---
language:
- da
- de
- en
- es
- fr
- nl
license: apache-2.0
library_name: onnx
pipeline_tag: token-classification
tags:
- pii
- privacy
- ner
- coreference-resolution
- distilbert
- multi-task
- onnx
- quantized
- int8
base_model: DataikuNLP/kiji-pii-model
---

# Kiji PII Detection Model (ONNX Quantized)

INT8-quantized ONNX version of the Kiji PII detection model for efficient CPU inference. Detects Personally Identifiable Information (PII) in text with coreference resolution.

## Source Model

This is a quantized version of [DataikuNLP/kiji-pii-model](https://huggingface.co/DataikuNLP/kiji-pii-model) — a multi-task DistilBERT model fine-tuned for PII detection with coreference resolution.

## Model Summary

| | |
|---|---|
| **Format** | ONNX (INT8 quantized) |
| **Architecture** | Shared DistilBERT encoder + two classification heads |
| **Tasks** | PII token classification (53 labels) + coreference detection (7 labels) |
| **PII entity types** | 26 |
| **Max sequence length** | 512 tokens |
| **Runtime** | ONNX Runtime |

## Files

| File | Size |
|------|------|
| `model_quantized.onnx` | 63.3 MB |
| `model.onnx.data` | 248.9 MB |
| `ort_config.json` | 0.7 KB |
| `label_mappings.json` | 2.9 KB |
| `model_manifest.json` | 1.6 KB |
| `tokenizer_config.json` | 1.3 KB |
| `tokenizer.json` | 653.2 KB |
| `vocab.txt` | 208.4 KB |
| `special_tokens_map.json` | 0.7 KB |

## Quantization Details

| | |
|---|---|
| **Method** | Dynamic quantization (ONNX Runtime / Optimum) |
| **Weights** | QInt8 (symmetric, per-channel) |
| **Activations** | QUInt8 (asymmetric, per-tensor) |
| **Mode** | IntegerOps |
| **Format** | QOperator |
| **Operators quantized** | Conv, MatMul, Attention, LSTM, Gather, Transpose, EmbedLayerNormalization |

## Usage

```python
import numpy as np
from onnxruntime import InferenceSession
from transformers import AutoTokenizer

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DataikuNLP/kiji-pii-model-onnx")
session = InferenceSession("DataikuNLP/kiji-pii-model-onnx/model_quantized.onnx")  # or local path

# Tokenize
text = "Contact John Smith at john.smith@example.com or call +1-555-123-4567."
inputs = tokenizer(text, return_tensors="np", truncation=True, max_length=512)

# Run inference
outputs = session.run(None, dict(inputs))
pii_logits, coref_logits = outputs  # (1, seq_len, 53), (1, seq_len, 7)

# Decode PII predictions
pii_predictions = np.argmax(pii_logits, axis=-1)[0]

# See label_mappings.json for label ID -> label name mapping
```

## PII Labels (BIO tagging)

The model uses BIO tagging with 26 entity types:

| Label | Description |
|-------|-------------|
| `AGE` | Age |
| `BUILDINGNUM` | Building number |
| `CITY` | City |
| `COMPANYNAME` | Company name |
| `COUNTRY` | Country |
| `CREDITCARDNUMBER` | Credit Card Number |
| `DATEOFBIRTH` | Date of birth |
| `DRIVERLICENSENUM` | Driver's License Number |
| `EMAIL` | Email |
| `FIRSTNAME` | First name |
| `IBAN` | IBAN |
| `IDCARDNUM` | ID Card Number |
| `LICENSEPLATENUM` | License Plate Number |
| `NATIONALID` | National ID |
| `PASSPORTID` | Passport ID |
| `PASSWORD` | Password |
| `PHONENUMBER` | Phone number |
| `SECURITYTOKEN` | API Security Tokens |
| `SSN` | Social Security Number |
| `STATE` | State |
| `STREET` | Street |
| `SURNAME` | Last name |
| `TAXNUM` | Tax Number |
| `URL` | URL |
| `USERNAME` | Username |
| `ZIP` | Zip code |


Each entity type has `B-` (beginning) and `I-` (inside) variants, plus `O` for non-PII tokens.

## Coreference Labels

| Label | Description |
|-------|-------------|
| `NO_COREF` | Token is not part of a coreference cluster |
| `CLUSTER_0`-`CLUSTER_3` | Token belongs to coreference cluster 0-3 |

## Training Data

The source model was trained on the [DataikuNLP/kiji-pii-training-data](https://huggingface.co/datasets/DataikuNLP/kiji-pii-training-data) dataset — a synthetic multilingual PII dataset with entity annotations and coreference resolution.

## Lineage

| Stage | Repository |
|-------|------------|
| Dataset | [DataikuNLP/kiji-pii-training-data](https://huggingface.co/datasets/DataikuNLP/kiji-pii-training-data) |
| Trained model | [DataikuNLP/kiji-pii-model](https://huggingface.co/DataikuNLP/kiji-pii-model) |
| **Quantized model** | **DataikuNLP/kiji-pii-model-onnx** (this repo) |

## Limitations

- Trained on **synthetically generated** data — may not generalize to all real-world text
- Coreference head supports up to 4 clusters per sequence
- Optimized for the 6 languages in the training data (English, German, French, Spanish, Dutch, Danish)
- Max sequence length is 512 tokens
- Quantization may slightly reduce accuracy compared to the full-precision model