> ## Documentation Index > Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt > Use this file to discover all available pages before exploring further. # Test sets > Hand-labelled datasets used to evaluate fine-tuned metrics. A **test set** is the ground truth for a [training run](/luna-studio/ui/runs/lifecycle). After fine-tuning, Luna Studio scores the resulting metric against the test set and reports F1, AUC-ROC, and other diagnostics. ## What makes a good test set * **Hand-labelled.** Don't auto-generate test labels — they're the tape measure for evaluating the run. * **Representative.** Sample inputs from the same distribution your application sees in production. Skewed test sets lead to misleading scores. * **Small but not tiny.** Use at least 300 hand-labelled rows when possible. Beyond a few hundred rows, you start paying inference cost without much added signal for most metrics. * **Held out.** Don't reuse test set rows in your training set. Luna Studio respects this when you generate a training set from a test set. ## Required schema Test sets need at least two columns: | Column | Required | Notes | | ------- | -------- | ----------------------------------------------------------------- | | `input` | Yes | The text the metric scores. | | `label` | Yes | The ground-truth value. Type depends on the metric's output type. | Other columns (e.g. `id`, `timestamp`) are kept but ignored during evaluation. For label format by currently trainable [output type](/luna-studio/ui/core-concepts#metrics): | Output type | Acceptable label values | | ----------- | ----------------------------------- | | Boolean | `true` / `false` (or 1 / 0). | | Categorical | One of the metric's defined labels. | Floating-point, percentage, multilabel, and numeric labels are not trainable in Luna Studio yet. ## File formats * **CSV** — standard comma-separated. Headers required. * **JSONL** — one JSON object per line, with `input` and `label` keys. Both formats accept up to a few hundred MB. Larger uploads work but take longer to validate. ## Add a test set You can add a test set in three places: * The [Datasets page](/luna-studio/ui/datasets/overview) → **Add test set** primary button. * The [Step 2 of the run creation flow](/luna-studio/ui/runs/new-run/step-2-test-set) → dropdown's **Add new test set** action. * (Indirectly) by importing from Galileo — see [Galileo integration](/luna-studio/ui/integrations/galileo). All three paths open the same **Add test set** modal — see [Add a dataset](/luna-studio/ui/datasets/add-a-dataset) for the modal reference. ## Test/eval split When a test set is selected for a training run, Luna Studio reserves 80% for evaluation and uses 20% as seed examples if you choose [Generate from test set](/luna-studio/ui/runs/new-run/step-3-training-set#generate-from-test-set) for the training source. The **Selected dataset card** in [Step 2](/luna-studio/ui/runs/new-run/step-2-test-set#what-happens-after-you-select-a-test-set) shows the split, e.g.: > 320 rows · 3 columns · Uploaded · 80% → \~256 for eval ## Used in metric The **Used in metric** column on the Datasets table shows every metric whose runs reference this test set. If the column shows `—`, the test set is unused — safe to delete. ## Renaming a test set Open the test set's details page and click the pencil icon next to its name in the breadcrumb. The **Edit dataset name** modal opens with the current name pre-filled. ## Where to go next Walk through the Upload / URL / Galileo flows. What Luna checks and what to do when validation fails. The other dataset type — used to fine-tune the base model.