Rosetta-Ko Suite
Collection
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Datasets:
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rosetta-ko-law-synth-dpo
Korean legal-domain data grounded in national statutes — a cleaned CPT corpus plus source-grounded QA/preference/RLVR sets.
Synthetic data generated with the Qwen3.6-27B teacher model — part of the Rosetta-KO suite for the Rosetta Korean LLM (PoSTMEDIA).
Law Suite
Sibling datasets from the same pipeline (each a separate repo):
| repo | format |
|---|---|
rosetta-ko-law-synth-cpt |
continued-pretraining corpus (plain text) |
rosetta-ko-law-synth-sft |
supervised fine-tuning (user/assistant messages) |
rosetta-ko-law-synth-sft-think |
reasoning SFT (<think> chain-of-thought) |
rosetta-ko-law-synth-dpo (this repo) |
preference pairs (chosen/rejected) |
rosetta-ko-law-synth-rlvr |
reinforcement-learning-with-verifiable-reward rows |
Data Fields
{"id": "...", "source_dataset": "...", "domain": "law",
"prompt": "...",
"chosen": [{"role": "assistant", "content": "..."}],
"rejected": [{"role": "assistant", "content": "..."}]}
prompt(string) — the Korean questionchosen/rejected— assistant responses;chosenis the verified-correct / better-grounded answer
Usage
from datasets import load_dataset
ds = load_dataset("PoSTMEDIA/rosetta-ko-law-synth-dpo", split="train")
License
Released under the Apache License 2.0.
Source (Korean public data, KOGL Type 1):
- National Law Information Center (법제처 국가법령정보센터) — https://www.law.go.kr
Citation
@misc{rosetta_ko_law_synth,
title = {Rosetta-KO Law Synthetic Dataset},
author = {PoSTMEDIA},
year = {2026},
howpublished = {Hugging Face: PoSTMEDIA/rosetta-ko-law-synth-dpo}
}
Contact
PoSTMEDIA — https://huggingface.co/PoSTMEDIA
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