A finetuned RoBERTa-base model for Personally Identifiable Information (PII) detection via Named Entity Recognition (NER), trained as part of a multi-layer enterprise AI guardrails system. The model identifies and classifies 57 types of PII entities in text using BIO tagging.

Model Description

This model is the PII detection component of a defense-in-depth guardrails gateway designed to prevent sensitive data exfiltration through LLM deployments. It operates alongside a jailbreak classifier and a prompt injection detector to form a multi-signal security layer.

Property	Value
Base model	`roberta-base` (125M params)
Task	Token classification (BIO NER)
Entity types	57 PII categories
Max input length	256 tokens
Training framework	PyTorch + HuggingFace Transformers

Training Data

Trained on the English subset of ai4privacy/pii-masking-200k — a large, diverse dataset of synthetic text with fine-grained PII annotations across 57 entity types.

Dataset split (English only):

Split	Examples
Train	~34,800
Validation	~4,350
Test	~4,351

Training Details

Optimizer:          AdamW
Learning rate:      3e-5
Epochs:             5 (best checkpoint: epoch 4)
Batch size:         16
Warmup ratio:       0.1
Weight decay:       0.01
Grad clipping:      1.0
Max seq length:     256
Label alignment:    First subword only (-100 for subsequent subwords)
Padding:            Dynamic (DataCollatorForTokenClassification)

Evaluation Results

Overall Metrics (Test Set)

Metric	Value
PII Recall	98.21% ← most critical for security
PII F1	97.96%
PII Precision	97.71%
F1 Micro (entity-level)	93.77%
Loss	0.0876

Training Curve

Epoch	Train Loss	Val F1 Micro	PII Recall
1	0.5546	0.8905	97.12%
2	0.0980	0.9252	97.72%
3	0.0784	0.9291	98.00%
4	0.0672	0.9380	98.07%
5	0.0565	0.9355	98.03%

Per-Entity-Type F1 (Test Set)

Entity	F1	Entity	F1
NEARBYGPSCOORDINATE	1.000	CREDITCARDISSUER	0.975
SEX	0.998	CITY	0.974
EMAIL	0.993	VEHICLEVIN	0.974
ORDINALDIRECTION	0.993	FIRSTNAME	0.973
URL	0.991	JOBTYPE	0.968
ACCOUNTNAME	0.990	LASTNAME	0.967
MAC	0.990	HEIGHT	0.965
VEHICLEVRM	0.990	BIC	0.961
USERNAME	0.990	STREET	0.959
IBAN	0.989	MIDDLENAME	0.949
GENDER	0.989	ACCOUNTNUMBER	0.948
USERAGENT	0.989	BITCOINADDRESS	0.946
SSN	0.988	CURRENCYSYMBOL	0.945
ETHEREUMADDRESS	0.987	EYECOLOR	0.945
PHONENUMBER	0.985	AMOUNT	0.944
PHONEIMEI	0.984	PIN	0.939
PASSWORD	0.982	CREDITCARDCVV	0.930
COMPANYNAME	0.982	PREFIX	0.929
SECONDARYADDRESS	0.981	BUILDINGNUMBER	0.918
JOBAREA	0.980	ZIPCODE	0.905
COUNTY	0.979	CREDITCARDNUMBER	0.901
TIME	0.979	LITECOINADDRESS	0.899
JOBTITLE	0.977	AGE	0.895
STATE	0.977	DATE	0.880
CITY	0.974	CURRENCYCODE	0.880
—	—	IP / IPV4 / IPV6	0.81 / 0.15

Harder entities: IP addresses (F1=0.15–0.81), DOB (0.71), CURRENCYNAME (0.42), and MASKEDNUMBER (0.84) score lower due to high ambiguity — these formats appear frequently in non-PII contexts.

