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bert-small-pii-detection

Token Classification
ONNX
Safetensors
English
bert
pii
ner
privacy
ai-gateway
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gravitee-io/bert-small-pii-detection 🚀

Token-classification model for PII detection, fine-tuned from prajjwal1/bert-small on gravitee-io/pii-detection-dataset.

Label Set 

AGE, COORDINATE, CREDIT_CARD, DATE_TIME, EMAIL_ADDRESS, FINANCIAL, HONORIFIC, 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 = "Contact John Smith at john@example.com"
pipe(text)

ONNX 

pip install transformers onnxruntime huggingface_hub 

from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, AutoConfig
import onnxruntime as ort

model_id = "gravitee-io/bert-small-pii-detection"

tokenizer = AutoTokenizer.from_pretrained(model_id)
id2label = AutoConfig.from_pretrained(model_id).id2label
session = ort.InferenceSession(hf_hub_download(model_id, "model.quant.onnx"))

text = "Contact John Smith at john@example.com"
enc = tokenizer(text, return_tensors="np")
inputs = {"input_ids": enc["input_ids"], "attention_mask": enc["attention_mask"]}
logits = session.run(None, inputs)[0][0]

tokens = tokenizer.convert_ids_to_tokens(enc["input_ids"][0])
labels = [id2label[i] for i in logits.argmax(-1)]

for tok, label in zip(tokens, labels):
  print(f"{tok:<20} {label}")

Intended use

Detect personally identifiable information (PII) spans in english text. Suitable for privacy filtering, redaction pipelines, and data-leak prevention particularly on structured data (JSON, HTML, XML, SQL, Document)

Evaluation

Metric Value
F1 0.8686
Precision 0.8182
Recall 0.9256
Eval loss 0.0132

Limitations

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

Benchmarks

External-corpus evaluation (English only), seqeval. Last run: 2026-05-21.

Benchmark Examples FP32 micro F1 FP32 macro F1 INT8 micro F1 INT8 macro F1
gretelai/gretel-pii-masking-en-v1:test 5,000 0.9141 0.8971 0.9121 0.8860
gretelai/synthetic_pii_finance_multilingual:test 2,962 0.7534 0.7354 0.7498 0.7351
DataikuNLP/kiji-pii-training-data:test 1,033 0.9259 0.8685 0.9265 0.8725
beki/privy:test 28,843 0.8809 0.9694 0.8800 0.9680
beki/privy:test-large 120,574 0.9833 0.9810 0.9825 0.9801

