ValidMind Library

Published

February 19, 2026

The ValidMind Library streamlines model development and validation by automating testing. Run tests, log those test results to the ValidMind Platform, and have fully supported drafts of documentation or reporting ready for you to fine-tune.

What is the ValidMind Library?

The ValidMind Library provides a rich collection of documentation tools and test suites, from documenting descriptions of your dataset to validation testing your models for weak spots and overfit areas.

ValidMind offers two primary methods for automating model documentation:

Generate documentation¹ — Through automation, the library extracts metadata from associated datasets and models for you and generates model documentation based on a template. You can also add more documentation and tests manually using the documentation editing capabilities in the ValidMind Platform.
Run validation tests² — The library provides a suite of validation tests for common financial services use cases. For cases where these tests do not cover everything you need, you can also extend existing test suites with your own proprietary tests or testing providers.

¹ ValidMind for model development

² ValidMind for model validation

The ValidMind Library is designed to be model agnostic. If your model is built in Python, the library provides all the standard functionality you may need without requiring you to rewrite any functions.

Key ValidMind concepts

model documentation: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses.

Within the realm of model risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model's application.

validation report: A formal document produced after a model validation process, outlining the artifacts, assessments, and recommendations related to a specific model's performance, appropriateness, and limitations. Provides a comprehensive review of the model's conceptual framework, data sources and integrity, calibration methods, and performance outcomes.

Within model risk management, the validation report is crucial for ensuring transparency, demonstrating regulatory compliance, and offering actionable insights for model refinement or adjustments.

documentation template

Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.

validation report template

Serves as a standardized framework for conducting and documenting model validation activities. It outlines the required sections, recommended analyses, and expected validation tests, ensuring consistency and completeness across validation reports. The template helps guide validators through a systematic review process while promoting comparability and traceability of validation outcomes.

ValidMind templates come with pre-defined sections, similar to test placeholders, including boilerplates and spaces designated for test results, evidence, or artifacts.

test: A function contained in the library, designed to run a specific quantitative test on the dataset or model. Test results are sent to the ValidMind Platform to generate the model documentation according to the template that is associated with the documentation.

Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.

metrics, custom metrics: Metrics are a subset of tests that do not have thresholds. Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered via the ValidMind Library to be used with the ValidMind Platform.

In the context of ValidMind's Jupyter Notebooks, metrics and tests can be thought of as interchangeable concepts.

inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

model: A single model that has been initialized in ValidMind with vm.init_model(). See the Model Documentation or the for more information.
dataset: Single dataset that has been initialized in ValidMind with vm.init_dataset(). See the Dataset Documentation for more information.
models: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom tests.
datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: [Run tests with multiple datasets(/notebooks/how_to/run_tests_that_require_multiple_datasets.ipynb)])

parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

test suite: A collection of tests which are run together to generate model documentation end-to-end for specific use cases.

For example, the classifier_full_suite test suite runs tests from the tabular_dataset and classifier test suites to fully document the data and model sections for binary classification model use cases.

Quickstart

After you sign up for ValidMind to get access, try our Jupyter Notebook quickstarts for model documentation or validation:

Quickstart for model documentation

Learn the basics of using ValidMind to document models as part of a model development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model.

Quickstart for model validation

Learn the basics of using ValidMind to validate models as part of a model validation workflow. Set up the ValidMind Library in your environment, and generate a draft of a validation report using ValidMind tests for a binary classification model.

End-to-end tutorials

Learn how to use the ValidMind Library with our high-level Jupyter Notebook courses covering usage of ValidMind for specific roles or concepts.

ValidMind for model development

Learn how to use ValidMind for your end-to-end model documentation process based on common model development scenarios with our ValidMind for model development series of four introductory notebooks:

1 — Set up the ValidMind Library

Get to know ValidMind by setting up the ValidMind Library in your own environment, and registering a sample binary classification model in the ValidMind Platform for use with this series of notebooks.

2 — Start the model development process

Learn to run and log tests with a variety of methods and in different situations with the ValidMind Library, then add the results or evidence to your documentation for the sample model you registered.

3 — Integrate custom tests

After you become familiar with the basics of the ValidMind Library, learn how to supplement ValidMind tests with your own and include them as additional evidence in your documentation.

4 — Finalize testing and documentation

Wrap up by learning how to ensure that custom tests are included in your model's documentation template. By the end of this series, you will have a fully documented sample model ready for review.

ValidMind for model validation

Learn how to use ValidMind for your end-to-end model validation process based on common scenarios with our ValidMind for model validation series of four introductory notebooks:

1 — Set up the ValidMind Library for validation

Get to know ValidMind by setting up the ValidMind Library in your own environment, and gaining access as a validator to a sample model in the ValidMind Platform for use with this series of notebooks.

2 — Start the model validation process

Independently verify the data quality tests performed on datasets used to train the dummy champion model using tests from the ValidMind Library, then add the results or evidence to your validation report.

3 — Developing a potential challenger model

After you become familiar with the basics of the ValidMind Library, use it to develop a potential challenger model and run thorough model comparison tests, such as performance, diagnostic, and feature importance tests.

4 — Finalize validation and reporting

Wrap up by learning how to include custom tests and verifying that all tests conducted during model development were run and reported accurately. By the end of this series, you will have a validation report complete with artifacts ready for review.

Learn how to use the ValidMind Library

Learn how to use the comprehensive out-of-the-box tests and test suites, and other features in the ValidMind Library that make it easy for you to automate building, documenting, validating your models and more.

How to run tests and test suites

ValidMind provides many built-in tests and test suites, which help you produce documentation during stages of the model lifecycle, where you need to ensure that your work satisfies regulatory and risk management requirements.

How to use ValidMind Library features

Browse our range of Jupyter Notebooks demonstrating how to use the core features of the ValidMind Library. Use these how-to notebooks to get familiar with the library's capabilities and apply them to your own use cases.

Try code samples by use case

Try our Jupyter Notebook code samples that showcase the capabilities of the ValidMind Library and cover a variety of sample use cases.

Code samples by use case

Examples that you can build on and adapt for your own usage include:

Work with model documentation

After you have tried out the ValidMind Library, continue working with your model documentation in the ValidMind Platform:³

³ Working with model documentation

Work with test results

Once generated via the ValidMind Library, view and add the test results to your model documentation in the ValidMind Platform.

Work with content blocks

Make edits to your model documents by adding or removing content blocks directly in the online editor.