Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Size:
100K - 1M
License:
| language: | |
| - en | |
| - hi | |
| license: mit | |
| task_categories: | |
| - token-classification | |
| task_ids: | |
| - named-entity-recognition | |
| size_categories: | |
| - 100K<n<1M | |
| tags: | |
| - pii | |
| - indian-pii | |
| - synthetic-data | |
| - maskara | |
| - ml-intern | |
| # Maskara Indian PII Dataset (Phase 2) | |
| Synthetic Indian PII dataset for training the Maskara NER model. This version has been expanded and diversified for Phase 2 training. | |
| ## Dataset Description | |
| - **Repository:** `somukandula/maskara-indian-pii-200k` | |
| - **Version:** 2.0 | |
| - **Language:** English, Hinglish (Hindi-English code-mixed) | |
| - **License:** MIT | |
| - **Task:** Token classification / Named Entity Recognition (NER) | |
| ## Splits | |
| | Split | Rows | Purpose | | |
| |:---|---:|:---| | |
| | `train` | ~250,000 | Model training | | |
| | `template_disjoint_eval` | 15,000 | Generalization evaluation: entire template families held out | | |
| | `real_world_eval` | ~2,600 | Manually curated real-world Indian text evaluation | | |
| ## Schema | |
| | Column | Type | Description | | |
| |:---|:---|:---| | |
| | `id` | string | Unique example identifier | | |
| | `text` | string | Raw text containing PII | | |
| | `entities` | list[dict] | Character-level spans: `{start, end, label}` | | |
| | `domain` | string | Source domain (e.g. `whatsapp`, `email`, `json`, `ocr`) | | |
| | `locale` | string | Text locale (`en-IN`, `en-US`, `hi-IN`) | | |
| | `difficulty` | string | `easy`, `medium`, `hard`, `hard_negative` | | |
| | `generator` | string | Generator version (`maskara-v2`) | | |
| | `seed` | int | Per-example random seed | | |
| | `template_family` | string | Template family for traceability | | |
| ## Supported Entities (17) | |
| `ADDRESS`, `API_KEY`, `CREDIT_CARD`, `DATE_OF_BIRTH`, `DRIVER_LICENSE`, `EMAIL`, `IP_ADDRESS`, `PASSWORD`, `PERSON_NAME`, `PHONE`, `SSN`, `USERNAME`, `AADHAAR`, `PAN_CARD`, `PASSPORT`, `UPI_ID`, `VEHICLE_REG`. | |
| Note: `LOCATION` was removed in Phase 2. | |
| ## Diversity Improvements | |
| - **Templates:** 150+ multi-entity templates, 80+ single-entity templates. | |
| - **Formatting variations:** 6–10 per entity (plain, spaced, dashed, labelled, etc.). | |
| - **Hinglish / code-mixed:** ~40% of examples. | |
| - **Hard negatives:** ~15% of examples. | |
| - **Structured / noisy documents:** ~20% of examples. | |
| - **Domains:** WhatsApp, SMS, email, support tickets, HTML, Markdown, JSON, CSV, SQL, logs, OCR, bank statements, government forms, Aadhaar letters, PAN documents, resumes, configuration files. | |
| ## Template-Disjoint Evaluation | |
| The following template families are excluded from training and reserved for the `template_disjoint_eval` split: | |
| - `passport_application` | |
| - `transport_challan` | |
| - `json_payload` | |
| - `config_yaml` | |
| - `html_user` | |
| - `api_request_json` | |
| - `sms_link` | |
| - `finance_pipe` | |
| - `identity_kyc` | |
| - `medical_record` | |
| ## Real-World Evaluation | |
| A manually curated split containing realistic Indian text such as: | |
| - Customer support conversations | |
| - Hinglish chats | |
| - OCR-noisy documents | |
| - Government forms | |
| - JSON payloads | |
| - API request bodies | |
| - Log lines | |
| - Bank statements | |
| - Emails | |
| - Transport challans | |
| This split is never used during training. | |
| ## Usage | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("somukandula/maskara-indian-pii-200k") | |
| print(dataset["train"][0]) | |
| ``` | |
| ## Generation | |
| Generated using `maskara_dataset_generator.py` (Maskara Phase 2 generator). Each example is produced by filling placeholder-based templates with synthetic values and recording character-level entity spans. | |
| ## Phase 2 Error-Analysis Augmentation | |
| After the first Phase 2 training run, targeted synthetic examples were added for entities that underperformed on real-world and template-disjoint evaluation: | |
| - `CREDIT_CARD` | |
| - `PASSWORD` | |
| - `DRIVER_LICENSE` | |
| - `USERNAME` | |
| - `ADDRESS` | |
| - `IP_ADDRESS` | |
| - `PHONE` | |
| - `AADHAAR` | |
| - `PAN_CARD` | |
| These examples live in `phase2/targeted_augmentation.py` and are merged into the train split before retraining. | |
| ## Files in this repository | |
| | File | Description | | |
| |---|---| | |
| | `maskara_dataset_generator.py` | Full synthetic dataset generator | | |
| | `generate_maskara_phase2_dataset.py` | Script that generates and pushes all splits | | |
| | `phase2/targeted_augmentation.py` | Targeted augmentation for weak entities | | |
| | `phase2/train_maskara.py` | Training pipeline | | |
| | `phase2/evaluate_maskara.py` | Evaluation pipeline | | |
| | `phase2/error_analysis_loop.py` | Error analysis and actionable report generator | | |
| | `phase2/retrain_with_augmentation.py` | End-to-end retraining with augmentation | | |
| ## Citation | |
| ```bibtex | |
| @misc{maskara_dataset2026, | |
| title={Maskara Indian PII Dataset}, | |
| author={Somu Kandula}, | |
| year={2026}, | |
| howpublished={\url{https://huggingface.co/datasets/somukandula/maskara-indian-pii-200k}} | |
| } | |
| ``` | |
| <!-- ml-intern-provenance --> | |
| ## Generated by ML Intern | |
| This dataset repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. | |
| - Try ML Intern: https://smolagents-ml-intern.hf.space | |
| - Source code: https://github.com/huggingface/ml-intern | |