iamSamurai/privacy-filter-nigeria

Privacy Filter Nigeria LoRA

A LoRA adapter on top of openai/privacy-filter for Nigerian-domain PII span detection.

• Adapter repo: iamSamurai/privacy-filter-nigeria
• Base model: openai/privacy-filter
• Adapter type: LoRA (PEFT) for token classification
• Source repo: https://github.com/iamNarcisse/naija-privacy-filter
• Eval artifacts: iamSamurai/openai-privacy-filter-naija-eval-artifacts
• License: Apache-2.0 (this adapter). Use of the base model remains subject to the upstream model's terms.

Evaluation

Latest v5 eval against the internal stage2 v5 private mixed dataset, after deterministic span postprocessing:

Split	Typed span F1	Precision	Recall	TP	FP	FN
Validation	0.9763	0.9707	0.9820	762	23	14
Test	0.9640	0.9593	0.9688	777	33	25

The v5 challenge split is hard-negative-only. Typed F1 is not meaningful because there are no gold positive spans. Use false-positive diagnostics:

Challenge diagnostic	Value
Examples	250
Examples with predictions	180
False-positive example rate	0.72
Predicted false-positive spans	456

This is a recall-oriented research adapter. The hard-negative audit shows that benign identifier-like text can be over-redacted. Precision-sensitive users should add deterministic filters, tune thresholds where applicable, or finetune on representative local negatives.

Supported Label Spans

The adapter emits these span labels. O is the background token label and is not returned as a detected span.

Label	Detects	Example
account_number	Nigerian bank account/NUBAN-style account numbers when context indicates an account	6318826391
private_address	Street, city, state, or postal address spans tied to a person or record	42 Unity Road, Ikeja, Lagos 100271
private_bvn	Nigerian Bank Verification Number references and values	22334455667
private_date	Dates tied to a person, record, document, or event in a private workflow	12 April 1988
private_drivers_license_number	Nigerian driver license identifiers	K2BHY7F6FEA0
private_email	Email addresses	amina.yusuf@example.ng
private_nin	Nigerian National Identification Number references and values	12345678901
private_passport_number	Nigerian passport identifiers	B05995318
private_person	Person names and name-like references	Amina Yusuf
private_phone	Nigerian local and international phone-number formats	+234 802 111 3344
private_url	URLs tied to private records, claims, documents, or workflows	https://claims.example/record/1234
private_voters_card_number	Nigerian voter card identifiers	ABCD 1234 5678 9012 345
secret	Known-format credentials, authorization codes, session tokens, and similar secrets	S3cure!9037Ops

How To Use

Note on the classifier head. This adapter ships a resized token- classification head for the Nigerian-domain label taxonomy (label_map.json / token_label_names in adapter_config.json). Loading the adapter on top of the unmodified base model with vanilla peft.PeftModel.from_pretrained will not resize the head automatically. Use the project runner (privacy_filter.py) or replicate its head-resize logic to get correct predictions.

Recommended: project runner

pip install "torch>=2.8" "transformers>=4.56" "peft>=0.17" "huggingface-hub>=0.34"
git clone https://github.com/iamNarcisse/naija-privacy-filter
cd naija-privacy-filter

python main.py \
  --model-name openai/privacy-filter \
  --adapter-name iamSamurai/privacy-filter-nigeria \
  "Amina Yusuf can be reached at +234 802 111 3344."

Or via uv:

uv run python main.py \
  --model-name openai/privacy-filter \
  --adapter-name iamSamurai/privacy-filter-nigeria \
  "Amina Yusuf can be reached at +234 802 111 3344."

Example result

For the adapter command above, the cleaned output should contain:

Field	Value
Status	PII detected
Detected spans	2
Mode	cleaned
Adapter	iamSamurai/privacy-filter-nigeria

Label	Text	Start	End
private_person	Amina Yusuf	0	11
private_phone	+234 802 111 3344	30	47

Confidence scores are model outputs and are not privacy, security, or compliance guarantees.

REST API

PRIVACY_FILTER_MODEL_NAME=openai/privacy-filter \
PRIVACY_FILTER_ADAPTER_NAME=iamSamurai/privacy-filter-nigeria \
uv run uvicorn api:app --reload

curl -X POST http://127.0.0.1:8000/predict \
  -H "Content-Type: application/json" \
  -d '{"text":"Amina Yusuf can be reached at +234 802 111 3344.","mode":"cleaned"}'

Direct transformers + peft (advanced)

If you want to bypass the project runner, you must resize the base model's classification head to match token_label_names from the adapter's adapter_config.json before applying the LoRA weights. See privacy_filter.py for the reference implementation.

Intended Use

Use this adapter for:

• Research and evaluation of Nigerian-domain PII detection.
• Prototyping local inference or REST API integration for privacy-filtering workflows.
• Studying LoRA adaptation and deterministic span postprocessing for token-classification models.
• Producing candidate spans for downstream review, redaction, or policy engines.

Do not use this adapter as the only control for regulatory, legal, medical, financial, or irreversible privacy decisions.

Training Data

The source repository includes a tiny public synthetic example bundle, data/examples:

Split	Examples
Train	5
Validation	5
Test	5
Challenge	5

This example bundle is for schema inspection and smoke tests only. It is not a training or evaluation release.

