AmareshHebbar/numerology_pythagorean-sft

Numerology (Pythagorean) SFT Dataset

Life Path (from DOB), Destiny/Expression, Soul Urge, and Personality numbers from name-letter arithmetic, standard Pythagorean A-Z=1-9 repeating table, Master Numbers 11/22/33 preserved.

Total examples	100000
Train / Validation / Test	89916 / 5104 / 4980
Question types	4
Avg citations per example	1.75
Zero-citation examples	0

Computation

Pure arithmetic — no astronomy, no external library. Fully deterministic and directly auditable (every value is checkable by hand from the name string).

Validation status — read before trusting this at scale

Arithmetic spot-checked by hand for a real generated example (Life Path for 1967-10-28: 1+9+6+7=23->5, 1+0=1, 2+8=10->1, 5+1+1=7 -> matches engine output of 7). Letter-value table and reduction logic match standard documented Pythagorean numerology practice.

Schema

Field	Type	Description
example_id	string	{synthetic_id}_{question_type}
synthetic_id	string	ID of the underlying synthetic input
system	string	"numerology_pythagorean"
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: 25000, career: 25000, relationships: 25000, full_profile: 25000

Usage

from datasets import load_dataset

ds = load_dataset("YOUR_USERNAME/numerology_pythagorean-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 Numerology (Pythagorean) 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

• Uses synthetic, non-identifying names from a small fixed pool (common first/last names across several cultural backgrounds) — not real people, and not a large enough pool to avoid repeated names at high row counts (see companion repo common/name_pool.py to extend it).
• No compound-number interpretation (e.g. 16, 19 read differently before reduction in some traditions) — only the final reduced digit (or Master Number) is used.

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.