bardsai/eu-pii-anonimization-multilang

license: apache-2.0 language:

• en
• pl
• de
• fr
• es
• it base_model:
• FacebookAI/xlm-roberta-base pipeline_tag: token-classification tags:
• pii-detection
• token-classification
• privacy
• sensitive-data
• multilingual
• bards-ai

eu-pii-anonimization-multilang

Multilingual PII and Sensitive Data Detection Model

bardsai/eu-pii-anonimization-multilang is a token classification model for detecting personally identifiable information (PII) and other regulated or high-sensitivity entities in multilingual text.

Built on top of XLM-RoBERTa-base, this model is intended for privacy-preserving NLP workflows, data redaction, secure preprocessing, and compliance-focused pipelines across multiple European languages.

Key Highlights

• Language support: Multilingual (EU-focused)
• Task: Token classification
• Base model: XLM-RoBERTa-base
• Entity schema: 36 sensitive-data classes (B-/I- labeling)

Intended Use

Typical use cases:

• PII redaction in documents, tickets, emails, and chat logs
• Dataset sanitization before training, analytics, or sharing
• Compliance and governance pipelines for sensitive data handling
• Pre-ingestion filtering for search, retrieval, and RAG systems

Detected Entity Types

The model predicts the following sensitive entity families:

• Personal identity and profile data
• Organization and institutional identifiers
• Contact details and location data
• Technical and digital identifiers
• Financial and commercial information
• Official document references
• Health, biometric, and genetic data
• Special-category personal data

Labels are defined in config.json (id2label and label2id).

Quick Start

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_name = "bardsai/eu-pii-anonimization-multilang"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

text = "John Smith, passport AB123456, phone +48 123 456 789"
inputs = tokenizer(text, return_tensors="pt", truncation=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)

tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
labels = [model.config.id2label[p.item()] for p in predictions[0]]

for token, label in zip(tokens, labels):
    if label != "O":
        print(label, token)

Repository Files

• model.safetensors - model weights
• config.json - model config and label mapping
• tokenizer.json, tokenizer_config.json - tokenizer assets
• training_args.bin - training metadata
• onnx/model.onnx - exported ONNX model
• onnx/model_quantized.onnx - INT8 quantized ONNX model

Limitations

• Performance can vary by language, domain, formatting quality, and OCR noise.
• Ambiguous phrases may require post-processing and human validation.
• The model should support compliance workflows, not replace legal decisions.

About bards.ai

At bards.ai, we build practical ML systems for NLP, vision, and time series.
More info: https://bards.ai