gaon12/sion_translate

KJ-X Korean–Japanese Translation

KJ-X is a custom PyTorch encoder-decoder model for bidirectional Korean↔Japanese translation. The uploaded release contains inference exports and its SentencePiece tokenizer; training datasets and training checkpoints are not distributed.

Files

• model_ema.pt: recommended FP32 EMA inference export
• model_int8.pt: smaller CPU-only dynamic INT8 export
• kjx.model, kjx.vocab, token_features.npz: tokenizer files

The model uses custom code from gaon12/sion_translate and is not a Transformers AutoModel checkpoint.

Usage

git clone https://github.com/gaon12/sion_translate.git
cd sion_translate
python -m pip install -e .
hf download gaon12/sion_translate --local-dir models/sion_translate

from kjx.inference import Translator

translator = Translator(
    "models/sion_translate/model_ema.pt",
    "models/sion_translate/kjx.model",
)
print(translator.translate(["안녕하세요."], target_language="ja", num_beams=4)[0])
print(translator.translate(["こんにちは。"], target_language="ko", num_beams=4)[0])

Use model_int8.pt for CPU inference with lower storage and memory requirements. Only load PyTorch pickle-based artifacts from repositories you trust.

Evaluation

The GitHub repository includes a small, independently authored diagnostic JSONL covering both directions and provides a common scorer for KJ-X, LibreTranslate, Papago, Google Translation, DeepL, M2M100 418M, and NLLB-200. No universal quality claim is made from that small set.

Limitations

• Machine translations can omit, add, or reverse meaning.
• Proper nouns, short context-dependent utterances, specialist terminology, and long inputs need human review.
• Do not use unreviewed output for medical, legal, safety-critical, or certified translation.
• The custom architecture requires the repository code; hosted Transformers inference is not available out of the box.

License

The original KJ-X code and uploaded model release are provided under the MIT License. This does not grant rights to user-provided inputs, generated service outputs, third-party software, or third-party models used only as comparison baselines.