README.md

metadata

language:
  - es
license: apache-2.0
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
  - token-classification
  - ner
  - pii
  - pii-detection
  - de-identification
  - privacy
  - healthcare
  - medical
  - clinical
  - phi
  - spanish
  - pytorch
  - transformers
  - openmed
pipeline_tag: token-classification
library_name: transformers
metrics:
  - f1
  - precision
  - recall
model-index:
  - name: OpenMed-PII-Spanish-BioClinicalBERT-110M-v1
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: AI4Privacy (Spanish subset)
          type: ai4privacy/pii-masking-400k
          split: test
        metrics:
          - type: f1
            value: 0.8411
            name: F1 (micro)
          - type: precision
            value: 0.8342
            name: Precision
          - type: recall
            value: 0.8481
            name: Recall
widget:
  - text: >-
      Dr. Carlos García (DNI: 12345678A) puede ser contactado en
      carlos.garcia@hospital.es o al +34 612 345 678. Vive en Calle Gran Vía 25,
      28013 Madrid.
    example_title: Clinical Note with PII (Spanish)

OpenMed-PII-Spanish-BioClinicalBERT-110M-v1

Spanish PII Detection Model | 110M Parameters | Open Source

Model Description

OpenMed-PII-Spanish-BioClinicalBERT-110M-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	0.8411
Precision	0.8342
Recall	0.8481
Macro F1	0.8602
Weighted F1	0.8387
Accuracy	0.9863

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

from transformers import pipeline

# Load the PII detection pipeline
ner = pipeline("ner", model="OpenMed/OpenMed-PII-Spanish-BioClinicalBERT-110M-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.

import unicodedata

def strip_accents(text: str) -> str:
    nfc = unicodedata.normalize("NFC", text)
    nfd = unicodedata.normalize("NFD", nfc)
    stripped = "".join(ch for ch in nfd if unicodedata.category(ch) != "Mn")
    return unicodedata.normalize("NFC", stripped)

text = strip_accents(text)  # call before passing to the pipeline
entities = ner(text)

De-identification Example

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

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

model_name = "OpenMed/OpenMed-PII-Spanish-BioClinicalBERT-110M-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

@misc{openmed-pii-2026,
  title = {OpenMed-PII-Spanish-BioClinicalBERT-110M-v1: Spanish PII Detection Model},
  author = {OpenMed Science},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/OpenMed/OpenMed-PII-Spanish-BioClinicalBERT-110M-v1}
}

Links

• Organization: OpenMed