chiefautism/privacy-parserReverse of OpenAI Privacy Filter: same 1.5B model, returns PII as structured spans instead of masking.
OpenAI shipped Privacy Filter — a model that hides PII in text. Defenders use it so data doesn't leak.
But every defense has a second side. Attackers need PII too. From logs, dumps, abandoned S3 buckets, stolen inboxes. Same task: find everything personal in a pile of text.
Privacy Filter masks. Privacy Parser extracts.
Same model, same label taxonomy, same weights. Instead of <REDACTED> you
get structured spans — what, where, which type.
Defense: audit your data before it leaks. Offense: parse someone else's after it did.
The tool doesn't know whose side it's on. It's just a good parser.
uv venv
uv pip install -e ./privacy-filter
uv pip install -e ./pii_parser
First run downloads the opf 1.5B checkpoint (~3 GB) to ~/.opf/privacy_filter/.
from pii_parser.hybrid import HybridPIIParser
parser = HybridPIIParser(device="cpu")
result = parser.parse(
"Hi Quindle Testwick ([email protected] / +1-415-555-0102), "
"account 40702810500001234567, 14 Beautiful Ct, Anytown USA, "
"password Priv4cy-Filt3r-2026."
)
for s in result.spans:
print(f"{s.label:18} {s.text}")
private_person Quindle Testwick
private_email [email protected]
private_phone +1-415-555-0102
account_number 40702810500001234567
private_address 14 Beautiful Ct, Anytown USA
secret Priv4cy-Filt3r-2026
python -m pii_parser.cli_model "Alice paid 40702810500001234567 on 2026-05-17."
Hybrid = model + span-merge + regex backstop. Ship this one.
opf v2 taxonomy — 8 categories:
private_person · private_email · private_phone · private_address ·
private_url · private_date · account_number · secret
text
↓
opf 1.5B → BIOES logits → Viterbi (tuned) → char spans
↓
span-merge (glues Quindle + Testwick)
↓
regex backstop (URL, secret, account — where the model misses)
↓
spans[]
python pii_parser/tests/test_hybrid.py
Fixture F1: 0.929
Scenarios: 8/8 passed
Latency: ~600 ms CPU
Apache-2.0.
| Backend | Weights | Speed | F1 |
|---|
PIIParser | none | µs | 1.000 |
ModelPIIParser | 1.5B | 500 ms CPU | 0.733 |
HybridPIIParser | 1.5B | 600 ms CPU | 0.929 |