| --- |
| license: other |
| license_name: mixed-per-row |
| license_link: LICENSE |
| language: |
| - en |
| task_categories: |
| - automatic-speech-recognition |
| - text-generation |
| pretty_name: Linux Admin Sentence Bank |
| tags: |
| - asr |
| - fine-tuning |
| - read-aloud-sentences |
| - read-aloud |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.jsonl |
| - split: test |
| path: data/test.jsonl |
| --- |
| |
| # Linux Admin Sentence Bank |
|
|
| English sentences about Linux desktop and server administration, built around names a speech model writes as an ordinary English word: Konsole (console), Okular (ocular), Thunar (thunder), LUKS (looks), Btrfs, GRUB, Chrony (crony). Where natural, a sentence also uses the ordinary word, so the model has to tell them apart from context. |
|
|
| **Website: [hebrish.app](https://hebrish.app)**: get the Hebrish keyboard for Android and the desktop apps, see the results, and find every model, dataset and demo in one place. |
|
|
| These are **sentences to read aloud** for fine-tuning a speech-recognition model, not |
| recordings. They feed a targeted fine-tune of NVIDIA Parakeet TDT 0.6B v2 |
| ([current model](https://huggingface.co/danielrosehill/Parakeet-TDT-0.6B-v2-English-Hebrew-Codeswitching)). A capture app shows these sentences one at a time to be read aloud; every take lands in [Read-Aloud-Voice-Sentences](https://huggingface.co/datasets/danielrosehill/Read-Aloud-Voice-Sentences) and is copied into the matching audio subset. The recordings of these sentences are in |
| [Linux-Admin-Sentences](https://huggingface.co/datasets/danielrosehill/Linux-Admin-Sentences). |
|
|
| This dataset gathers sentences from several places into one file for my own training, and |
| it is reproduced here for convenience. Every row says where it came from, who wrote it and |
| how it was changed. Credit for upstream sentences belongs to their authors (see |
| "Attribution"). |
|
|
| ## Size |
|
|
| 317 train, 24 test (341 total). The test split is **term-disjoint**: |
| no target term in `test` appears in `train`, so an evaluation on it measures generalisation, |
| not memorisation. |
|
|
| Derivation: generated 341. |
| Sentence authors: `claude (Claude Code subagent)` 336, `claude-opus-5.5 (Claude Code session)` 5. |
|
|
| ## Columns |
|
|
| | Column | Meaning | |
| |---|---| |
| | `id` | sentence id: a hash of the text (same id as the capture app and the audio datasets' `prompt_id`) | |
| | `text` | the sentence, exactly as read. Punctuated and cased; numbers in digits for versions, sizes, percentages and money | |
| | `target_terms` | the words the sentence exists to teach | |
| | `term_origins` | for each target term, who proposed it: `daniel` (from my own repos and notes, or I asked for it), `claude` (an AI agent proposed it), or the public list it came from (`openrouter`, `github-topics`, `cncf`, `pypi`, `stackoverflow`) | |
| | `categories` | `tech`, `linux`, `smart_home`, `homophone`, `numeric`, … | |
| | `homophone_set` | homophone rows only: the set the context disambiguates | |
| | `split` | `train` or `test` | |
| | `derivation` | `mined-verbatim` (taken unchanged from a public dataset), `mined-edited` (taken, then lightly fixed for casing, digits or readability), `generated` (written by an LLM for this project, then reviewed by a second model), `borrowed` (taken whole from an earlier sentence set) | |
| | `sentence_author` | the upstream dataset, or the model that wrote the sentence | |
| | `source_dataset`, `source_licence`, `source_url`, `source_authors`, `source_date` | upstream provenance for mined and borrowed rows. `source_url` links the paper or post; `source_authors` lists arXiv authors (Reddit usernames are deliberately not copied) | |
| | `original_text` | the upstream text before editing (`mined-edited` rows) | |
| | `review_note` | the reviewer's note when a sentence was fixed | |
|
|
| ## Attribution and licence |
|
|
| Licensing is per row (`source_licence`). Rows without one were written for this project and |
| are CC-BY-4.0. |
|
|
| | Upstream | Licence | |
| |---|---| |
| | (none: every row was written for this project) | CC-BY-4.0 | |
|
|
| Mined sentences were selected by `scripts/mine_tech_sentences.py` in the project repository: |
| they are 8–30 words long, contain at least one named tech term, and come from newer posts first. |
| An AI reviewer then kept or dropped each one and fixed casing and numbers. |
|
|
| ## Updating |
|
|
| Upstream sources can be re-mined, and new sentences are added as the project grows. Ids are |
| content hashes, so an edited sentence gets a new id. |
|
|
