A fine-tuned DistilBERT model for detecting personally identifiable information (person names and addresses) in unstructured text; the core "separate model" component of PrivyShield, an on-device privacy protection tool built for OSDHack 2026.
Regex can reliably catch structured PII (card numbers, emails, Aadhaar/PAN formats), but it cannot catch names and addresses, which don't follow a fixed pattern. This model fills that specific gap as part of PrivyShield's layered detection pipeline; regex handles structured formats, this model handles unstructured entities, and both run entirely on-device.
Designed to run locally (via ONNX Runtime) as part of a real-time screen-content privacy scanner. Not intended as a general-purpose NER model; it's scoped specifically to PERSON_NAME and ADDRESS detection for this use case, trained primarily on Indian name/address patterns.
1from huggingface_hub import hf_hub_download
2import onnxruntime as ort
3
4model_path = hf_hub_download(
5 repo_id="aditrynacode/privyshield-ner",
6 filename="ner_model.onnx"
7)
8session = ort.InferenceSession(model_path)
Part of
PrivyShield, submitted to
OSDHack 2026 (Open Source Developers Community).
