feat: Enable unit tests for dataset presets

[attafosu]

What does this PR do?

Adds unit tests for dataset presets

Type of change

• Bug fix
• New feature
• Documentation update
• Refactor/cleanup

Related issues

Testing

• Tests added/updated
• All tests pass locally
• Manual testing completed

Checklist

• Code follows project style
• Pre-commit hooks pass
• Documentation updated (if needed)

[github-actions]

MLCommons CLA bot All contributors have signed the MLCommons CLA ✍️ ✅

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a dedicated unit testing framework for dataset presets. Its primary goal is to ensure the reliability and correctness of data transformations applied by these presets, without the overhead of full end-to-end benchmark runs. This enhancement will significantly improve the development workflow by providing immediate feedback on preset changes and reducing the risk of introducing regressions in data preparation logic.

Highlights

• New Documentation: Added a new documentation file, DATASET_PRESET_TESTING.md, which provides a comprehensive guide on how to run and contribute unit tests for dataset presets.
• Unit Test Implementation: Implemented extensive unit tests for several dataset presets, including CNNDailyMail, AIME25, GPQA, LiveCodeBench, and OpenOrca, to validate their correct instantiation, data transformation logic, and expected output formats.
• Slow Test Marking: Introduced the @pytest.mark.slow decorator for tests that depend on heavier libraries like transformers, enabling selective execution and faster test runs when excluding these marked tests.

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[gemini-code-assist]

Code Review

This pull request is a great addition, enabling unit tests for dataset presets and providing clear documentation. The tests cover several presets and verify key aspects like instantiation and transform application. My review includes a few suggestions to improve the test suite's efficiency by reducing redundant computations, ensure consistency in marking slow tests, and enhance test coverage for one of the presets. Overall, this is a valuable contribution to the project's test infrastructure.