Hybrid detection: the model handles person names and addresses, the SDK postprocessor handles structured patterns (phone numbers, ID numbers, dates, URLs). 136 MB, CPU-only.

Benchmark (benchmark_v2, 253 cases)

Compared against every public Korean PII model on HuggingFace (as of Aug 2026).

Model	Params	F1	P	R
OpenMed-PII-Korean-NomicMed-Large	395M	0.041	0.029	0.071
OpenMed-PII-Korean-QwenMed-XLarge	600M	0.084	0.061	0.138
LFM2.5-Encoder-350M-PII	350M	0.254	0.207	0.328
FrameByFrame/korean-pii-e5-base	~110M	0.435	0.319	0.684
seungkukim/korean-pii-masking-v2	~110M	0.553	0.672	0.470
vmaca123/korean-pii-ner-v3	~125M	0.569	0.810	0.439
schift-ko-pii-v4	111M	0.702	0.602	0.842
schift-ko-pii-v5	34M	0.823	0.812	0.834

All models evaluated raw (no postprocessing) except v4/v5 which use the SDK postprocessor (regex for structured patterns). v5 with postprocess disabled scores F1=0.578 (P=0.847, R=0.439) — still competitive, and the hybrid approach pushes it to #1.

Not included: bbanany/qwen25-3b-korean-pii-* (3B CausalLM, binary PII/NOT_PII classifier on pre-extracted spans — different task, not NER), upgle/bert-pii-korean and alphagyuu/* (no model weights uploaded), psh3333/* (gated), mncai/* (broken config).

v5 is 3x smaller than v4 and scores higher on the harder benchmark.

Raw model (no postprocess)

Model	Params	raw F1	raw P	raw R
LFM2.5-350M-PII	350M	0.254	0.207	0.328
schift-ko-pii-v4	111M	0.702	0.602	0.842
schift-ko-pii-v5	34M	0.578	0.847	0.439

v5 raw recall is low because the model only predicts private_person and private_address. All other categories (phone, email, dates, IDs) are handled by the regex postprocessor — by design, not by limitation.

Category breakdown (benchmark_v1, 93 cases)

Category	v5 (34M)	v4 (111M)	LFM2.5 (350M)
Person (standard)	1.00	1.00	0.67
Person (rare surnames)	1.00	1.00	0.60
Person (short names)	1.00	1.00	0.57
Address (urban)	1.00	1.00	0.67
Address (rural)	1.00	1.00	0.60
Phone (via postprocess)	0.86	0.86	0.92
Vehicle plate	1.00	1.00	0.00
Date (Korean)	1.00	1.00	0.00
Resident ID	1.00	1.00	0.75

Installation

pip install schift-ko-pii

Usage

1. Basic detection

python

from schift_ko_pii import detect

# Raw model output (person + address only)
spans = detect("피고 김민수의 전화번호는 010-1234-5678이다.")
for s in spans:
    print(f"  [{s['id']}] {s['label']}: {s['text']} (score: {s['score']})")
# [person_1] private_person: 김민수 (score: 0.99)

# With postprocessing — adds regex-based detection for phone, ID, dates, etc.
spans = detect("피고 김민수의 전화번호는 010-1234-5678이다.", postprocess=True)
# [person_1] private_person: 김민수
# [phone_1] phone_number: 010-1234-5678

2. Mask and review

python

from schift_ko_pii import mask

result = mask(
    "피의자 김철수(주민번호 850205-1234567)가 피해자 박영희에게 "
    "서울특별시 강남구 테헤란로 521에서 금 3,000만원을 전달하였다.",
    postprocess=True
)

print(result["masked"])
# 피의자 [사람1](주민번호 [주민번호1])가 피해자 [사람2]에게
# [주소1]에서 금 3,000만원을 전달하였다.

