| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: SensiGuard-PII |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # SensiGuard-PII |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0067 |
| - Precision: 0.6437 |
| - Recall: 0.9659 |
| - F1: 0.7726 |
|
|
| ## Model description |
|
|
| 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 |
| |
