---
title: multilang-pii-ner-ONNX
canonical_url: "https://www.modelscope.cn/models/onnx-community/multilang-pii-ner-ONNX"
md_url: "https://www.modelscope.cn/models/onnx-community/multilang-pii-ner-ONNX.md"
repository: onnx-community/multilang-pii-ner-ONNX
last_updated: 2025-12-21
license: mit
pipeline_tag: token-classification
tasks:
  - token-classification
model_type:
  - xlm-roberta
architectures:
  - XLMRobertaForTokenClassification
base_model:
  - Ar86Bat/multilang-pii-ner
base_model_relation: finetune
library_name:
  - transformer
  - onnx
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - de
  - fr
  - it
downloads: 50
stars: 0
tags:
  - pii
  - deidentification
  - sensitive
  - multilingual
---

# multilang-pii-ner-ONNX

> multilang-pii-ner-ONNX - onnx-community 在 ModelScope 开源的模型。multilang-pii-ner (ONNX)

onnx-community/multilang-pii-ner-ONNX 是 ModelScope 魔搭社区上的token-classification模型，采用 mit 许可，基于 Ar86Bat/multilang-pii-ner 构建。

- **Repository**: onnx-community/multilang-pii-ner-ONNX
- **License**: mit
- **Tasks**: token-classification
- **Base model**: Ar86Bat/multilang-pii-ner
- **Tags**: pii, deidentification, sensitive, multilingual
- **Downloads**: 50
- **Stars**: 0
- **Last updated**: 2025-12-21

Source: https://www.modelscope.cn/models/onnx-community/multilang-pii-ner-ONNX

---

# multilang-pii-ner (ONNX)


This is an ONNX version of [Ar86Bat/multilang-pii-ner](https://huggingface.co/Ar86Bat/multilang-pii-ner). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).


## 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](https://huggingface.co/datasets/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

```python
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](https://github.com/Ar86Bat/multilang-pii-ner).
- 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](https://github.com/Ar86Bat/multilang-pii-ner)

## License

MIT License