Datasets:

Taimwe
/

kiribati-language-dataset

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
task: string
category: string
letter: string
ipa: string
kiribati: string
english: string
source: string
confidence: string
sg: string
pl: string
paradigm: struct<nominative: string, accusative: string, emphatic: string, genitive: string, poss_suffix: stri (... 3 chars omitted)
  child 0, nominative: string
  child 1, accusative: string
  child 2, emphatic: string
  child 3, genitive: string
  child 4, poss_suffix: string
row_label: string
proximal: string
medial: string
distal: string
relative: string
time: string
place: string
notes: string
value: string
with_ua: string
example: string
gloss: string
context: string
to
{'id': Value('string'), 'task': Value('string'), 'category': Value('string'), 'kiribati': Value('string'), 'english': Value('string'), 'ipa': Value('string'), 'gloss': Value('string'), 'context': Value('string'), 'source': Value('string'), 'confidence': Value('string'), 'notes': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              task: string
              category: string
              letter: string
              ipa: string
              kiribati: string
              english: string
              source: string
              confidence: string
              sg: string
              pl: string
              paradigm: struct<nominative: string, accusative: string, emphatic: string, genitive: string, poss_suffix: stri (... 3 chars omitted)
                child 0, nominative: string
                child 1, accusative: string
                child 2, emphatic: string
                child 3, genitive: string
                child 4, poss_suffix: string
              row_label: string
              proximal: string
              medial: string
              distal: string
              relative: string
              time: string
              place: string
              notes: string
              value: string
              with_ua: string
              example: string
              gloss: string
              context: string
              to
              {'id': Value('string'), 'task': Value('string'), 'category': Value('string'), 'kiribati': Value('string'), 'english': Value('string'), 'ipa': Value('string'), 'gloss': Value('string'), 'context': Value('string'), 'source': Value('string'), 'confidence': Value('string'), 'notes': Value('string')}
              because column names don't match

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Kiribati (Gilbertese) Language AI Learning Dataset

A structured, source-cited dataset for learning/teaching the Kiribati language (taetae ni Kiribati, ISO 639: gil / ki), arranged from the alphabet → phonology → grammar → numbers → vocabulary → example sentences / use cases. Built for AI tutoring, translation pairs, and fine-tuning.

🔒 Access control: this repository is gated with manual approval ("Manually approve" setting). Only users you personally approve can see/download the data. Everyone else must request access, and the decision is yours. The data is additionally protected by the custom Permission License in LICENSE: redistribution, inclusion in other public datasets, model training/publication, and commercial use all require your prior written permission.

Data

Single split, train, as JSONL (one record per line): data/train.jsonl — 279 records, 16 task types.

Field	Type	Description
`id`	string	Stable record ID (`kir-00001`…)
`task`	string	Record type (see below)
`category`	string	Curriculum section it came from
`kiribati`	string	Kiribati text
`english`	string	English translation/definition
`ipa`	string	IPA (alphabet records)
`gloss`	string	Word-by-word literal gloss (example sentences)
`context`	string	Situation (phrases)
`source`	string	Cited source(s)
`confidence`	string	`verified` (in fetched sources) / `common-unverified` (verify before training)
`notes`	string	Extra info

Task types & counts

word 125 · number 30 · phrase 30 · alphabet 23 · example_sentence 17 · interrogative 16 · pronoun 6 · verb_particle 6 · number_old_system 6 · numeral_classifier 8 · demonstrative 4 · adverbial_demonstrative 3 · reduplication 3 · article 1 · sample_text 1

Loading

from datasets import load_dataset
ds = load_dataset("YOUR_USERNAME/kiribati-language-dataset", split="train")
print(ds[0])  # {'id': 'kir-00001', 'task': 'alphabet', 'letter': 'A a', 'ipa': '/a/', 'kiribati': 'ataei', 'english': 'child', ...}

Raw JSONL also works with no dependencies:

import json
rows = [json.loads(l) for l in open("data/train.jsonl", encoding="utf-8")]

Curriculum (markdown study files)

File	Content
`01_overview_alphabet_phonology.md`	Overview, dialects, alphabet (23 letters, IPA, examples), phonology
`02_grammar.md`	Word order (VOS), articles, nouns/classes, pronoun tables, possession, demonstratives, adjectives, verbs & particles, reduplication, negation, copula/existential, conjunctions, prepositions, interrogatives
`03_numbers.md`	Numerals + classifier system, ordinals, fractions, old native large-number system
`04_vocabulary.md`	~125 curated core words in 11 themes with confidence tags
`05_examples_usecases.md`	Glossed example sentences, ~30 practical phrases, sample text, AI use cases
`build_dataset.py`	Reproducibly regenerates `data/train.jsonl` from the markdown files
`SOURCES.md`	Full source list + licensing notes

Method & provenance

Compiled from online research (Wikipedia CC BY-SA, Omniglot, Trussel.com/Peace Corps handbook via Wayback Machine, Internet Archive, ERIC). Every record carries its source.
Included: factual linguistic data — orthography, IPA inventory, grammar rules, pronoun paradigms, numerals, word–translation pairs, short phrases/sentences — re-organized and re-written with citation.
Intentionally NOT dumped verbatim: full texts of copyrighted references (Sabatier's dictionary, Trussel/Peace Corps handbook, Trussel–Groves dictionary). Bingham (1908) is public domain and is the recommended target for bulk expansion. See SOURCES.md.

Suggested AI uses

Tutoring agent following the curriculum order (alphabet → … → use cases).
Translation/eval from the phrase and example_sentence pairs.
Grammar drills: negation (aki), reduplication (habitual/continuative), passive (-aki), causative (ka-V-a), numerals with classifiers.

Gaps / next steps

Harvest the public-domain Bingham (1908) dictionary (~179 pp) for a full wordlist.
Add audio (Kaipuleohone archive, Univ. of Hawai'i).
Verify common-unverified items & NGG examples against Sabatier/Bingham.

Compiled 2026-10-15. The Kiribati Language Board is the normative authority for the standard language.

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