Per-entity breakdown

gretelai/gretel-pii-masking-en-v1:test
Entity FP32 F1 FP32 P / R Support INT8 F1 INT8 P / R
AGE 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
COORDINATE 0.8966 0.876 / 0.918 85 0.8966 0.876 / 0.918
CREDIT_CARD 0.9572 0.937 / 0.979 663 0.9524 0.926 / 0.980
DATE_TIME 0.9605 0.935 / 0.988 3,805 0.9568 0.929 / 0.987
EMAIL_ADDRESS 0.9854 0.976 / 0.995 1,048 0.9854 0.976 / 0.995
FINANCIAL 0.7143 0.641 / 0.806 31 0.6857 0.615 / 0.774
IMEI 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
IP_ADDRESS 0.9819 0.974 / 0.990 961 0.9829 0.976 / 0.990
LOCATION 0.8549 0.853 / 0.857 1,760 0.8561 0.855 / 0.857
NRP 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
ORGANIZATION 0.7159 0.611 / 0.865 185 0.6974 0.587 / 0.859
PASSWORD 0.8712 0.793 / 0.966 119 0.8679 0.788 / 0.966
PERSON 0.7973 0.781 / 0.814 3,209 0.7948 0.781 / 0.809
PHONE_NUMBER 0.9738 0.962 / 0.986 904 0.9701 0.955 / 0.986
TITLE 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
URL 0.8846 0.793 / 1.000 23 0.8302 0.733 / 0.957
US_BANK_NUMBER 0.9610 0.962 / 0.960 398 0.9611 0.960 / 0.962
US_DRIVER_LICENSE 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
US_ITIN 0.8936 0.875 / 0.913 23 0.8333 0.800 / 0.870
US_LICENSE_PLATE 0.9171 0.873 / 0.965 579 0.9156 0.871 / 0.965
US_PASSPORT 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
US_SSN 0.9880 0.985 / 0.991 1,705 0.9898 0.988 / 0.992
gretelai/synthetic_pii_finance_multilingual:test
Entity FP32 F1 FP32 P / R Support INT8 F1 INT8 P / R
AGE 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
COORDINATE 0.6000 0.483 / 0.792 53 0.6087 0.494 / 0.792
CREDIT_CARD 0.5874 0.467 / 0.792 53 0.6143 0.494 / 0.811
DATE_TIME 0.7410 0.667 / 0.833 4,294 0.7406 0.667 / 0.833
EMAIL_ADDRESS 0.7971 0.746 / 0.856 576 0.7981 0.741 / 0.865
FINANCIAL 0.7048 0.632 / 0.796 294 0.6967 0.624 / 0.789
IBAN_CODE 0.8514 0.778 / 0.940 67 0.8571 0.787 / 0.940
IP_ADDRESS 0.7854 0.796 / 0.775 111 0.7892 0.786 / 0.793
LOCATION 0.7554 0.684 / 0.844 1,938 0.7506 0.677 / 0.842
NRP 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
ORGANIZATION 0.6975 0.612 / 0.811 2,702 0.6876 0.602 / 0.802
PASSWORD 0.6392 0.508 / 0.861 36 0.5941 0.462 / 0.833
PERSON 0.8125 0.778 / 0.851 3,295 0.8085 0.771 / 0.850
PHONE_NUMBER 0.8648 0.791 / 0.953 406 0.8651 0.790 / 0.956
TITLE 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
URL 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
US_BANK_NUMBER 0.6038 0.511 / 0.738 65 0.5976 0.495 / 0.754
US_DRIVER_LICENSE 0.7731 0.697 / 0.868 53 0.7797 0.708 / 0.868
US_ITIN 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
US_LICENSE_PLATE 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
US_PASSPORT 0.7419 0.708 / 0.780 59 0.7680 0.727 / 0.814
US_SSN 0.8112 0.773 / 0.853 68 0.8056 0.763 / 0.853
DataikuNLP/kiji-pii-training-data:test
Entity FP32 F1 FP32 P / R Support INT8 F1 INT8 P / R
AGE 0.8682 0.789 / 0.966 116 0.8794 0.801 / 0.974
CREDIT_CARD 0.9431 0.892 / 1.000 58 0.9587 0.921 / 1.000
DATE_TIME 0.8276 0.742 / 0.936 141 0.8354 0.754 / 0.936
EMAIL_ADDRESS 0.9942 0.989 / 1.000 258 0.9942 0.989 / 1.000
IBAN_CODE 0.9655 0.942 / 0.990 99 0.9703 0.951 / 0.990
LOCATION 0.9115 0.878 / 0.948 3,630 0.9116 0.881 / 0.945
ORGANIZATION 0.7439 0.716 / 0.774 274 0.7435 0.712 / 0.777
PASSWORD 0.8732 0.845 / 0.903 103 0.9005 0.880 / 0.922
PERSON 0.9685 0.956 / 0.981 1,987 0.9665 0.952 / 0.981
PHONE_NUMBER 0.9676 0.968 / 0.968 247 0.9676 0.968 / 0.968
TITLE 0.0000 0.000 / 0.000 3 0.0000 0.000 / 0.000
URL 0.9474 0.936 / 0.959 169 0.9419 0.926 / 0.959
