---
title: Evaluating dataset quality | Adaption
description: Read evaluation status and quality metrics for a processed dataset.
---

After an adaptation **run finishes successfully**, the platform can produce **evaluation signals**—scores and related metrics that summarize how the augmented data compares to the original on quality dimensions the pipeline measures.

In the Python SDK you retrieve that information in two complementary ways:

- **`datasets.get_evaluation(dataset_id)`** — dedicated response with **evaluation pipeline status** and structured **`quality`** metrics.
- **`datasets.get(dataset_id)`** — full dataset record including **`evaluation_summary`**, a compact mirror of the headline metrics when evaluation has finished.

See **DatasetGetEvaluationResponse**, **Quality**, and **EvaluationSummary** in the [API Reference](/api/index.md) for every field and type. Evaluation runs on its own schedule relative to the adaptation job; **`quality`** stays empty until evaluation reaches a completed state.

## Fetch evaluation results

Call **`get_evaluation`** with the same **`dataset_id`** you adapted:

```
ev = client.datasets.get_evaluation(dataset_id)


print(ev.status)  # pending | running | succeeded | failed | skipped
if ev.quality:
    print(f"Score before: {ev.quality.score_before}")
    print(f"Score after:  {ev.quality.score_after}")
    print(f"Improvement:  {ev.quality.improvement_percent}%")
```

When **`status`** is **`succeeded`**, **`quality`** includes fields such as **`score_before`** / **`score_after`** (0–10 scale), **letter grades**, **`improvement_percent`**, and **`percentile_after`** where applicable. If evaluation is still **`pending`** or **`running`**, expect **`quality`** to be absent until the pipeline finishes.

## Quick view on the dataset record

**`datasets.get`** returns a **`Dataset`** whose **`evaluation_summary`** is populated when a compact summary is available—useful for dashboards or listing datasets without a second request:

```
ds = client.datasets.get(dataset_id)
if ds.evaluation_summary:
    print(ds.evaluation_summary.score_after, ds.evaluation_summary.improvement_percent)
```

**`get_status`** focuses on ingestion/run progress and does **not** include evaluation; use **`get`** or **`get_evaluation`** when you care about quality metrics.

## Poll until evaluation finishes

Adaptation may show **`succeeded`** before evaluation is done. Poll **`get_evaluation`** (or **`get`** if you only need **`evaluation_summary`**) until **`status`** is no longer **`pending`** or **`running`**:

```
import time


while True:
    ev = client.datasets.get_evaluation(dataset_id)
    if ev.status in ("succeeded", "failed", "skipped"):
        break
    time.sleep(5)


if ev.status == "succeeded" and ev.quality:
    print(ev.quality.model_dump(exclude_none=True))
```

Adjust interval and timeout to match your environment (notebooks vs CI).

**Async** clients use the same shape: `await client.datasets.get_evaluation(dataset_id)`.

## Full example

After a completed run (see [Getting started](/introduction/getting-started/index.md)), pull evaluation:

```
import os


from adaption import Adaption


client = Adaption(api_key=os.environ["ADAPTION_API_KEY"])
dataset_id = os.environ["ADAPTION_DATASET_ID"]


ev = client.datasets.get_evaluation(dataset_id)
print(f"Evaluation status: {ev.status}")


ds = client.datasets.get(dataset_id)
print(f"Dataset status: {ds.status}")
if ds.evaluation_summary:
    print(f"Summary: {ds.evaluation_summary.model_dump(exclude_none=True)}")
```

## When to use it

Use **`get_evaluation`** when you need **explicit evaluation status** and full **`quality`** details. Use **`get`** when you already fetch the dataset and want a **single summary** on the same object. Pair either approach with **`estimate=True`** on future runs (see [Processing large datasets](/guides/processing-large-datasets/index.md) and the FAQ) when you are iterating on quality before scaling row counts.