Show HN: Context-aware Japanese furigana using Sudachi and ModernBERT
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What is Context-aware Japanese furigana using Sudachi and ModernBERT?
Context-aware Japanese furigana using Sudachi and ModernBERT is a digital product or tool described as:
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Data for Context-aware Japanese furigana using Sudachi and ModernBERT was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
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The initial public indexing or launch date for Context-aware Japanese furigana using Sudachi and ModernBERT within our tracked developer communities was recorded on May 29, 2026.
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Context-aware Japanese furigana using Sudachi and ModernBERT has achieved measurable traction, logging over 11 traction score and facilitating 7 recorded discussions or engagements.
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Community Voice & Feedback
Fantastic tool and love the delivery; no sign up required. Interested to hear how you pulled that off.Also interested to hear if you plan to eventually support an option to add pitch accent; I've never seen what training material exists for that or how that is supported in unicode.
It really works. Very cool. I’ve been looking for this kind of service for a long time since I started learning Japanese, and I’ve rarely been satisfied with the available services.
I built a context-aware furigana converter for Japanese text, files, and web pages.The main problem I wanted to solve was that simple dictionary-based furigana works well for common cases, but breaks on words where the reading depends on context:* 市場: いちば or しじょう* 大分: おおいた or だいぶ* 人気: にんき or ひとけ* 最中: さいちゅう or さなか or もなか* 方: かた or ほうThe engine is a hybrid system:* Sudachi for tokenization, base forms, POS, and candidate readings* Expanded dictionary coverage for compounds and fixed expressions* Custom rules for counters, suffixes, rendaku patterns, and phrase overrides* ModernBERT fallback for 144 especially context-dependent target wordsI have been testing it against an LLM-assisted benchmark of 7,500 Japanese lines. On the current benchmark, it gets about 12 wrong readings per 1,000 tokens. I treat that as a practical regression benchmark rather than a formal academic evaluation, but it has been useful for comparing versions and catching regressions.The hardest remaining cases are personal names, place names, rendaku, rare vocabulary, and domain-specific terms.I would especially appreciate examples where it gets the reading wrong, since those are the most useful for improving the system.
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