Datasetpublic

AmareshHebbar/nine_star_ki-sft

Nine Star Ki (Japanese Kyusei) SFT Dataset Birth-year Main Star (1-9) via the standard digit-reduction formula, using the same Li-Chun-adjusted year boundary as BaZi. Total examples 100000 Train / Validation / Test 89876 / 5124 / 5000 Question types 2 Avg citations per example 1.0 Zero-citation examples 0 Computation Deterministic arithmetic (digit-sum reduction of the solar-year-adjusted birth year). Validation status — read… See the full description on the dataset page: https://huggingface.co/datasets/AmareshHebbar/nine_star_ki-sft.

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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Dataset Card

Nine Star Ki (Japanese Kyusei) SFT Dataset

Birth-year Main Star (1-9) via the standard digit-reduction formula, using the same Li-Chun-adjusted year boundary as BaZi.


Total examples	100000
Train / Validation / Test	89876 / 5124 / 5000
Question types	2
Avg citations per example	1.0
Zero-citation examples	0

Computation

Deterministic arithmetic (digit-sum reduction of the solar-year-adjusted birth year).

Validation status — read before trusting this at scale

Formula is the standard, widely-documented Main Star calculation. NOT independently cross-checked against a live reference calculator in this build environment — unlike BaZi's year pillar, I do not have independently-memorized known-correct Nine Star Ki values to spot-check against. Treat with more caution than the BaZi/Chinese Zodiac cards until you verify a sample yourself.

Schema

Field	Type	Description
`example_id`	string	`{synthetic_id}_{question_type}`
`synthetic_id`	string	ID of the underlying synthetic input
`system`	string	`"nine_star_ki"`
`facts`	dict	Full computed output for this system (chart/pillars/numbers depending on system)
`retrieved_rules`	list[dict]	Rules/observations matched for this input
`question_type`	string	See distribution below
`user_question`	string	Synthetic user question
`response`	string	Grounded reading — every claim traces to `facts` or `retrieved_rules`
`citations`	list[string]	Which rules the response draws on
`source`	string	`"rule_matched"` for all rows in this version

Question type distribution: general: 50000, yearly_outlook: 50000

Usage

python

from datasets import load_dataset

ds = load_dataset("YOUR_USERNAME/nine_star_ki-sft")
print(ds)

ex = ds["train"][0]
print(ex["user_question"])
print(ex["response"])

What this dataset does NOT claim

Like every dataset in this collection, this teaches a model to narrate a correctly-computed Nine Star Ki (Japanese Kyusei) reading using its own traditional rules faithfully and with citations. It does not, and cannot, contain any ground truth about whether any prediction comes true — no such data exists for any divination system. See the companion repo's top-level README for the shared "what these datasets don't claim" statement that applies across the whole collection.

Known limitations

—Only the Main Star (birth-year) is computed. Character Star (month-based) and Spirit Star (derived) — both standard parts of a full Nine Star Ki profile — are NOT implemented in this version.
—No directional/Feng Shui application (Nine Star Ki is often used for favorable/unfavorable directions by year, not implemented here).

Companion repo

Full generation pipeline (8 systems, shared scripts) — for regenerating at larger scale or extending the rule set — is the companion GitHub repo astrology-datasets (update this link once pushed).

License

Apache 2.0. All computed data is synthetic (no real people). Rule/effect text is written in-house, paraphrasing widely known traditional principles, not reproduced from any single copyrighted source.