	OpenMed-PII-Spanish-SnowflakeMed-Large-568M-v1	0.9495	0.9501	0.9490
2	OpenMed-PII-Spanish-SuperClinical-Large-434M-v1	0.9491	0.9515	0.9468
3	OpenMed-PII-Spanish-BigMed-Large-560M-v1	0.9436	0.9447	0.9426
4	OpenMed-PII-Spanish-EuroMed-210M-v1	0.9419	0.9443	0.9395
5	OpenMed-PII-Spanish-mClinicalE5-Large-560M-v1	0.9405	0.9362	0.9448
6	OpenMed-PII-Spanish-ClinicalBGE-568M-v1	0.9391	0.9348	0.9434
7	OpenMed-PII-Spanish-NomicMed-Large-395M-v1	0.9379	0.9418	0.9339
8	OpenMed-PII-Spanish-mSuperClinical-Base-279M-v1	0.9352	0.9312	0.9392
9	OpenMed-PII-Spanish-SuperMedical-Large-355M-v1	0.9346	0.9370	0.9323
10	OpenMed-PII-Spanish-SuperClinical-Base-184M-v1	0.9256	0.9208	0.9303

Supported Entity Types

This model detects 54 PII entity types organized into categories:

Identifiers (22 types)

Entity	Description
`ACCOUNTNAME`	Accountname
`BANKACCOUNT`	Bankaccount
`BIC`	Bic
`BITCOINADDRESS`	Bitcoinaddress
`CREDITCARD`	Creditcard
`CREDITCARDISSUER`	Creditcardissuer
`CVV`	Cvv
`ETHEREUMADDRESS`	Ethereumaddress
`IBAN`	Iban
`IMEI`	Imei
...	and 12 more

Personal Info (11 types)

Entity	Description
`AGE`	Age
`DATEOFBIRTH`	Dateofbirth
`EYECOLOR`	Eyecolor
`FIRSTNAME`	Firstname
`GENDER`	Gender
`HEIGHT`	Height
`LASTNAME`	Lastname
`MIDDLENAME`	Middlename
`OCCUPATION`	Occupation
`PREFIX`	Prefix
...	and 1 more

Contact Info (2 types)

Entity	Description
`EMAIL`	Email
`PHONE`	Phone

Location (9 types)

Entity	Description
`BUILDINGNUMBER`	Buildingnumber
`CITY`	City
`COUNTY`	County
`GPSCOORDINATES`	Gpscoordinates
`ORDINALDIRECTION`	Ordinaldirection
`SECONDARYADDRESS`	Secondaryaddress
`STATE`	State
`STREET`	Street
`ZIPCODE`	Zipcode

Organization (3 types)

Entity	Description
`JOBDEPARTMENT`	Jobdepartment
`JOBTITLE`	Jobtitle
`ORGANIZATION`	Organization

Financial (5 types)

Entity	Description
`AMOUNT`	Amount
`CURRENCY`	Currency
`CURRENCYCODE`	Currencycode
`CURRENCYNAME`	Currencyname
`CURRENCYSYMBOL`	Currencysymbol

Temporal (2 types)

Entity	Description
`DATE`	Date
`TIME`	Time

Usage

Quick Start

python

from transformers import pipeline

# Load the PII detection pipeline
ner = pipeline("ner", model="OpenMed/OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1", aggregation_strategy="simple")

text = """
Paciente María López (nacida el 15/03/1985, DNI: 87654321B) fue atendida hoy.
Contacto: maria.lopez@email.es, Teléfono: +34 612 345 678.
Dirección: Calle Serrano 42, 28001 Madrid.
"""

entities = ner(text)
for entity in entities:
    print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.3f})")

Important — Accent Handling: This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text.

python
1import unicodedata 2 3def strip_accents(text: str) -> str: 4 nfc = unicodedata.normalize("NFC", text) 5 nfd = unicodedata.normalize("NFD", nfc) 6 stripped = "".join(ch for ch in nfd if unicodedata.category(ch) != "Mn") 7 return unicodedata.normalize("NFC", stripped) 8 9text = strip_accents(text) # call before passing to the pipeline 10entities = ner(text)

De-identification Example

python

def redact_pii(text, entities, placeholder='[REDACTED]'):
    """Replace detected PII with placeholders."""
    # Sort entities by start position (descending) to preserve offsets
    sorted_entities = sorted(entities, key=lambda x: x['start'], reverse=True)
    redacted = text
    for ent in sorted_entities:
        redacted = redacted[:ent['start']] + f"[{ent['entity_group']}]" + redacted[ent['end']:]
    return redacted

# Apply de-identification
redacted_text = redact_pii(text, entities)
print(redacted_text)

Batch Processing

python

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

model_name = "OpenMed/OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

texts = [
    "Paciente María López (nacida el 15/03/1985, DNI: 87654321B) fue atendida hoy.",
    "Contacto: maria.lopez@email.es, Teléfono: +34 612 345 678.",
]

inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)

Training Details

Dataset

Source: AI4Privacy PII Masking 400k (Spanish subset)
Format: BIO-tagged token classification
Labels: 109 total (54 entity types × 2 BIO tags + O)

Training Configuration

Max Sequence Length: 512 tokens
Epochs: 3
Framework: Hugging Face Transformers + Trainer API

Intended Use & Limitations

Intended Use

De-identification: Automated redaction of PII in Spanish clinical notes, medical records, and documents
Compliance: Supporting GDPR, and other privacy regulation compliance
Data Preprocessing: Preparing datasets for research by removing sensitive information
Audit Support: Identifying PII in document collections

Limitations

Important: This model is intended as an assistive tool, not a replacement for human review.

False Negatives: Some PII may not be detected; always verify critical applications
Context Sensitivity: Performance may vary with domain-specific terminology
Language: Optimized for Spanish text; may not perform well on other languages

Citation

bibtex

@misc{openmed-pii-2026,
  title = {OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1: Spanish PII Detection Model},
  author = {OpenMed Science},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/OpenMed/OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1}
}

OpenMed/OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1

OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1

Model Description

Key Features

Performance

Top 10 Spanish PII Models

Supported Entity Types

Usage

Quick Start

De-identification Example

Batch Processing

Training Details

Dataset

Training Configuration

Intended Use & Limitations

Intended Use

Limitations

Citation

Links

OpenMed/
OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1