Add dataset card

README.md

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+---
+license: mit
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- bash
+- shell
+- code
+- instruction-tuning
+- sft
+- command-line
+size_categories:
+- 10K<n<100K
+pretty_name: Bash Instruction-Tuning Dataset
+configs:
+- config_name: default
+  data_files: bash_dataset.jsonl
+---
+
+# Bash Instruction-Tuning Dataset (~55k)
+
+A synthetic instruction-tuning dataset that pairs natural-language requests with
+correct Bash solutions, built for fine-tuning small LLMs to translate plain
+requests into runnable shell commands, pipelines, and scripts.
+
+Each example is a chat conversation (system / user / assistant) plus two metadata
+fields (`category`, `utility`) for slicing and analysis.
+
+## Format
+
+One JSON object per line (`bash_dataset.jsonl`):
+
+```json
+{
+  "messages": [
+    {"role": "system",    "content": "You are a Bash expert."},
+    {"role": "user",      "content": "Show the last 20 lines of error.log."},
+    {"role": "assistant", "content": "tail -n 20 error.log"}
+  ],
+  "category": "single",
+  "utility": "tail"
+}
+```
+
+- `messages` — the training conversation. The system prompt is one of 5 equivalent
+  Bash-assistant prompts (rotated so the model doesn't overfit a single string).
+- `category` — `single` | `pipeline` | `script` (see below).
+- `utility` — the primary command of the solution (e.g. `grep`, `awk`, `find`,
+  `for`), used to enforce a per-command cap and to analyze coverage.
+
+Scripts (`category == "script"`) contain real multi-line Bash (JSON-escaped `\n`),
+typically with `set -euo pipefail`, functions, `getopts`, `trap` cleanup, loops,
+here-docs, etc.
+
+## Statistics
+
+| Metric | Value |
+|---|---|
+| Examples | 55,000 |
+| `single` (one-shot commands) | 22,000 (40%) |
+| `pipeline` (pipes, `&&`/`\|\|`, `$(...)`, `xargs`) | 19,250 (35%) |
+| `script` (multi-line) | 13,750 (25%) |
+| Distinct primary utilities | 89 |
+| Max share of any one utility | 4.00% (hard cap 2,200/util) |
+| Unique user (NL) strings | 92.9% |
+| Unique assistant (Bash) strings | 75.3% |
+
+**Validity**: 100% pass `bash -n` (0 syntax errors). Static analysis with
+shellcheck flags no warning-level findings on **96.3%** of a 4,000-example sample;
+almost all remaining findings are a single style nit (`SC2010`, `ls | grep`).
+
+**Coverage of system/info utilities** that small models often get wrong
+(counts include single + pipeline + script uses):
+
+| util | n | util | n | util | n |
+|---|---|---|---|---|---|
+| journalctl | 2200 | lsof | 648 | ss | 481 |
+| pstree | 753 | renice | 365 | nice | 293 |
+| vmstat | 288 | netstat | 234 | iostat | 183 |
+| w | 158 | dmesg | 154 | free | 90 |
+| uptime | 34 | hostname | 25 | | |
+
+(`uptime` / `hostname` are lower because their genuine idiomatic command space is
+small; they are covered with real flag variants rather than padded phrasings.)
+
+## How it was built
+
+Generated by a recipe engine (`generate.py`, included). Each recipe family emits
+`(request, command)` pairs by combining hand-written phrasing templates
+(imperative / question / casual) with realistic parameter pools (plausible file
+names, directories, ports, services, users, patterns). The generator enforces:
+
+- category quotas (40 / 35 / 25),
+- a 4% per-utility cap so no command dominates (a real failure mode of earlier runs),
+- minimum floors for the info/system utilities above,
+- SHA-1 deduplication of `(request, command)` pairs,
+- a `bash -n` syntax gate on every command before it is written.
+
+## Loading
+
+```python
+from datasets import load_dataset
+ds = load_dataset("json", data_files="bash_dataset.jsonl", split="train")
+print(ds[0]["messages"])
+
+# analyze by slice
+import collections
+print(collections.Counter(ds["category"]))
+print(collections.Counter(ds["utility"]).most_common(15))
+```
+
+The dataset viewer will expose `messages`, `category`, and `utility` as columns.
+
+## Intended use & limitations
+
+**Good for**: teaching a small model to map natural-language requests to correct
+single Bash commands, short pipelines, and small scripts — including the
+system/info utilities listed above.
+
+**Limitations (be aware before relying on it):**
+
+- **Synthetic / semi-templated.** Variety comes from recombining templates and
+  token pools, not from human authorship. Phrasing shapes repeat (e.g. most
+  script requests open with "Write a script that…"); ~25% of commands recur with
+  different phrasings.
+- **Validity ≠ full semantic correctness.** `bash -n` + shellcheck prove the
+  commands parse and are lint-clean; they do not prove every command perfectly
+  satisfies its request. Some pairs are plausible-but-approximate (e.g. an `awk`
+  column index assumes a particular log layout).
+- **Single-turn, one canonical answer.** No explanations, alternatives, negative
+  examples, or multi-turn dialogue.
+- **Not executed at scale on real Linux.** Many commands (`systemctl`,
+  `journalctl`, `apt`, `vmstat`, …) are idiomatic but were validated statically,
+  not run.
+
+## Files
+
+- `bash_dataset.jsonl` — the dataset (55,000 rows).
+- `generate.py` — the generator (reproducible; resumable via `progress.json`).
+- `README.md` — this card.