# Entity map — review what was detected
for e in result["entities"]:
    print(f"  {e['id']:>15}: {e['text']}")
#       person_1: 김철수
#  resident_id_1: 850205-1234567
#       person_2: 박영희
#      address_1: 서울특별시 강남구 테헤란로 521

3. Selective replacement

python

from schift_ko_pii import mask, apply

text = ("계약자 남궁혜진(010-9876-5432)의 거주지 "
        "부산광역시 해운대구 우동 1414번지 마린시티 102동 305호에서 "
        "참고인 Mike Johnson을 면담하였다.")

result = mask(text, postprocess=True)

# User reviews and decides what to redact
redacted = apply(text, result["entities"], {
    "person_1": "○○○",       # 남궁혜진 → ○○○
    "person_2": "외국인A",    # Mike Johnson → 외국인A
    "phone_1": "010-****-****",
    # address_1 intentionally left unmasked
})
print(redacted)
# 계약자 ○○○(010-****-****)의 거주지
# 부산광역시 해운대구 우동 1414번지 마린시티 102동 305호에서
# 참고인 외국인A을 면담하였다.

4. Bulk masking (replace all with ***)

python

from schift_ko_pii import mask, apply

text = "피해자 이도(여권 M12345678)는 서울 서초구 반포대로 58에 거주한다."
result = mask(text, postprocess=True)

# Replace everything with ***
clean = apply(text, result["entities"])
# 피해자 ***(여권 ***)는 ***에 거주한다.

5. Using transformers directly

python

from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline

model = AutoModelForTokenClassification.from_pretrained(
    "schift-io/schift-ko-pii-v5", trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("schift-io/schift-ko-pii-v5")

pipe = pipeline("token-classification", model=model, tokenizer=tokenizer,
                aggregation_strategy="simple")

results = pipe("참고인 제갈량의 증언을 채택한다.")
for r in results:
    print(f"  {r['entity_group']}: {r['word']} ({r['score']:.2f})")
# private_person: 제갈량 (0.95)

6. Processing files

python

from schift_ko_pii import mask, apply

with open("document.txt") as f:
    text = f.read()

result = mask(text, postprocess=True)

# Show what was found
print(f"Found {len(result['entities'])} PII entities:")
for e in result["entities"]:
    print(f"  {e['id']}: {e['text']}")

# Redact all and save
clean = apply(text, result["entities"])
with open("document_redacted.txt", "w") as f:
    f.write(clean)

API (free, no model download needed)

python

from schift import Schift

client = Schift(api_key="...")  # free at schift.io
result = client.pii.redact("김민수의 전화번호는 010-1234-5678입니다.")
# Postprocessing is always enabled on the API.

Labels

Label	Description	Detected by	Examples
`private_person`	Person names (Korean, Hanja, foreign)	Model	김민수, 남궁혜진, Mike Johnson, 田中太郎
`private_address`	Street/postal addresses	Model	서울특별시 강남구 테헤란로 521
`phone_number`	Phone numbers	Postprocess (regex)	010-1234-5678, 02-1234-5678
`resident_id`	Resident registration numbers	Postprocess (regex + checksum)	850205-1234567
`account_number`	Bank accounts, passport, vehicle plates, business IDs	Postprocess (regex)	M12345678, 52가1234
`private_date`	Dates in Korean format	Postprocess (regex)	2024년 3월 15일
`private_url`	URLs, IP addresses	Postprocess (regex)	192.168.1.1
`secret`	API keys, passwords	Postprocess (regex)

Architecture

Encoder: Conv + attention hybrid, 6 layers, hidden 512
Head: Linear classifier (512 → 33 labels)
Params: 34M
Format: safetensors float32 (136 MB)
Tokenizer: klue/roberta-base
Max length: 512 tokens
Tagging: BIES (Begin/Inside/End/Single)
Requires: trust_remote_code=True

Running benchmarks

python benchmark/run_benchmark.py --benchmark benchmark/benchmark_v2.jsonl --postprocess

License

Schift License v2.0 — Apache 2.0 base with revenue threshold. Free for everyone under $10M annual revenue. Research, education, and non-profit always permitted.

Citation

bibtex

@software{schift_ko_pii_v5_2026,
  author = {Schift Inc.},
  title = {schift-ko-pii-v5: 34M Korean PII Detection Model},
  year = {2026},
  url = {https://huggingface.co/schift-io/schift-ko-pii-v5},
}