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Datasets:
IB-Emper
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hinglish-translit-v2

Tasks:
Translation
Modalities:
Text
Formats:
parquet
Languages:
Hindi
Size:
100K - 1M
Tags:
transliteration
romanization
hindi
hinglish
input-method
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metadata 
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.