detectLanguage

Decide the dominant language of a piece of text by character ratio. Pure statistics — no model, no dictionary. Use it to branch on "which tokenizer / which language-specific model / which typographic metrics".

API

detectLanguage(text, sampleSize?)

Parameter	Description	Type	Default
text	Text to inspect	string	Required
sampleSize	Characters to sample	number	20000

Returns 'zh' \| 'en' \| 'other'.

navigatorLanguage()

The browser's UI language mapped to the same three buckets — the default when there is no content to inspect. Returns 'other' under SSR.

Example

import { detectLanguage, navigatorLanguage } from 'ranuts';

const lang = book.content ? detectLanguage(book.content) : navigatorLanguage();
const model = { zh: 'chapter-title-zh-v1', en: 'chapter-title-en-v1' }[lang];

Notes

1. Only the head is sampled. Prose language is consistent throughout; scanning a million-character book to learn what its first paragraph already tells you is waste.
2. Chinese text with some English stays Chinese. Latin has to clearly dominate (more than 3×) before the verdict flips to English — mixing English into Chinese is common, the reverse is not.
3. 'other' means "neither CJK nor Latin" — Japanese kana, Cyrillic, Arabic and digits-only text all land here. This is a coarse three-way bucket, not language identification.