license: cc-by-4.0
language:
- en
task_categories:
- text-generation
pretty_name: Example Sentence Bank (format reference)
size_categories:
- n<1K
tags:
- reference
- format-standard
- hebrish
configs:
- config_name: default
data_files:
- split: train
path: data/train.jsonl
- split: test
path: data/test.jsonl
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.