barflyman/multilang-pii-ner-ONNX

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

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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 EMAIL and DATE (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 epochs
  • per_device_train_batch_size=32 # Batch size for training
  • per_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