| --- |
| language: |
| - en |
| - de |
| - fr |
| - es |
| - pt |
| - it |
| - pl |
| - ru |
| - zh |
| - ja |
| - ko |
| - ar |
| - hi |
| - id |
| - vi |
| - th |
| tags: |
| - liquid |
| - lfm2 |
| - lfm2.5 |
| - bidirectional |
| - masked-lm |
| - encoder |
| - pii |
| - ner |
| - privacy |
| - multilingual |
| - token-classification |
| library_name: transformers |
| license: other |
| license_name: lfm1.0 |
| license_link: LICENSE |
| pipeline_tag: token-classification |
| base_model: |
| - LiquidAI/LFM2.5-Encoder-350M |
| --- |
| |
| <div align="center"> |
| |
| # LFM2.5-Encoder-350-PII-Detector |
|
|
| A full fine-tune of [LFM2.5-Encoder-350M](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M) with a token-classification head, covering **40 PII types** 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: |
|
|
| ```bash |
| 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) |
| ``` |
|
|
