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| # Reproducing the experiments | |
| ## Browser | |
| Upload the repository files to one root folder in a Hugging Face Static Space. Open App. Models fit automatically (locally) to `train.json`. Open Overview, or Experiments → Run four baselines. Select challenge (54) or IID (130); enter a bootstrap seed. Download metrics JSON and prediction CSV. | |
| ## Node, no dependencies | |
| ```sh | |
| node --test test_engine.js | |
| node benchmark_cli.js | |
| ``` | |
| Both commands use only Node 18+ built-ins and the bundled dataset files. No Hugging Face token, Python packages or GPU needed. | |
| ## Browser test, optional | |
| ```sh | |
| python -m pip install playwright | |
| python -m playwright install chromium | |
| python test_browser.py | |
| ``` | |
| The test injects local project files directly into the Chromium page and mocks `fetch` *only for fixture-loading* to avoid external network dependency in restricted CI. This is not a test of deployed Hugging Face hosting. | |
| ## Interpreting results | |
| Use the fixed split names. Do not compare any archived Python-model evaluations as if they used the same code. Synthetic evaluations are not proof of generalization to real scholarly work. | |