PolyFact

Parallel multilingual factual multiple-choice QA grounded in Wikidata. 100K facts × 12 languages, fully aligned by fact_id across all per-language configs.

Usage

from datasets import load_dataset

# One language at a time (SFT / eval)
ds = load_dataset("jvonrad/PolyFact", "en")
print(ds["train"][0])

# All languages aligned per fact (cross-lingual training)
par = load_dataset("jvonrad/PolyFact", "parallel")
print(par["train"][0]["translations"]["en"])

Schema

Per-language configs (ar, bn, de, en, es, fr, id, ja, pt, ru, sw, zh) — flat, one row per (fact, language):

Column	Type	Description
fact_id	string	Cross-language join key — <subject_qid>|<property_pid>|<object_qid>
language	string	ISO language code
subject	string	Subject entity label
relation	string	Relation name (e.g. educated at)
object	string	Object entity label (the canonical answer)
question	string	Natural-language question in language
option_a..option_d	string	Four multiple-choice options
answer_text	string	The correct option as text (matches one of option_a..option_d)
answer_index	int	0-based index of the correct option

For Wikidata Q-/P-ids (subject_id, property_id, object_id), load the parallel config and join on fact_id.

parallel config — one row per fact:

Column	Type	Description
fact_id, subject, subject_id, relation, property_id, object, object_id	—	Shared across languages; Wikidata grounding lives here
translations	dict	{lang_code: {question, option_a..d, answer_text, answer_index}}

Splits are parallel across languages: every fact_id in a split is present in all per-language configs.