[!NOTE] 💻 Demos: Try this fine-tuned model running in a CPU-only Hugging Face space: PII detection — spot and remove 40 kinds of personal information across 16 languages.

Entity types (40 PII types across 11 domains)

Domain	Types
Identity	`identity.person_name`, `identity.ssn`, `identity.national_id`, `identity.passport`, `identity.drivers_license`, `identity.date_of_birth`, `identity.tax_id`
Contact	`contact.email`, `contact.phone`, `contact.address`, `contact.postal_code`, `contact.ip_address`
Financial	`financial.credit_card`, `financial.iban`, `financial.bank_account`, `financial.swift_bic`, `financial.crypto_wallet`, `financial.amount`
Credentials	`credential.api_key`, `credential.password`, `credential.private_key`, `credential.jwt`, `credential.connection_string`, `developer.login_credentials`
Online	`online.username`, `online.url`
Device	`device.mac_address`, `device.imei`, `developer.device_id`
Location	`location.gps_coordinates`
Healthcare	`healthcare.medical_record`, `healthcare.condition`, `healthcare.medication`, `healthcare.health_plan_id`
Organization	`org.company_name`
Special-category	`special.religion`, `special.political`, `special.orientation`, `special.health_status`
Legal	`legal.case_number`

Benchmarks (18-locale-filtered, partial-F1, hybrid decode)

Benchmark	this model	detection tier	SauerkrautLM GLiNER	openai/privacy-filter	Piiranha-v1	OpenMed privacy-filter	regex + validators
SPY	0.428	0.509	0.280	0.264	0.232	0.226	0.358
Gretel	0.880	0.885	0.663	0.458	0.553	0.770	0.337
TAB	0.867	0.888	0.685	0.543	0.262	0.672	0.000
ai4privacy	0.715	0.774	0.488	0.394	0.946	0.432	0.195
Nemotron	0.855	0.863	0.639	0.572	0.658	0.918	0.335
MAPA	0.236	0.267	0.416	0.288	0.228	0.164	0.000

Best on every benchmark except MAPA, whose idiosyncratic date-as-date_of_birth labeling convention penalises correctly-typed predictions. Only two external scores land higher anywhere — Piiranha-v1's 0.946 on ai4privacy and OpenMed's 0.918 on Nemotron — and both are in-distribution: each model trains on that exact corpus, as did our own encoder's pretraining on those same two.
Detection tier is the same model and the same predictions, scored with the type label ignored — did it find the PII span at all, which is the metric that matters for redaction. The gap to exact-type is the type-confusion rate, e.g. SPY 0.428 → 0.509.

Usage

⚠️ Loads custom code via trust_remote_code=True (the model wraps a trust_remote_code encoder).

Install the required packages:

pip install torch transformers huggingface_hub

Run PII detection:

python

import importlib.util
import sys

from huggingface_hub import hf_hub_download
from transformers import AutoModelForTokenClassification, AutoTokenizer

model_id = "LiquidAI/LFM2.5-Encoder-350-PII-Detector"

helper_path = hf_hub_download(model_id, "pii_hybrid_decode.py")
hf_hub_download(model_id, "context_cued.py")
sys.path.insert(0, helper_path.rsplit("/", 1)[0])

spec = importlib.util.spec_from_file_location("pii_hybrid_decode", helper_path)
hd = importlib.util.module_from_spec(spec)
spec.loader.exec_module(hd)

tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForTokenClassification.from_pretrained(model_id, trust_remote_code=True).eval()

spans = hd.predict("Email Dr. Laura Schmidt at laura@charite.de.", tok, model)
print(spans)

📬 Contact

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Citation

bibtex

@article{liquidAI2026Encoders,
  author = {Liquid AI},
  title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2-5-encoders},
}

LiquidAI/LFM2.5-Encoder-350M-PII-Detector

LFM2.5-Encoder-350-PII-Detector

Entity types (40 PII types across 11 domains)

Benchmarks (18-locale-filtered, partial-F1, hybrid decode)

Usage

📬 Contact

Citation

LiquidAI/
LFM2.5-Encoder-350M-PII-Detector