aegis

Aegis

Urdu span detector for personally identifiable information. This repository is the privacy-filter checkpoint. It is not the female TTS voice mahwizzzz/piper-voice-ur-aegis-female.

Purpose

Span-level detection of names, phone numbers, email addresses, addresses, dates, and account or CNIC-style identifiers in Urdu text, for assisted redaction. A demo that downloads this repository is mahwizzzz/de-identify.

Upstream attribution

finetune_summary.json records base_checkpoint as openai/privacy-filter. The same file records model_id as mahwizzzz/urdu-legal-privacy-filter, which is not this repository's id. config.json sets model_type to privacy_filter.

Span labels in config.json: O, private_person, private_phone, private_email, private_address, private_date, account_number.

Data provenance

Training data are mahwizzzz/uopf-sipar. That dataset repository is private, so the public cannot recheck it from the Hub.

Its dataset card tags the corpus synthetic and lists these train-split sources:

Those four counts sum to 80,000. The dataset card's split sizes, also repeated below, are 80,000 / 10,000 / 10,000. Generator model ids, prompts, and any human review are not in this model repository. The dataset card declares CC BY 4.0. This model card declares Apache-2.0. The model license does not relicense the dataset.

Train-domain counts stated on both cards: legal 21,986; finance 13,850; telecom 6,315; healthcare 5,410; travel 4,876; social_media 4,845; news_channel 4,818; government 3,965; ecommerce 3,923; insurance 3,382; education 3,319; hr_jobs 3,311.

Training

Figures below are copied from finetune_summary.json (updated_at 2026-05-22T08:26:21.708110Z). The checkpoint name in that file is uopf_sipar_prod_20260520_090709.

Evaluation

Two different records are in this repository. They are not the same result.

eval_metrics.json has an empty test object. Its validation block points at data/validation.jsonl and reports span-detection F1 0.9994091722694807, precision 0.9997770246164823, and recall 0.9990415905139749. Per-class span F1 in that file: private_person 0.9998982550742649, private_phone 1.0, private_email 0.9997391077484998, private_address 0.9978453383767306, private_date 1.0, account_number 0.9997052755673446. That file does not contain a base-model comparison.

The table below is retained from the previous card. It is not reproduced by eval_metrics.json. The card text described the metric as same-label overlap span F1 on a held-out test split, with exact span F1 requiring label and character offsets to match.

finetune_summary.json also records an evaluation_10k loss of 0.0015, 10,000 examples, 459,982 tokens, and 1004.19 inference tokens/sec. The script, split, and hardware for that timing are not named.

Limitations

• The public Hub copy of the training set cannot be inspected.
• The dataset card describes the text as synthetic. Performance on real documents is not reported in eval_metrics.json.
• The retained test table and the shipped validation JSON disagree. Treat them as separate, unreconciled records.
• The de-identify Space states that redaction is not a substitute for human review, and that Roman Urdu and mixed script are unsupported. Those statements are interface text, not rows in eval_metrics.json.
• The Space also says account_number is the weakest class. In the retained table above it is the lowest fine-tuned overlap F1. In eval_metrics.json validation span F1 it is not the lowest class.

License

Apache-2.0, as declared in the metadata. A LICENSE file was not in the repository file list retrieved for this revision.

Usage

This repository does not include an inference script or a Transformers modeling file. config.json uses model_type: privacy_filter. The de-identify Space downloads mahwizzzz/aegis and decodes BIOES labels with a Viterbi routine implemented in that Space, not in this repository. ONNX exports are under onnx/. Their input contract is not documented here.

Citation

@misc{mahwiz2026urdu_privacy_filter,
  title        = {Aegis},
  author       = {Mahwiz Khalil},
  year         = {2026},
  howpublished = {Hugging Face Model Hub},
  url          = {https://huggingface.co/mahwizzzz/aegis}
}