MaskPipe

MaskPipe is a spaCy-native toolkit for detecting, refining, resolving, and redacting PII.

Use it when you want one of these workflows:

• detect PII with built-in and custom rules, then redact it
• take entities from another NER system, run overlap resolution, then redact them
• combine both approaches in one spaCy pipeline

Where maskpipe fits

Maskpipe is a refinement and masking layer. It does not replace your NER model — it makes the output of any NER model production-ready: scoring, context boosting, conflict resolution, validation, and redaction, all in a composable spaCy pipeline.

Bring your own detection source:

Source	Use case
GLiNER	Zero-shot multilingual PII detection across 100+ languages; define entity types on the fly without retraining — covers 40+ PII categories including SSN, passport, IBAN, credit card
GLiNER2	Multi-task PII detection with text classification and structured extraction in one model
HuggingFace NER	Fine-tuned transformer models (BERT, DeBERTa) for high-accuracy PII on standard categories; strongest choice when you have labeled data or need domain-specific models (legal, code, multilingual)
OpenMed	Clinical NER and HIPAA-aware de-identification for healthcare; detects clinical entities (diseases, medications, anatomy) alongside patient PII in 13 languages
Built-in rules	Rule-based detection for 13+ countries without an external model; high-precision validators for country-specific IDs (SSN, passport, national ID, VAT)

Each source produces character-offset spans with a label and score. DocBuilder normalizes these into spaCy docs. The maskpipe pipeline then applies context-aware scoring, resolves overlapping spans, validates matches, and writes doc._.masked.

What MaskPipe Does

MaskPipe gives you four composable pipeline components:

• recognizer: finds spans from token patterns, phrase patterns, and custom matchers
• context_enhancer: boosts scores or relabels spans from nearby context
• conflict_resolver: resolves overlap and filters low-confidence spans
• anonymizer: writes masked output to doc._.masked

The original doc.text is never modified.

Installation

pip install maskpipe
python -m spacy download nl_core_news_sm

Requirements:

• Python 3.11-3.14
• spaCy 3.8+

Optional dependencies for examples and integrations:

pip install faker gliner transformers

Quick Start: Built-in Detection + Masking

This is the default workflow if you want MaskPipe to detect PII itself.

import spacy
from maskpipe import PipelineBuilder
from maskpipe import entities
from maskpipe.entities import nl

nlp = spacy.load("nl_core_news_sm", disable=["ner"])

builder = PipelineBuilder(nlp)
builder.add_entities([
    nl.BSN.replace(redactor="[BSN]"),
    entities.PHONE_NUMBER.replace(redactor="[PHONE_NUMBER]"),
    entities.EMAIL.replace(redactor="[EMAIL]"),
])

nlp = builder.build()

doc = nlp("Mijn BSN is 692015644, bel me op 0612345678 of mail naar info@example.com")

print(doc.text)
print(doc._.masked)
for ent in doc.ents:
    print(ent.text, ent.label_, ent._.score, ent._.replacement)
# Mijn BSN is 692015644, bel me op 0612345678 of mail naar info@example.com
# Mijn BSN is [BSN], bel me op [PHONE_NUMBER] of mail naar [EMAIL]
# 692015644 BSN 0.85 [BSN]
# 0612345678 PHONE_NUMBER 0.75 [PHONE_NUMBER]
# info@example.com EMAIL 1.0 [EMAIL]

Output model:

• doc.text: original text
• doc._.masked: masked text
• doc.ents: resolved spans after conflict resolution
• span._.replacement: replacement chosen by the anonymizer

If no redactor is registered for a label, MaskPipe uses [LABEL].

Quick Start: External NER + Masking

This is the right setup if another model already produced entity offsets.

import spacy
from transformers import pipeline
from maskpipe import PipelineBuilder, DocBuilder, HF_NER_MAPPER

# Load your NER model
ner = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")

# Set up MaskPipe to only resolve overlaps and mask (no local detection).
nlp = spacy.load("nl_core_news_sm", disable=["ner"])
builder = PipelineBuilder(nlp, disable=["recognizer", "context_enhancer"])
nlp = builder.build()

text = "Alice works at Google. Contact her at alice@example.com or 555-1234."
results = ner(text)

# results is a list of dicts like:
# [{"word": "Alice", "start": 0, "end": 5, "entity_group": "B-PER", "score": 0.98}, ...]

doc = DocBuilder(nlp, text).with_entities(results, entity_mapper=HF_NER_MAPPER).build()
doc = nlp(doc)
print(doc._.masked)
# [PERSON] works at [ORG]. Contact her at [EMAIL] or [PHONE_NUMBER].