US_DRIVER_LICENSE 0.9323 0.900 / 0.967 121 0.9558 0.930 / 0.983
US_ITIN 0.9474 0.947 / 0.947 95 0.9474 0.947 / 0.947
US_LICENSE_PLATE 0.9669 0.959 / 0.975 120 0.9508 0.935 / 0.967
US_PASSPORT 0.9787 0.966 / 0.991 116 0.9746 0.958 / 0.991
US_SSN 0.9291 0.892 / 0.969 196 0.9337 0.900 / 0.969
beki/privy:test
Entity FP32 F1 FP32 P / R Support INT8 F1 INT8 P / R
AGE 0.9659 0.934 / 1.000 764 0.9610 0.926 / 0.999
COORDINATE 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
CREDIT_CARD 1.0000 1.000 / 1.000 757 1.0000 1.000 / 1.000
DATE_TIME 0.9975 0.995 / 1.000 5,289 0.9975 0.995 / 0.999
EMAIL_ADDRESS 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
FINANCIAL 0.9584 0.924 / 0.996 2,243 0.9541 0.916 / 0.996
HONORIFIC 0.9970 0.994 / 1.000 2,345 0.9972 0.995 / 1.000
IBAN_CODE 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
IMEI 1.0000 1.000 / 1.000 769 0.9994 0.999 / 1.000
IP_ADDRESS 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
LOCATION 0.8851 0.968 / 0.815 12,930 0.8850 0.968 / 0.815
MAC_ADDRESS 0.9986 0.997 / 1.000 735 0.9959 0.992 / 1.000
NRP 0.9958 0.992 / 0.999 3,829 0.9956 0.992 / 0.999
ORGANIZATION 0.9820 0.977 / 0.987 1,493 0.9807 0.974 / 0.987
PASSWORD 0.9348 0.881 / 0.996 720 0.9386 0.886 / 0.997
PERSON 0.9897 0.988 / 0.991 7,986 0.9878 0.986 / 0.990
PHONE_NUMBER 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
TITLE 0.9661 0.942 / 0.992 732 0.9655 0.939 / 0.993
URL 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
US_BANK_NUMBER 0.9951 0.990 / 1.000 717 0.9958 0.992 / 1.000
US_DRIVER_LICENSE 0.9303 0.890 / 0.974 781 0.9225 0.875 / 0.976
US_ITIN 0.9811 0.965 / 0.997 754 0.9824 0.968 / 0.997
US_LICENSE_PLATE 0.9390 0.895 / 0.987 788 0.9334 0.885 / 0.987
US_PASSPORT 0.9334 0.893 / 0.977 753 0.9320 0.894 / 0.973
US_SSN 0.0000 0.000 / 0.000 0 0.0000 0.000 / 0.000
beki/privy:test-large
Entity FP32 F1 FP32 P / R Support INT8 F1 INT8 P / R
AGE 0.9447 0.895 / 1.000 3,092 0.9441 0.895 / 0.999
COORDINATE 0.9994 0.999 / 1.000 9,543 0.9996 0.999 / 1.000
CREDIT_CARD 0.9968 0.997 / 0.996 3,151 0.9970 0.997 / 0.997
DATE_TIME 0.9925 0.986 / 1.000 22,136 0.9923 0.985 / 0.999
EMAIL_ADDRESS 0.9992 0.999 / 1.000 3,142 0.9987 0.998 / 1.000
FINANCIAL 0.9481 0.907 / 0.993 9,360 0.9433 0.898 / 0.993
HONORIFIC 0.9982 0.997 / 1.000 9,584 0.9982 0.997 / 1.000
IBAN_CODE 0.9982 0.996 / 1.000 3,099 0.9982 0.996 / 1.000
IMEI 0.9998 1.000 / 1.000 3,116 0.9997 0.999 / 1.000
IP_ADDRESS 0.9972 0.994 / 1.000 3,185 0.9970 0.995 / 0.999
LOCATION 0.9764 0.964 / 0.990 43,932 0.9761 0.963 / 0.989
MAC_ADDRESS 0.9957 0.992 / 1.000 3,137 0.9951 0.991 / 1.000
NRP 0.9948 0.991 / 0.999 15,943 0.9948 0.991 / 0.998
ORGANIZATION 0.9794 0.970 / 0.989 6,165 0.9762 0.963 / 0.989
PASSWORD 0.9656 0.936 / 0.997 3,082 0.9599 0.925 / 0.997
PERSON 0.9887 0.987 / 0.990 32,380 0.9878 0.985 / 0.990
PHONE_NUMBER 0.9979 0.996 / 1.000 3,099 0.9974 0.995 / 1.000
TITLE 0.9744 0.954 / 0.995 3,192 0.9696 0.945 / 0.996
URL 0.9985 0.997 / 1.000 6,237 0.9985 0.997 / 1.000
US_BANK_NUMBER 0.9948 0.991 / 0.999 3,091 0.9937 0.989 / 0.998
US_DRIVER_LICENSE 0.9238 0.874 / 0.979 3,041 0.9208 0.869 / 0.979
US_ITIN 0.9821 0.966 / 0.999 2,995 0.9829 0.967 / 0.999
US_LICENSE_PLATE 0.9458 0.902 / 0.994 3,049 0.9414 0.894 / 0.994
US_PASSPORT 0.9344 0.889 / 0.985 3,044 0.9405 0.901 / 0.983
US_SSN 0.9982 0.996 / 1.000 2,980 0.9990 0.998 / 1.000

Citation

Data citation are present in the dataset card used for this model. 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}
}
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