multilang-pii-ner (ONNX)
This is an ONNX version of Ar86Bat/multilang-pii-ner. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for token-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TokenClassificationPipeline
Multilingual PII NER
A multilingual transformer model (xlm-roberta-base) fine-tuned for Named Entity Recognition (NER) to detect and mask Personally Identifiable Information (PII) in text across English, German, Italian, and French.
Model Description
- Architecture: XLM-RoBERTa Base
- Task: Named Entity Recognition (NER) for PII detection and masking
- Languages: English, German, Italian, French
- Training Data: ai4privacy/open-pii-masking-500k-ai4privacy (CoNLL format)
- License: MIT
Intended Uses & Limitations
- Intended use: Detect and mask PII entities in multilingual text for privacy-preserving applications.
- Not suitable for: Use cases requiring perfect recall/precision on rare or ambiguous PII types without further fine-tuning.
How to Use
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
model_id = "Ar86Bat/multilang-pii-ner"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)
nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
text = "John Doe was born on 12/12/1990 and lives in Berlin."
results = nlp(text)
print(results)
Evaluation Results
- Overall accuracy: 99.24%
- Macro F1-score: 0.954
- Weighted F1-score: 0.992
Entity-level highlights
- High F1-scores (>0.97) for common entities:
AGE,BUILDINGNUM,CITY,DATE,EMAIL,GIVENNAME,STREET,TELEPHONENUM,TIME - Excellent performance on
EMAILandDATE(F1 ≈ 0.999) - Lower F1-scores for challenging/rare entities:
DRIVERLICENSENUM(F1 ≈ 0.85),GENDER(F1 ≈ 0.83),PASSPORTNUM(F1 ≈ 0.88),SURNAME(F1 ≈ 0.85),SEX(F1 ≈ 0.84)
Training & Validation
- Preprocessing, training, and validation scripts are available in the GitHub repository.
- All model artifacts and outputs are in the
model/directory. - Training hyperparameters:
num_train_epochs=2# Total number of training epochsper_device_train_batch_size=32# Batch size for trainingper_device_eval_batch_size=32# Batch size for evaluation
Citation
If you use this model, please cite the repository:
@misc{ar86bat_multilang_pii_ner_2025,
author = {Arif Hizlan},
title = {Multilingual PII NER},
year = {2025},
howpublished = {\\url{https://huggingface.co/Ar86Bat/multilang-pii-ner}}
}
GitHub Repository
https://github.com/Ar86Bat/multilang-pii-ner
License
MIT License