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LiquidAI / LFM2.5-Encoder-350M-PII-Detector
Model's Last Updated: July 28 2026
token-classification
Introduction of LFM2.5-Encoder-350M-PII-Detector
Model Details of LFM2.5-Encoder-350M-PII-Detector
LFM2.5-Encoder-350-PII-Detector
A full fine-tune of LFM2.5-Encoder-350M with a token-classification head, covering 40 PII types across 16 languages .
Find more details about our encoders in our blog post .
💻 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_birthlabeling 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 atrust_remote_codeencoder).
Install the required packages:
pip install torch transformers huggingface_hub
Run PII detection:
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 [email protected].", tok, model)
print(spans)
📬 Contact
- Got questions or want to connect? Join our Discord community
- If you are interested in custom solutions with edge deployment, please contact our sales team .
Citation
@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},
}
Runs of LiquidAI LFM2.5-Encoder-350M-PII-Detector on huggingface.co
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More Information About LFM2.5-Encoder-350M-PII-Detector huggingface.co Model
Url of LFM2.5-Encoder-350M-PII-Detector
LFM2.5-Encoder-350M-PII-Detector huggingface.co Url
Provider of LFM2.5-Encoder-350M-PII-Detector huggingface.co
Other API from LiquidAI
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