> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sdvm.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Dataset conventions

> Declare formatting that is a convention, not a defect, so the audit does not flag it.

Some datasets have formatting that looks like a defect but is a deliberate convention: uniform lowercasing, markup tokens, contexts truncated by design. Without being told, the quality audit flags such formatting as a grammatical problem. `conventions` is a free-text description that tells the audit what to leave alone.

```python wrap theme={"theme":{"light":"material-theme-lighter","dark":"material-theme-darker"}}
import os

from sdvm import Auditor, Fixer

WIKIHOW = (
    "Bracketed markers like [header] [title] [step] are section labels, not errors; the "
    "text is uniformly lowercased, do not flag lowercasing; the context may end "
    "mid-sentence by design, do not flag it as incomplete."
)

audited = Auditor(api_key=os.environ["SDVM_API_KEY"]).run(samples, conventions=WIKIHOW)
fixed = Fixer(api_key=os.environ["SDVM_API_KEY"]).run(audited, conventions=WIKIHOW)
```

On HellaSwag's wikiHow text this takes the grammatical false-alarm rate from roughly 40% down to about 12%.

## Where it applies

* `Auditor.run(..., conventions=...)` for the quality audit.
* `Fixer.run(..., conventions=...)` for the routing audit, so a convention does not trigger a needless fix.
* `Refinery(conventions=...)` forwards it to every audit and fix stage.

## Writing a good description

* Be self-contained: the text is the only context the audit gets about your dataset.
* Name the convention and say what not to flag, as in the example above.
* Keep it to the formatting. Conventions are not a place to change what counts as a wrong answer.