# Evaluation pipelines

> An Evaluation pipeline is used to evaluate a trained ML model.

An Evaluation pipeline is used to evaluate a trained ML model.

## Evaluate a trained model

Configure the evaluation pipeline as follows:

* In the **Pipeline type** field, select **Evaluation run**.
* In the **Choose package** field, select the package you want to evaluate.
* In the **Choose package major version** field, select a major version for your package.
* In the **Choose package minor version** field, select a minor version you want to evaluate.
* In the **Choose evaluation dataset** field, select a representative evaluation dataset.
* In the **Enter parameters** section, there is one environment variable is relevant for Evaluation pipelines you could use:
* `eval.redo_ocr` which, if set to **true**, allows you to rerun OCR when running the pipeline to assess the impact of OCR on extraction accuracy. This assumes an OCR engine was configured when the ML Package was created.
* The **Enable GPU** slider is disabled by default, in which case the pipeline is runs on CPU. We strongly recommend that Evaluation pipelines run only on CPU.
* Select one of the options when the pipeline should run: **Run now**, **Time based** or **Recurring**.

  ![Screenshot of the Create new pipeline run interface.](https://dev-assets.cms.uipath.com/assets/images/document-understanding/document-understanding-screenshot-of-the-create-new-pipeline-run-interface-118113-77b9e8c4-a7f9a136.webp)

After you configure all the fields, select **Create**. The pipeline is created.

## Artifacts

For an Evaluation Pipeline, the **Outputs** pane also includes an **artifacts** / **eval_metrics** folder which contains two files:

![Screenshot of the Output artifacts interface.](https://dev-assets.cms.uipath.com/assets/images/document-understanding/document-understanding-screenshot-of-the-output-artifacts-interface-119385-3c9ec68c-7dba13f6.webp)

* `evaluation_default.xlsx` is an Excel spreadsheet with three different sheets:
* The first sheet presents a summary of the overall scores and the scores per batch, for each field, Regular, Column, and Classification fields. A percentage of the perfectly extracted documents is also provided for both per batch and overall documents.
* The second sheet presents a side by side, color coded comparison of Regular Fields, for increasing document accuracy. The most inaccurate documents are presented at the top to facilitate diagnosis and troubleshooting.
* The third sheet presents a side by side color, coded comparison of the Column Fields. All scores presented in the Excel file represent accuracy scores.
* `evaluation_metrics_default.txt` contains the F1 scores of the predicted fields.