Hebrish/Example-Sentence-Bank

Example Sentence Bank

This is the Hebrish project's reference for the sentence dataset format. It is the example a new sentence dataset copies, and sentence.schema.json beside it is the checkable form. The sentences are invented and neutral; they teach nothing and are never trained on.

Topic datasets carry these columns as their sentences tab (2026-11-06): see Example-Audio-Sentences, which shows both tabs in one repo.

• Pair: Example-Audio-Sentences is the reference for the audio type (read takes).
• Standard, column meanings, naming, checks and the list of live datasets that differ: docs/dataset-format-standard.md in the ASR-Training repo.
• Checker: python3 scripts/check_dataset_format.py data/train.jsonl --type sentence (ASR-Training repo, stdlib only).
• Card layout (type and role badges, section order): docs/dataset-card-standard.md. This reference card is kept short on purpose.
• Website: hebrish.app

Layout

data/train.jsonl, data/test.jsonl, sentence.schema.json, this README. Name a real bank <Topic>-Sentence-Bank.

Columns

Required in every row: id, text, target_terms, categories, split, derivation, sentence_author, term_origins, pronunciation. Optional, present and null when unset: homophone_set, source_dataset, source_licence, source_url, source_date, source_authors, original_text, review_note. The rows here carry all of them.

Column	Meaning
id	p_ + first 10 hex of sha1 of the lowercased, whitespace-collapsed text; the same id the capture app and the audio sets' prompt_id use
text	the sentence exactly as read; punctuated, cased, numbers in digits, a statement
target_terms	words the sentence exists to teach ([] for general sentences)
categories	tags such as tech, numeric
split	train or test; term-disjoint
derivation	mined-verbatim, mined-edited, generated, borrowed or written
sentence_author	upstream dataset, model or person
term_origins	one per target term: daniel, claude or the upstream list
pronunciation	reading guide, [] by default, otherwise {term, say, note, status} (below)

The pronunciation object

One object per term whose reading is unclear. say is a stress respelling (syllables joined by hyphens, the stressed one in capitals, as in the Recorder's pron-variants.json). status is unreviewed, ai-reviewed or human-reviewed. A person can edit it and an AI agent can review it. It says how a term should be read; it is not a record of how a take was said (the audio datasets' hebrew_pronunciation and pronunciation_variants do that).

Attribution and licence

CC-BY-4.0. Every sentence was invented for this example.