| --- |
| license: apache-2.0 |
| base_model: openai/privacy-filter |
| tags: |
| - token-classification |
| - pii-detection |
| - secrets-detection |
| - dlp |
| - india |
| - dpdp |
| language: |
| - en |
| - hi |
| metrics: |
| - f1 |
| - precision |
| - recall |
| --- |
| |
| # privacy-filter-india-v2 |
|
|
| Fine-tune of [`openai/privacy-filter`](https://huggingface.co/openai/privacy-filter) for Indian-PII + secrets detection in enterprise DLP. |
|
|
| ## What this is |
|
|
| A token-classifier targeting the **8 base categories** from `openai/privacy-filter` (`account_number`, `private_address`, `private_date`, `private_email`, `private_person`, `private_phone`, `private_url`, `secret`), trained to recognise the long tail of Indian-specific identifiers and the secret formats the base model under-detected: |
|
|
| - **IFSC codes** (Indian bank routing) |
| - **Indian credit cards** including Visa/MC/Amex/JCB/Diners/RuPay BINs |
| - **Indian-domain emails** (.in / .co.in) |
| - **Indian addresses** in short form |
| - **UPI IDs** across all 18 PSPs |
| - **AWS keys** in natural-language contexts (`my access key is AKIA...`) |
| - **Database connection strings** with embedded credentials |
| - **Indian passport, voter ID, GSTIN, PAN, Aadhaar, ABHA, bank account** patterns |
|
|
| ## Eval (431-case Janus DLP corpus, base model + this fine-tune) |
|
|
| | | Base `openai/privacy-filter` | This fine-tune | |
| |---|---|---| |
| | Overall F1 | 0.8557 | **0.9398** | |
| | Precision | 0.875 | 0.941 | |
| | Recall | 0.838 | 0.938 | |
| | p50 latency (T4) | 26 ms | 27 ms | |
|
|
| ### Largest gains over base |
|
|
| | Category | Base F1 | This F1 | |
| |---|---|---| |
| | ifsc | 0.545 | **0.938** | |
| | aws_keys | 0.667 | **0.968** | |
| | db_connection_strings | 0.737 | **1.000** | |
| | credit_card | 0.643 | **0.875** | |
| | email | 0.769 | **0.938** | |
| | address_in | 0.815 | **0.968** | |
| | upi_id | 0.800 | **0.968** | |
| | gstin | 0.875 | **1.000** | |
| | voter_id | 0.909 | **1.000** | |
| | passport_in | 0.968 | **1.000** | |
|
|
| Categories already strong in base (private_keys, multi_pii, iban, secrets_in_code, ssn, etc.) preserved. |
|
|
| ## Training |
|
|
| - 957 spans-format examples (770 train / 187 val) covering Indian PII templates + regression-protection negatives |
| - 3 epochs, batch 4, grad-accum 2, lr 1e-4, bf16 output |
| - Trained on a single Tesla T4 in 78 seconds total |
| - Best checkpoint: epoch 2 (val_loss 0.029, val_token_accuracy 98.83%) |
| |
| ## Usage |
| |
| ```python |
| from opf._api import OPF |
|
|
| opf = OPF(model="janus-ai/privacy-filter-india-v2", device="cuda") |
| result = opf.redact("my Aadhaar 234567890123 IFSC HDFC0001234 password Admin#123") |
| print(result.redacted_text) |
| # my Aadhaar <ACCOUNT_NUMBER> IFSC <ACCOUNT_NUMBER> password <SECRET> |
| ``` |
| |
| Drop-in replacement for the base `openai/privacy-filter` checkpoint — same tokenizer, same architecture, same 8-label space. |
| |
| ## Limitations |
| |
| - Trained primarily on English + Hinglish-context Indian PII. Non-Indian addresses/names rely on base-model coverage. |
| - Date-of-birth detection (`private_date`) sits at F1 ~0.85 — context-gating could help. |
| - 18 false positives across 431 cases (4.4% FP rate). |
|
|
| ## License |
|
|
| Apache 2.0 — inherited from `openai/privacy-filter`. |
|
|