Why this works:

• HF_NER_MAPPER normalizes HuggingFace NER output to the canonical entity format.
• with_entities() converts character offsets to spaCy spans with scores.
• conflict_resolver deduplicates overlapping spans and writes clean results to doc.ents.
• anonymizer reads doc.ents and generates doc._.masked.

Built-in Entities

Generic entities in maskpipe.entities:

Entity	Description
CREDIT_CARD	Credit card numbers (Luhn-validated)
CRYPTO	Bitcoin and Ethereum wallet addresses
DATE	Date expressions
EMAIL	Email addresses
IBAN	International bank account numbers
IPV4 / IPV6	IP addresses
MAC_ADDRESS	MAC / hardware addresses
NUMBER	Generic numeric values
PHONE_NUMBER	Phone numbers (international)
URL	Web URLs

Country-specific entities:

Package	Entities
maskpipe.entities.australia	AU_ABN, AU_ACN, AU_MEDICARE, AU_TFN
maskpipe.entities.finland	FI_PERSONAL_IDENTITY_CODE
maskpipe.entities.india	IN_AADHAAR, IN_GSTIN, IN_PAN, IN_PASSPORT, IN_VEHICLE_REGISTRATION, IN_VOTER
maskpipe.entities.italy	IT_DRIVER_LICENSE, IT_FISCAL_CODE, IT_IDENTITY_CARD, IT_PASSPORT, IT_VAT_CODE
maskpipe.entities.korea	KR_BRN, KR_DRIVER_LICENSE, KR_FRN, KR_PASSPORT, KR_RRN
maskpipe.entities.nigeria	NG_NIN, NG_VEHICLE_REGISTRATION
maskpipe.entities.nl	NL_BSN
maskpipe.entities.poland	PL_PESEL
maskpipe.entities.singapore	SG_FIN, SG_UEN
maskpipe.entities.spain	ES_NIE, ES_NIF
maskpipe.entities.thai	TH_TNIN
maskpipe.entities.uk	UK_NHS, UK_NINO, UK_PASSPORT, UK_POSTCODE, UK_VEHICLE_REGISTRATION
maskpipe.entities.us	ABA_ROUTING, BANK_ACCOUNT, MEDICAL_LICENSE, US_DRIVER_LICENSE, US_ITIN, US_MBI, US_NPI, US_PASSPORT, US_SSN

Entity objects are immutable configs. Use .replace(...) to override one field without rebuilding the whole entity:

from maskpipe import entities

masked_email = entities.EMAIL.replace(redactor="[EMAIL]")

Creating Custom Entities

from maskpipe.entities import Entity

EMPLOYEE_ID = Entity(
    label="EMPLOYEE_ID",
    patterns=[
        {"pattern": [{"TEXT": {"REGEX": r"EMP-\\d{5}"}}], "score": 0.9, "id": "employee-id"},
    ],
    context_patterns=[
        {"pattern": [{"LOWER": "employee"}]},
        {"context_label": "STAFF_ID", "pattern": [{"LOWER": "staff"}, {"LOWER": "id"}]},
    ],
    validator=lambda span: span.text.startswith("EMP-"),
    redactor=lambda text: "EMP-XXXXX",
)

Supported redactors:

• fixed string: "[MASK]"
• zero-argument callable: lambda: "generated-value"
• one-argument callable: lambda text: text[:1] + "*" * (len(text) - 1)

DocBuilder

DocBuilder converts character offsets from external NER systems into spaCy spans with scores.

Basic Usage

from maskpipe import DocBuilder

# Create a doc and add entities
doc = DocBuilder(nlp, text).with_entities(
    entities=[
        {"start": 0, "end": 5, "label": "PERSON", "score": 0.95},
        {"start": 30, "end": 45, "label": "EMAIL", "score": 0.99},
    ]
).build()

doc = nlp(doc)
print(doc._.masked)

Entity Format

with_entities() expects a list of dicts with at least:

• start: character offset (int)
• end: character offset (int)
• label: entity type (str)
• score: confidence [0.0, 1.0] (float, optional)

entities = [
    {"start": 0, "end": 5, "label": "PERSON", "score": 0.95},
    {"start": 30, "end": 45, "label": "EMAIL", "score": 0.99},
]
doc = DocBuilder(nlp, text).with_entities(entities).build()

