Detects: PERSON (full name), PERSON_F (first name), PERSON_L (last name), STREET (street/road), CITY, ORG (company/institution).

House numbers are intentionally excluded from NER — handled downstream by regex in post-processing.

Intended use: Polish web forms — browser-side inference via @xenova/transformers + ONNX Runtime Web (WASM), no backend required.

Training data

Merged in data/merged/conll/ from two sources:

Split	Synthetic	KPWr	Total	Tokens
train	12 750	3 935	16 685	248 437
validation	1 500	437	1 937	29 384
test	750	750	1 500	25 547

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

Fine-tuned for 3 epochs with early stopping (patience=2), best checkpoint selected by eval F1 STREET.

Evaluation (`data/merged/conll/test.conll`, 1 500 sentences, seqeval)

Entity	fp32 F1	int8 F1	Δ fp32→int8
PERSON	97.7%	97.1%	−0.6pp
PERSON_F	100.0%	99.4%	−0.6pp
PERSON_L	100.0%	99.2%	−0.8pp
ORG	85.1%	82.7%	−2.5pp
STREET	98.9%	99.0%	+0.2pp
CITY	96.9%	96.7%	−0.2pp
overall	96.0%	95.4%	−0.6pp

INT8 quantization loss = −0.6pp overall (target was <3pp). No entity loses more than 2.5pp.

Label mapping

The model outputs 13 BIO classes:

Model label	Meaning
`B-PERSON` / `I-PERSON`	full person name
`B-PERSON_F` / `I-PERSON_F`	first name only
`B-PERSON_L` / `I-PERSON_L`	last name only
`B-ORG` / `I-ORG`	company / institution
`B-STREET` / `I-STREET`	street / road / avenue
`B-CITY` / `I-CITY`	city
`O`	not an entity

Files

File	Format	Notes
`model.onnx`	FP32	highest quality, BERT graph-optimized
`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
`tokenizer_config.json`	JSON	HerBERT tokenizer config

Usage

Python (Transformers)

python

from transformers import pipeline

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

Browser (@xenova/transformers + ONNX Runtime Web)

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

const ner = await pipeline(
    "token-classification",
    "ArkadiuszPawlak/fastpdn-ner-polish-pii",
    { dtype: "q8", aggregation_strategy: "simple" }
);

const raw = await ner("Jan Kowalski mieszka przy ul. Marszałkowskiej 1 w Warszawie.");
console.log(raw);
// [{ entity_group: "PERSON", word: "Jan Kowalski", score: 0.99 },
//  { entity_group: "STREET", word: "ul. Marszałkowskiej", score: 0.98 },
//  { entity_group: "CITY", word: "Warszawa", score: 0.97 }]

ArkadiuszPawlak/fastpdn-ner-polish-pii

FastPDN NER — Polish PII (ONNX)