Usage

python

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("shashidharbabu/roberta-base-pii-guardrails")
model     = AutoModelForTokenClassification.from_pretrained("shashidharbabu/roberta-base-pii-guardrails")
model.eval()

def detect_pii(text):
    words = text.split()
    enc   = tokenizer(
        words,
        is_split_into_words=True,
        return_tensors="pt",
        truncation=True,
        max_length=256,
    )
    with torch.no_grad():
        logits = model(**enc).logits
    preds    = logits.argmax(dim=-1)[0].tolist()
    word_ids = enc.word_ids(0)
    id2label = model.config.id2label

    results, prev_wid = [], None
    for wid, pred in zip(word_ids, preds):
        if wid is None or wid == prev_wid:
            prev_wid = wid
            continue
        label = id2label[pred]
        if label != "O":
            results.append({"word": words[wid], "entity": label})
        prev_wid = wid
    return results

# Example
text = "Hi, my name is John Smith and my email is john.smith@gmail.com"
print(detect_pii(text))
# → [
#     {'word': 'John',               'entity': 'B-FIRSTNAME'},
#     {'word': 'Smith',              'entity': 'B-LASTNAME'},
#     {'word': 'john.smith@gmail.com','entity': 'B-EMAIL'},
#   ]

Supported Entity Types (57 total)

ACCOUNTNAME · ACCOUNTNUMBER · AGE · AMOUNT · BIC · BITCOINADDRESS · BUILDINGNUMBER · CITY · COMPANYNAME · COUNTY · CREDITCARDCVV · CREDITCARDISSUER · CREDITCARDNUMBER · CURRENCY · CURRENCYCODE · CURRENCYNAME · CURRENCYSYMBOL · DATE · DOB · EMAIL · ETHEREUMADDRESS · EYECOLOR · FIRSTNAME · GENDER · HEIGHT · IBAN · IP · IPV4 · IPV6 · JOBAREA · JOBTITLE · JOBTYPE · LASTNAME · LITECOINADDRESS · MAC · MASKEDNUMBER · MIDDLENAME · NEARBYGPSCOORDINATE · ORDINALDIRECTION · PASSWORD · PHONEIMEI · PHONENUMBER · PIN · PREFIX · SECONDARYADDRESS · SEX · SSN · STATE · STREET · TIME · URL · USERAGENT · USERNAME · VEHICLEVIN · VEHICLEVRM · ZIPCODE

Limitations

English only: Trained exclusively on English text. Performance on non-English inputs is untested and likely poor
IP address detection: F1 of 0.15 for generic IP label — bare IP addresses are highly ambiguous and frequently appear in non-PII contexts
DOB / date ambiguity: Date of birth (F1=0.71) is confused with general dates; context-free date strings are hard to classify
Credit card vs SSN confusion: Long numeric sequences (e.g. credit card numbers in space-separated format) are sometimes misclassified as SSN
Synthetic training data: Trained on synthetically generated text — performance may differ on real-world informal text styles
Max 256 tokens: Long documents must be chunked; PII spanning chunk boundaries may be missed

Intended Use

This model is designed as a pre-filter and post-filter in an LLM serving pipeline to:

Detect PII in user inputs before they reach the base model
Detect PII in model outputs before they are returned to the user
Flag or redact sensitive information in enterprise AI deployments

Not intended for:

Legal compliance decisions without human review
Languages other than English
Real-time systems requiring sub-millisecond latency without optimization

Citation

@misc{guardrails2026,
  title  = {Multi-Layer LLM Security Gateway with Specialized Finetuned Models},
  author = {Shashidhar Babu et al.},
  year   = {2026},
  note   = {San Jose State University, Graduate Project}
}

Project

This model is part of the Guardrails Gateway project at San Jose State University — a multi-layer LLM security system combining:

🔍 PII Detection (this model)
🛡️ Jailbreak Detection (shashidharbabu/roberta-jailbreak-guardrails)
💉 Prompt Injection Detection (protectai/deberta-v3-base-prompt-injection-v2)

Tracked with Weights & Biases.

shashidharbabu/deberta-pii-guardrails

roberta-base-pii-guardrails