gravitee-io/bert-small-pii-detection 🚀

A more accurate PII detector fine-tuned from prajjwal1/bert-small on the datasets described in metatada.

About the dataset:

We combined various datasets in order to cover wide range of document formats like:

1. JSON,
2. HTML,
3. XML,
4. SQL
5. Documents

Label Set

AGE, COORDINATE, CREDIT_CARD, DATE_TIME, EMAIL_ADDRESS, FINANCIAL, IBAN_CODE, IMEI,
IP_ADDRESS, LOCATION, MAC_ADDRESS, NRP, ORGANIZATION, PASSWORD, PERSON, PHONE_NUMBER,
TITLE, URL, US_BANK_NUMBER, US_DRIVER_LICENSE, US_ITIN, US_LICENSE_PLATE, US_PASSPORT, US_SSN

How to Use

Quick start (pipeline)

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

repo = "gravitee-io/bert-small-pii-detection"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForTokenClassification.from_pretrained(repo)

pipe = pipeline("token-classification", model=model, tokenizer=tok, aggregation_strategy="simple")
text = ""
pipe(text)

Evaluation

Metric: precision / recall / F1 per entity, micro/macro averages

Entity	Precision	Recall	F1-score	Support
AGE	0.9898	0.8858	0.9349	219
COORDINATE	0.9627	0.8738	0.9161	325
CREDIT_CARD	0.9273	0.8870	0.9067	115
DATE_TIME	0.8598	0.7364	0.7933	3255
EMAIL_ADDRESS	0.9428	0.8941	0.9178	387
FINANCIAL	0.9862	0.9565	0.9711	299
IBAN_CODE	0.9577	0.9252	0.9412	147
IMEI	0.9885	0.9663	0.9773	89
IP_ADDRESS	0.9338	0.8812	0.9068	160
LOCATION	0.8849	0.8222	0.8524	4264
MAC_ADDRESS	0.9889	1.0000	0.9944	89
NRP	1.0000	0.9818	0.9908	494
ORGANIZATION	0.7454	0.6688	0.7051	3551
PASSWORD	0.8384	0.8137	0.8259	102
PERSON	0.9123	0.8826	0.8972	4454
PHONE_NUMBER	0.9462	0.8199	0.8785	322
TITLE	0.9887	0.9734	0.9810	451
URL	1.0000	0.9787	0.9892	188
US_BANK_NUMBER	1.0000	0.9579	0.9785	95
US_DRIVER_LICENSE	0.9167	0.9167	0.9167	120
US_ITIN	0.9659	0.8763	0.9189	97
US_LICENSE_PLATE	1.0000	0.9000	0.9474	90
US_PASSPORT	0.9200	0.9200	0.9200	100
US_SSN	0.9744	0.9580	0.9661	119
micro avg	0.8804	0.8141	0.8460	19532
macro avg	0.9429	0.8948	0.9178	19532
weighted avg	0.8785	0.8141	0.8446	19532

Intended Uses & Limitations

Use this model for:

• Low resource environmens
• Redacting PII in customer support logs, dev/test environments, API traces and articles
• Real-time hints in form fields or data entry systems

Limitations:

• English-focused; other languages will degrade
• Domain drift is real: audit on your own data

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Citation

If you use the model, please consider citing the papers:

@misc{bhargava2021generalization,
      title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
      author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
      year={2021},
      eprint={2110.01518},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{DBLP:journals/corr/abs-1908-08962,
  author    = {Iulia Turc and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {Well-Read Students Learn Better: The Impact of Student Initialization
               on Knowledge Distillation},
  journal   = {CoRR},
  volume    = {abs/1908.08962},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.08962},
  eprinttype = {arXiv},
  eprint    = {1908.08962},
  timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@online{WinNT,
  author = {Benjamin Kilimnik},
  title = {{Privy} Synthetic PII Protocol Trace Dataset},
  year = 2022,
  url = {https://huggingface.co/datasets/beki/privy},
}

@online{gretel2023,
  author = {Gretel.ai},
  title = {{Synthetic PII Finance Multilingual Dataset}},
  year = 2023,
  url = {https://huggingface.co/datasets/gretelai/synthetic_pii_finance_multilingual},
}

@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
    title = "Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition",
    author = "Tjong Kim Sang, Erik F. and De Meulder, Fien",
    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003",
    year = "2003",
    url = "https://aclanthology.org/W03-0419",
}
}