| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - shell |
| - command-correction |
| - terminal |
| - synthetic |
| pretty_name: "Mash: mangled shell" |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # mash — mangled shell (training dataset) |
|
|
| ~42k pairs for training shell-command **correction** models: garbled or |
| natural-language input → the command the user meant. As of Aug 2026 this task |
| had no public dataset — everything nearby is NL→bash translation. Built for |
| [fash](https://github.com/lumpenspace/fash)'s local fixer model, |
| [mash](https://huggingface.co/lumpenspace/mash). |
|
|
| ## Format |
|
|
| mlx-lm chat JSONL (`train`/`valid`/`test`): |
|
|
| ```json |
| {"messages": [ |
| {"role": "system", "content": "Return the shell command the user most likely wants. Reply with the command only."}, |
| {"role": "user", "content": "gti pusj origin mian"}, |
| {"role": "assistant", "content": "git push origin main"} |
| ]} |
| ``` |
|
|
| ## Composition |
|
|
| Valid commands come from [tldr-pages](https://github.com/tldr-pages/tldr) |
| (CC-BY-4.0, ~30k canonical example invocations) and |
| [NL2Bash](https://github.com/TellinaTool/nl2bash) (MIT, ~12k expert |
| one-liners). Placeholders are filled from concrete scenarios, with descriptions |
| rewritten to match (so "open a specific file" becomes "open the settings file" |
| → `joe settings.json`). |
|
|
| A share of rows (~11%) carry a **context header** — `cwd:` plus a `files:` |
| directory listing — synthesized so exactly one plausible variant of the |
| referenced file exists; the answer must use the file that is actually there |
| ("open the settings file" → `joe settings.toml` when the listing has |
| `settings.toml`, not `settings.json`). The rest are bare, so models trained on |
| this data work with or without context. |
|
|
| Three pair types: |
|
|
| - **typo pairs** — commands corrupted by a weighted taxonomy of realistic |
| manglings: transposed/dropped/adjacent-QWERTY/doubled chars, `-`/`--` |
| confusion, merged words, stripped quotes, missing or spurious `sudo`, |
| copy-pasted `$ ` prompt prefixes, duplicated words, smart quotes/en-dashes, |
| plus curated high-frequency head typos (`gerp`, `sl`, `suod`, `dokcer`, …). |
| - **request pairs** — example descriptions, verbatim and "casualized" into the |
| terse phrasing people actually type. |
| - **identity pairs** (~5%) — correct→correct, so models learn not to |
| over-correct. Kept deliberately small: at ~12% a 1.5B run learned to echo |
| 39% of genuinely garbled inputs. |
|
|
| Generated deterministically by |
| [`training/build_dataset.py`](https://github.com/lumpenspace/fash/blob/main/training/build_dataset.py) |
| (seed 7). No user data or shell history is included. |
|
|
| ## Evaluation |
|
|
| Exact match on the held-out `test` split flatters models — it shares the |
| generator's bias. For real-world numbers use |
| [mash-bench](https://huggingface.co/datasets/lumpenspace/mash-bench), 203 |
| scenarios extracted from human breakage (NoFAQ, thefuck). |
|
|
| ## Attribution |
|
|
| Derived from [tldr-pages](https://github.com/tldr-pages/tldr) (CC-BY-4.0) and |
| [NL2Bash](https://github.com/TellinaTool/nl2bash) (MIT; Lin et al., LREC 2018). |
| Dataset licensed CC-BY-4.0 accordingly. |
|
|
