bert small pii detection

Name: bert small pii detection
Author: gravitee-io

Providergravitee-io

Categorytoken-classification

Licenseapache-2.0

Downloads309.5K

Stars0

Overview

The bert-small-pii-detection model is a lightweight token-classification engine optimized for identifying personally identifiable information (PII) within text streams. Designed for developers who need to balance privacy compliance with low-latency performance, this model provides a computationally efficient alternative to larger BERT variants without sacrificing significant accuracy in entity recognition. It is particularly well-suited for preprocessing pipelines where data must be anonymized or masked before being passed to larger LLMs or stored in logs. Integration is straightforward via standard token-classification APIs, making it an ideal choice for edge deployments or high-throughput middleware where memory overhead is a critical constraint.

Highlights

Low-latency PII identification for high-throughput data pipelines
Lightweight architecture reduces infrastructure and memory overhead
Apache-2.0 license ensures flexible commercial integration
Ideal for pre-processing and data anonymization workflows

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("gravitee-io/bert-small-pii-detection")
tokenizer = AutoTokenizer.from_pretrained("gravitee-io/bert-small-pii-detection")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download gravitee-io/bert-small-pii-detection

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download gravitee-io/bert-small-pii-detection config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('gravitee-io/bert-small-pii-detection')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/gravitee-io/bert-small-pii-detection

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/gravitee-io/bert-small-pii-detection

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('gravitee-io/bert-small-pii-detection')
tokenizer = AutoTokenizer.from_pretrained('gravitee-io/bert-small-pii-detection')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
datasets:

gravitee-io/pii-detection-dataset

language:

base_model:

prajjwal1/bert-small

pipeline_tag: token-classification
tags:
- pii
- ner
- token-classification
- privacy
- ai-gateway
---

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

code

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)

python

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 [email protected]"
pipe(text)

ONNX

commandline

pip install transformers onnxruntime huggingface_hub

python

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 [email protected]"
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

<details>
<summary><code>gretelai/gretel-pii-masking-en-v1:test</code></summary>

| 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 |

</details>

<details>
<summary><code>gretelai/synthetic_pii_finance_multilingual:test</code></summary>

| 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 |

</details>

<details>
<summary><code>DataikuNLP/kiji-pii-training-data:test</code></summary>

| 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 /

View official source