KPWr filtered (3 113 samples) — clarin-pl/kpwr-ner Polish press corpus; LOC-only (geographic) and schematic form-label samples removed.
LLM-synthetic (1 700 train samples) — generated with GPT-4o-mini and claude-haiku-4-5, covering ADDRESS with city suffix (w Mieście), PERSON in email context, and mixed cases.

Fine-tuned for 8 epochs with early stopping (patience=3), best checkpoint selected by eval F1 (seqeval, micro-averaged over B-PER/I-PER/B-LOC/I-LOC).

Evaluation (`kpwr_final_test.json`, 615 samples, character-level IoU >= 0.5)

Label	F1	Prec	Recall	TP	FP	FN
PERSON	0.922	0.885	0.962	425	55	17
ADDRESS	0.944	0.903	0.990	102	11	1

ADDRESS recall 0.990 — the model captures full addresses including city names.

Label mapping

The model outputs 5 BIO classes:

Model label	Meaning for this use case
`B-PER` / `I-PER`	PERSON
`B-LOC` / `I-LOC`	ADDRESS
`O`	not PII

Files

File	Format	Notes
`model.onnx`	FP32	highest quality
`model_quantized.onnx`	INT8	recommended for browser
`onnx/model_quantized.onnx`	INT8	alias for Transformers.js `dtype:"q8"`
`config.json`	JSON	label mapping, model config
`tokenizer.json`	JSON	HerBERT tokenizer

Usage

Python (Transformers)

python

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="ArkadiuszPawlak/pczarnik-herbert-ner-polish-pii",
    aggregation_strategy="simple",
)
result = ner("Jan Kowalski mieszka przy ul. Marszałkowskiej 1, 00-001 Warszawa.")
# [{"entity_group": "PER", "word": "Jan Kowalski", ...},
#  {"entity_group": "LOC", "word": "ul. Marszałkowskiej 1, 00-001 Warszawa", ...}]

Browser (@xenova/transformers + ONNX Runtime Web)

import { pipeline } from "@xenova/transformers";

const ner = await pipeline(
    "token-classification",
    "ArkadiuszPawlak/pczarnik-herbert-ner-polish-pii",
    { aggregation_strategy: "simple" }
);

const LABEL_MAP = { PER: "PERSON", LOC: "ADDRESS" };

const raw = await ner("Jan Kowalski mieszka przy ul. Marszałkowskiej 1 w Krakowie.");
const entities = raw
    .filter(e => e.entity_group in LABEL_MAP)
    .map(e => ({ label: LABEL_MAP[e.entity_group], text: e.word, score: e.score }));
console.log(entities);
// [{ label: "PERSON", text: "Jan Kowalski", score: 0.99 },
//  { label: "ADDRESS", text: "ul. Marszałkowskiej 1 w Krakowie", score: 0.97 }]

ArkadiuszPawlak/pczarnik-herbert-ner-polish-pii

HerBERT NER — Polish PII (ONNX)

Training data

Evaluation (kpwr_final_test.json, 615 samples, character-level IoU >= 0.5)