ohhsj/pii-ner-singapore

PII NER — Singapore

Fine-tuned BERT model for detecting Personally Identifiable Information (PII) in Singapore-context English text. Optimised for use with ONNX Runtime Web (browser inference via the Local Presidio extension).

Labels

Label	Description
PER	Person name
ID	NRIC / FIN / passport number
PHONE	Phone number
EMAIL	Email address
ADDR	Physical address
ORG	Organisation name
FIN	Financial info (bank account, credit card)
DOB	Date of birth
REG	Registration number (vehicle, company)
MISC	Other PII

Usage (Transformers.js)

import { pipeline } from '@xenova/transformers';
const ner = await pipeline('token-classification', 'ohhsj/pii-ner-singapore');
const result = await ner('John Tan NRIC S1234567D lives at 123 Orchard Road.');
console.log(result);

Usage (ONNX Runtime directly)

The onnx/model_quantized.onnx file can be loaded directly with ONNX Runtime Web for browser inference without the Transformers.js wrapper.

Training Details

Parameter	Value
Base model	dslim/bert-base-NER
Training data	Synthetic Singapore PII sentences
Sentences	2,500
Epochs	4
Batch size	32
Max length	256 tokens
GPU	NVIDIA A100 (RunPod serverless)
Quantisation	int8 dynamic (ONNX Runtime)

Evaluation (synthetic test set)

Entity	Precision	Recall	F1
ADDR	1.000	1.000	1.000
DOB	1.000	1.000	1.000
EMAIL	1.000	1.000	1.000
FIN	1.000	1.000	1.000
ID	1.000	1.000	1.000
ORG	1.000	1.000	1.000
PER	1.000	1.000	1.000
PHONE	1.000	1.000	1.000
REG	1.000	1.000	1.000
avg	1.000	1.000	1.000

Note: Evaluated on synthetic data generated by the same pipeline. Real-world performance will vary — use regex detectors for structured fields (NRIC, phone, email) and this model for unstructured text (names, addresses, organisations).

Files

onnx/model_quantized.onnx   104 MB   int8-quantised, for browser inference
label_map.json               mapping of label IDs to entity names
config.json                  model config (dslim/bert-base-NER base)
tokenizer.json               tokenizer (WordPiece, bert-base-cased vocab)
tokenizer_config.json
special_tokens_map.json
vocab.txt