vpermilp/lid-tyv

spellman LID corpus — tyv

182,189 rows / 65.9M chars of tyv-language text with provenance, built for training and evaluating the spellman language detector. Rows are single-line texts: news articles and library works (paragraph/sentence-group sized) plus Telegram/VK community posts and comments (wild register — emoji, hashtags, code-switching artefacts, folk spellings kept on purpose).

Row length: median 173 chars (p10 48, p90 743).

Configs

config	contents	rows
moderated (default)	news · library · wiki — edited, published sources	59,816
wild	tg-post · tg-comment · vk-post · vk-comment — Telegram and VK community posts and comments	122,373
all	union of both	182,189

from datasets import load_dataset
moderated = load_dataset("vpermilp/lid-tyv")            # default config
wild = load_dataset("vpermilp/lid-tyv", "wild")

Columns

column	meaning
text	the row (single line, whitespace-flattened)
lang	ISO 639-3 (tyv)
source	site store or Telegram channel handle
register	news · library · wiki · tg-post · tg-comment · vk-post · vk-comment
date	publication date when the source exposed one (YYYY-MM-DD)
id	source URL (crawl) or message/post id (Telegram, VK)

Registers

register	rows
vk-post	118,270
news	59,816
tg-post	3,460
tg-comment	643

Sources

source	register	rows
choon_hemchik_kojuun	vk-post	62,930
shyn.ru-tyv	news	59,816
hemhcik	vk-post	20,113
erzin_podslushano2025	vk-post	17,951
tmgnews17	vk-post	7,065
tmgnews	tg-post	3,107
tuva24	vk-post	2,986
tuva_khuresh_official	vk-post	2,850
erzinkozhuun2022	vk-post	2,103
tyvvam	vk-post	1,612
tmgnews	tg-comment	618
tovsp_tuva	vk-post	603
Tuva_Accordtar	tg-post	197
tuvinskiepesni	tg-post	140
vtuve17rus	vk-post	57
Tuva_Accordtar	tg-comment	25
tyva_dyl	tg-post	10
tuvayrylar	tg-post	6

How it was built and cleaned

1. Orthographic gate — every row carries letters that never occur in Russian orthography (e.g. Sakha ө ү ҥ ҕ һ), with a per-language marker-density floor; Russian-function-word share gates code-switched rows, which are split into native sentence runs rather than dropped.
2. Structural cleaning — site chrome and per-channel boilerplate (lines repeated across ≥8 posts), self-promo tails, TV schedules, Telegram thread roots (auto-forwarded post copies) removed.
3. Structural repairs — soft hyphens removed and print-layout splits rejoined (hyphenated / lowercase-continued paragraphs merged in page order); site breadcrumbs, TV schedules, weather tables, chord sheets, sponsor tails and letter-spacing artefacts dropped; Latin h/x homoglyphs inside Cyrillic words normalized (744 rows dropped); long rows must carry marker letters in ≥2 tokens (1,030).
4. Content-free filter — emoji/URL/number-only rows, Latin-dominant or mojibake rows, HTML-entity leftovers dropped (204).
5. Cross-source dedup on a normalized key (casefold; URLs, handles, hashtags, punctuation, digits stripped) — catches site↔channel cross-posts and reposts (5,268 removed).
6. PII masking — phone numbers and e-mails replaced by <phone> / <email>; no author identities were ever stored.
7. Model judge — rows the current spellman model contradicts at ≥0.9 confidence outside the language's close-pair group are dropped (9).

No train/validation/test split is shipped: the spellman pipeline assigns splits content-addressed (crc32) at mix time.

License

The compilation, cleaning and annotations are released under the MIT license. Row texts are redistributed from the public sources listed above (newspapers, libraries, public Telegram channels and VK communities) for language-identification research; each row carries its source and id so upstream attribution is always recoverable.

Built 2026-08-30T12:26:54+00:00.