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import re
from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
def mask_pii_multilingual(text: str):
# Load model only once globally if needed
model_name = "Davlan/xlm-roberta-base-ner-hrl"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
ner_pipe = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
regex_patterns = {
"email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b",
"phone_number": r"(?:\+?\d{1,3})?[-.\s]?\(?\d{1,4}\)?[-.\s]?\d{2,4}[-.\s]?\d{2,4}[-.\s]?\d{2,4}",
"dob": r"\b(0?[1-9]|[12][0-9]|3[01])[-/](0?[1-9]|1[012])[-/](19[5-9]\d|20[0-3]\d)\b",
"aadhar_num": r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}\b",
"credit_debit_no": r"\b(?:\d{4}[\s-]?){3}\d{4}\b",
"cvv_no": r"\b\d{3,4}\b",
"expiry_no": r"\b(0[1-9]|1[0-2])[/-]?(?:\d{2}|\d{4})\b"
}
entities = []
masked_text = text
offsets = []
# Step 1: Apply regex PII masking first
for entity_type, pattern in regex_patterns.items():
for match in re.finditer(pattern, text):
start, end = match.start(), match.end()
if any(start < e[1] and end > e[0] for e in offsets):
continue
token = f"[{entity_type}]"
entity_val = text[start:end]
masked_text = masked_text[:start] + token + masked_text[end:]
offsets.append((start, end))
entities.append({
"position": [start, end],
"classification": entity_type,
"entity": entity_val
})
# Step 2: Run NER on updated masked_text to avoid overlap
ner_results = ner_pipe(masked_text)
for ent in ner_results:
start, end = ent["start"], ent["end"]
if ent["entity_group"] != "PER":
continue
if any(start < e[1] and end > e[0] for e in offsets):
continue
token = "[full_name]"
entity_val = text[start:end]
masked_text = masked_text[:start] + token + masked_text[end:]
entities.append({
"position": [start, end],
"classification": "full_name",
"entity": entity_val
})
offsets.append((start, end))
# Sort final result
entities.sort(key=lambda x: x["position"][0])
return masked_text, entities
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