PrivacyFilterTuned
PrivacyFilterTuned is a finetuned version of
openai/privacy-filter. It is
adapted for privacy filtering workflows where partial SSNs and related compact
identifiers should be captured more consistently.
The main target behavior is improved detection of account-number style spans
such as:
SSN ending 1234SSN last four are 1234partial SSN 1234- nearby MRN, member ID, student ID, and similar identifier patterns
The model keeps the original Privacy Filter label space. Partial SSNs are
labeled as account_number.
Base Model
This checkpoint is based on openai/privacy-filter, a bidirectional token
classification model for PII detection and masking. The base model detects the
following span categories:
account_numberprivate_addressprivate_dateprivate_emailprivate_personprivate_phoneprivate_urlsecret
Finetuning
This checkpoint was finetuned locally on a small targeted dataset of healthcare,
education, and finance-style prompts. The data focuses on partial SSNs and
identifier boundary cases.
Limitations
This model is a privacy-filtering aid, not an anonymization or compliance
guarantee. It may still miss sensitive spans or redact spans that should remain
visible. Evaluate it on your own in-domain data before production use, and keep
human review paths for high-sensitivity workflows.
The finetuning set is small and targeted, so improvements are expected mainly
around partial SSNs and similar compact identifiers rather than broad PII
coverage.
Usage
With this repository downloaded locally:
1python -m opf "Patient Alice Smith has SSN ending 1234." \
2 --checkpoint /path/to/privacyfiltertuned \
3 --device cpu
For evaluation:
1python -m opf eval /path/to/eval.jsonl \
2 --checkpoint /path/to/privacyfiltertuned \
3 --device cpu
