As the maintainer of ml‑assert, I’m looking to expand and solidify our library through testing and documentation enhancements. We welcome contributors to help with the following:
✅ 1. Add/expand pytest unit tests
Cover key assertion functions—e.g., edge cases, expected failures, valid inputs.
Include ML‑specific scenarios like tensor shape mismatches or floating‑point tolerance.
Ensure CI catches regressions.
✍️ 2. Improve documentation or user guide
Provide clear examples: how to pip install, import, and use typical functions in pipelines.
Showcase common workflows (e.g. comparing arrays, validating model outputs).
Highlight troubleshooting tips or performance notes.
🎯 3. Add badges/configs (optional but valuable)
Integration with CI (GitHub Actions): tests + lint.
Coverage reports (e.g. Codecov).
Docs site readiness, if applicable.
As the maintainer of ml‑assert, I’m looking to expand and solidify our library through testing and documentation enhancements. We welcome contributors to help with the following:
✅ 1. Add/expand pytest unit tests
Cover key assertion functions—e.g., edge cases, expected failures, valid inputs.
Include ML‑specific scenarios like tensor shape mismatches or floating‑point tolerance.
Ensure CI catches regressions.
✍️ 2. Improve documentation or user guide
Provide clear examples: how to pip install, import, and use typical functions in pipelines.
Showcase common workflows (e.g. comparing arrays, validating model outputs).
Highlight troubleshooting tips or performance notes.
🎯 3. Add badges/configs (optional but valuable)
Integration with CI (GitHub Actions): tests + lint.
Coverage reports (e.g. Codecov).
Docs site readiness, if applicable.