Entity Mappers

Use entity_mapper to normalize different NER output formats. MaskPipe provides pre-configured mappers:

Mapper	Use For	Key Fields
GLINER_MAPPER	GLiNER (x-large)	start, end, label, score
GLINER2_MAPPER	GLiNER2 (nested format)	nested {label: {start, end, confidence}}
HF_NER_MAPPER	HuggingFace NER	entity_group (or entity), start, end, score
OPENMED_MAPPER	OpenMed (clinical NER / HIPAA)	start, end, label, confidence

Example with GLiNER:

from maskpipe import GLINER_MAPPER, DocBuilder
from gliner import GLiNER

model = GLiNER.from_pretrained("knowledgator/gliner-x-large")
text = "Patient John Doe, email: john@example.com"
predictions = model.predict_entities(text, labels=["person", "email"], threshold=0.5)

doc = DocBuilder(nlp, text).with_entities(predictions, entity_mapper=GLINER_MAPPER).build()
doc = nlp(doc)
print(doc._.masked)
# [PERSON], email: [EMAIL]

Example with HuggingFace NER:

from maskpipe import HF_NER_MAPPER, DocBuilder
from transformers import pipeline

ner = pipeline("ner", model="dslim/bert-base-NER")
text = "Contact: alice@example.com"
results = ner(text)

doc = DocBuilder(nlp, text).with_entities(results, entity_mapper=HF_NER_MAPPER).build()
doc = nlp(doc)
print(doc._.masked)
# Contact: [EMAIL]

Custom Mappers

Create custom mappers for other NER systems:

from maskpipe import EntityMapper

# For any system with {start, end, label, score}
custom_mapper = EntityMapper(label="type", score="confidence")

# For systems with conditional label fields
fallback_mapper = EntityMapper(
    label="entity_type",
    label_fallback="category",  # use if entity_type not found
    score="conf"
)

Batch Processing

Use build_batch() to process multiple texts at once:

docs = list(DocBuilder.build_batch(
    nlp=nlp,
    texts=["text1", "text2", "text3"],
    entities_list=[
        [{"start": 0, "end": 5, "label": "PERSON", "score": 0.9}],
        [{"start": 10, "end": 20, "label": "EMAIL", "score": 0.95}],
        [],  # no entities
    ],
))

for doc in nlp.pipe(docs):
    print(doc._.masked)

Context Words

Add context to help the context_enhancer component make relabeling decisions:

doc = DocBuilder(nlp, text).with_context_words(["email", "contact"]).build()

Customizing Components

PipelineBuilder

PipelineBuilder adds the default component chain in this order:

1. recognizer
2. context_enhancer
3. conflict_resolver
4. anonymizer

You can disable components you do not need:

from maskpipe import PipelineBuilder

builder = PipelineBuilder(
    nlp,
    label_mapping={"persoon": "PERSON"},
    disable=["context_enhancer"],
)

Context Enhancement

Add context patterns directly to the component:

context_enhancer = nlp.get_pipe("context_enhancer")
context_enhancer.add_patterns([
    {
        "label": "EMAIL",
        "pattern": [{"LOWER": {"IN": ["email", "mail", "e-mail"]}}],
    }
])

Important:

• context patterns match by label
• score changes come from component config such as confidence_boost
• context_label can relabel a matched span
• doc._.context_words lets you add extra context terms not present in the text

Anonymizer

anonymizer = nlp.get_pipe("anonymizer")
anonymizer.add_redactors({
    "EMAIL": "[REDACTED]",
    "ID": lambda: "ID-000001",
    "PERSON": lambda text: text[0] + "." * (len(text) - 1),
})

The anonymizer:

• leaves doc.text unchanged
• stores masked output in doc._.masked
• stores the chosen replacement in span._.replacement

spaCy Extensions Added by MaskPipe

Document extensions:

• doc._.masked
• doc._.context_words

Span extensions:

• span._.score
• span._.context
• span._.replacement

Minimal API Reference

PipelineBuilder(nlp, label_mapping=None, disable=None)
DocBuilder(
    nlp,
    text,
    label_mapping=None,
    spans_key="sc",
    annotate_ents=False,
    default_score=0.6,
    alignment_mode="strict",
)
Entity(
    label,
    patterns=None,
    custom_matcher=None,
    validator=None,
    context_patterns=None,
    redactor=None,
)

Development

uv sync --dev
uv run pytest -q

To regenerate entity files from Presidio recognizers:

uv sync --group codegen
python scripts/gen_entity.py --update-all

License

MIT. See LICENSE.