Instruction-tuned checkpoint of Han2Han (han2han-ul2-base-1-it, step 43153). Han2Han is a 169M-parameter encoder-decoder model that learns script-invariant representations of Korean text: a document written in Hanja and its Hangul transcription land at the same point in embedding space. The recipe (jamo and character-level embedding fusion, morpheme-aware denoising, bidirectional Hanja-Hangul transcription) is described in the paper, accepted to Findings of EMNLP 2026. This repo holds the PyTorch weights, the SentencePiece tokenizer, and the modeling code needed to load them through the transformers Auto classes with trustremotecode=True. Training code, the Flax model, the Flax-to-PyTorch…
Independent publisher
Cellik Adams
cadazar
historical (korean) language modeling
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Models on Hugging Face1
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