# `mix text.gen_golden_fixtures`
[🔗](https://github.com/kipcole9/text/blob/v0.6.2/lib/mix/tasks/text.gen_golden_fixtures.ex#L1)

Runs the canonical test inputs through the reference fastText
implementation and writes per-input top-K predictions to
`test/fixtures/golden_predictions.json`.

Acts as a thin wrapper over `priv/scripts/generate_golden_fixtures.py` so
the workflow is consistent with `mix text.download_lid176`. The wrapper
simply ensures the prerequisites exist and shells out to Python; it does
not embed any model logic itself.

## Prerequisites

* Python 3.8 or newer.

* The `fasttext` Python package: `pip install fasttext`.

* The `lid.176.bin` model file present in `priv/lid_176/` — run
  `mix text.download_lid176` first.

## Usage

    mix text.gen_golden_fixtures
    mix text.gen_golden_fixtures --top-k 10

## Options

* `--top-k N` — number of predictions to record per input (default 5).

* `--python PATH` — path to the Python interpreter (default: `python3`).

Any unrecognised options are forwarded to the underlying script
unchanged.

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*Consult [api-reference.md](api-reference.md) for complete listing*