---
license: cc-by-4.0
task_categories:
- translation
language:
- hi
tags:
- transliteration
- romanization
- hindi
- hinglish
- input-method
size_categories:
- 100K<n<1M
---

# Hinglish -> Hindi Transliteration Dataset (sloppy roman input)

Parallel data for training a **romanized-Hindi -> Devanagari** transliteration
model that tolerates the messy, inconsistent way people actually type Hindi on
phones. Built to power a smart Hindi input method (IME), analogous to Chinese
smart-pinyin engines.

Each row is one `(roman, devanagari)` pair: `roman` is a plausible sloppy
human romanization, `devanagari` is the correct standard Hindi.

## Columns
- `roman` — model input, sloppy romanized Hindi
- `devanagari` — model target, correct Devanagari
- `count` — occurrences of this exact pair across generation (common typo
  patterns have higher counts; usable as a sampling weight)
- `mode` — provenance stream (see below)
- `persona` / `register` — typing-style and code-switch metadata (nullable)

## Provenance (`mode`)
- **chatroman** — realistic WhatsApp-style conversations generated in
  Devanagari, then romanized via a numbered ID-echo protocol so the
  Devanagari is never model-invented.
- **sentence** — real sentences from the Leipzig `hin_news_2011_1M` corpus
  (CC BY), romanized via the same ID-echo protocol. Ground-truth Hindi.
- **surgical** — a hand-curated priority vocabulary (loanwords, confusable
  near-homophones, high-frequency function words, kinship/number/date terms)
  with many romanization variants each, to anchor the hardest short-word cases.
- **scenario** — legacy single-pass conversational data (small remainder).

Romanizations were produced by Sarvam's Indic LLM across 10 typing personas
(fast-thumbs, dropped-schwa, gen-z abbreviator, boomer-formal, etc.) and 3
registers (pure Hindi, light/heavy code-switch). Correct Hindi ground truth
comes only from the Leipzig corpus and the curated list; the LLM authored
casual conversational Hindi for the chatroman stream, validated for script and
structure. About 13% of rows retain in-line English words on the Devanagari
side (real code-switch, kept intentionally).

## Splits
`train` / `validation` (validation is a small random holdout for loss tracking;
it is NOT a clean transliteration benchmark — use Dakshina or Aksharantar for
held-out evaluation).

## Cleaning
Line-level validation during generation: script/charset checks, digit and
garbage rejection, ID-echo word-count parity (anti-invention), and per-mode
quarantine with reason codes. Exact-duplicate pairs collapsed with a `count`.

## Intended use
Training small char-level seq2seq transliteration models for Hindi IMEs.
Not a general MT dataset.