The current v5 adapter was trained and evaluated against a later private stage2 v5 mixed dataset that is not distributed with this model card. That private mix includes synthetic examples, OCR-derived Nigerian identity-document samples used to test document-layout and OCR behavior, and real-world domain samples. Direct identifiers and sensitive fields were annotated and redacted from model-use fields. Source materials and derived artifacts remain private and are not distributed.

Supported span labels are listed in Supported Label Spans.

The committed public examples are synthetic. The private v5 mix is broader and includes reviewed non-synthetic source material after direct identifiers and sensitive fields were redacted from model-use fields. It is not a public corpus of real user records.

Bias, Risks, And Limitations

This adapter is built on openai/privacy-filter and inherits the upstream model's bias, risk, and limitation profile. The notes below summarize and extend that profile for the Naija research preview. Consult the upstream model card for the authoritative description of base-model behavior.

Over-Reliance

This adapter, like the base model, is a redaction and data-minimization aid. It is not an anonymization, compliance, or safety guarantee. Treating its output as a blanket anonymization claim risks missing the privacy objectives the system is being deployed to support. Use it as one layer in a privacy-by-design pipeline alongside policy controls, access controls, logging discipline, and human review where mistakes have material impact. The model detects spans; it does not by itself enforce retention, access control, consent, or data-subject rights.

Static Label Policy

The model only detects spans that match its trained label taxonomy. Real-world privacy policies vary, and label boundaries appropriate for one organization may not be appropriate for another. Adjusting the policy requires further finetuning, not runtime configuration. The Naija adapter shifts boundaries for Nigerian-domain identifiers (NIN, BVN, NUBAN account numbers, voter card, driver license, passport, addresses, phone formats), but does not introduce a runtime policy configuration mechanism.

Domain And Language Coverage

Performance can drop on:

• non-English text and non-Latin scripts;
• naming patterns or identifier formats not represented in training data;
• domains outside the evaluated private mix, including unseen OCR layouts, noisy chat logs, code-switched multilingual text, and organization-specific record formats.

The private evaluation mix cannot fully represent every deployment distribution. High typed-span F1 on the included splits should not be read as evidence of production readiness on all real records.

Failure Modes

Like all models, this adapter can make mistakes. Common failure modes include:

• under-detection of uncommon personal names, regional naming conventions, initials, or honorific-heavy references;
• over-redaction of organizations, locations, or common nouns when local context is ambiguous;
• fragmented or shifted span boundaries in mixed-format text, long documents, or text with heavy punctuation and layout artifacts;
• structured-identifier ambiguity - a numeric string may be a NIN, BVN, account number, invoice number, order ID, or unrelated code, and the model cannot always disambiguate without surrounding context;
• missed secrets for novel credential formats, project-specific token patterns, or secrets split across surrounding syntax;
• over-redaction of benign high-entropy strings, hashes, placeholders, sample credentials, synthetic examples, dates, checksums, and routing IDs that resemble real secrets or identity numbers.

Deterministic span postprocessing (span_postprocess.py in the source repo) reduces some boundary and known-format failures, but it is tuned for the synthetic Naija release. Applied outside that distribution it may itself introduce false positives.

These failure modes can interact with demographic, regional, and domain variation. Names and identifiers underrepresented in the training data, or that follow conventions different from the dominant training distribution, are more likely to be missed or inconsistently bounded.

High-Risk Deployment Caution

Additional caution is warranted in medical, legal, financial, human resources, education, and government workflows. In these settings, both false negatives and false positives can be costly: missed spans may expose sensitive information, while excess masking can remove material context needed for review, auditing, or downstream decisions. Do not use this adapter as the only control in such workflows.

Recommendations

• Use the model as part of a privacy-by-design pipeline, not as a standalone anonymization claim.
• Evaluate on representative in-domain data under local policy before production use.
• Add deterministic filters for high-precision structured IDs where local policy allows it.
• Finetune further when your policy boundaries differ from the trained taxonomy or when hard-negative precision matters.
• Preserve human review paths for high-sensitivity workflows.

Privacy And Safety

The public example bundle is synthetic and should not intentionally contain real personal data. Before publishing any new dataset, predictions, logs, or eval artifacts derived from this adapter, inspect them for accidental real PII or secrets.

Do not publish:

• raw real records;
• production prompts, logs, tickets, emails, or support transcripts containing personal data;
• API keys, access tokens, cookies, or credentials;
• model outputs that include unredacted real PII from private systems;
• raw private/internal prediction JSONL or configs containing absolute paths, private dataset IDs, or temporary directory names.

Citation And Attribution

If you use this adapter, please cite both the base model and this repository. Preserve the adapter repo ID, dataset version, code commit, and evaluation artifact commit in experiment reports so results are reproducible.

@misc{egonu2026privacyfilternigeria,
  author = {Egonu Narcisse},
  title  = {Privacy Filter Nigeria LoRA (v0.1 research preview)},
  year   = {2026},
  url    = {https://github.com/iamNarcisse/naija-privacy-filter},
  note   = {Adapter on top of openai/privacy-filter}
}

License

This adapter is released under the Apache License, Version 2.0. The base model openai/privacy-filter is governed by its own license; consult the upstream model card for terms.