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Datasets:
aneethsolomon
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PII_India

Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Hindi
Size:
100K - 1M
Tags:
pii
indian-pii
synthetic-data
maskara
ml-intern
Libraries:
Datasets
pandas
Polars
License:
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PII_India / README.md
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metadata 
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 

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 

@misc{maskara_dataset2026,
  title={Maskara Indian PII Dataset},
  author={Somu Kandula},
  year={2026},
  howpublished={\url{https://huggingface.co/datasets/somukandula/maskara-indian-pii-200k}}
}

Generated by ML Intern

This dataset repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.