SensiGuard-PII is a token-classification model fine-tuned to detect common PII/PCI/PHI fields (e.g., names, emails, phone, SSN, card numbers, bank details, IPs, API keys). The base encoder is microsoft/deberta-v3-base trained on a mixture of synthetic, weak-labeled, and public PII datasets, using BIO tagging with class weighting to handle imbalance. Sample Usage:

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "your_namespace/SensiGuard-PII"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

nlp = pipeline("token-classification", model=model, tokenizer=tok, aggregation_strategy="simple")
text = "My SSN is 123-45-6789 and my card is 4111 1111 1111 1111."
print(nlp(text))
# [{'entity_group': 'SSN', 'score': 0.99, 'word': '123-45-6789', 'start': 10, 'end': 21},

Intended uses & limitations

Intended Uses

Ingress/egress scanning for applications or LLM systems to identify sensitive spans.
Redaction or logging workflows where you need start/end offsets and label types.
Semi-supervised bootstrapping: weak-label new corpora with this model and fine-tune further.

Limitations

Not a silver bullet: precision/recall can vary by domain, language (primarily English), and formatting.
PCI: needs coverage for diverse card formats; pair with regex + Luhn validation and post-processing thresholds.
May miss edge cases or yield false positives on lookalike numbers/strings; test on your own data.
No safety/ethical filtering beyond PII detection; downstream policy is your responsibility.

Training and evaluation data

Sources: Mixed synthetic + public/weak-labeled PII corpora. Synthetic data was generated with pattern templates and optional LLM augmentation (vLLM/OpenAI-compatible) to cover names, emails, phones, SSN, PCI (card number/expiry/CVV/last4), bank account/routing, IPs, credentials, and healthcare identifiers. Public components include Nemotron-PII, AI4Privacy PII, Mendeley financial PII, and optional weak-labeling over Enron-style text. Labels were normalized into a common schema; unsupported labels were dropped.
Splits: If no validation file is provided, the training JSONL is auto-split 90/10 (train/val) with train_test_split(test_size=0.1, seed=42).
Class balancing: Inverse-frequency class weights were applied to mitigate the dominant O class.
Notes: PCI coverage includes spaced/dashed card formats and expiries; regex/Luhn hard negatives were used to reduce false positives. Evaluation metrics are token-level precision/recall/F1 (seqeval) on the held-out validation split.
Limitations: Mostly English; domain and format shifts may impact performance. Test on your own data and adjust thresholds/label mappings as needed.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.0148	1.0	4650	0.0099	0.6266	0.9636	0.7594
0.0018	2.0	9300	0.0067	0.6437	0.9659	0.7726

Framework versions

Transformers 4.57.3
Pytorch 2.6.0+rocm6.1
Datasets 4.4.1
Tokenizers 0.22.1

TensorGreed/SensiGuard-PII

SensiGuard-PII

Model description