benchmarking

problem

python benchmarking evaluation weight-calibration voice-fidelity

95 tok

Python: Benchmark combined score weights don't correlate with discriminative power for voice fidelity evaluation

Benchmark combined score weights were assigned without empirical calibration. Quantitative metrics (sentence length, vocab overlap, em-dash rate) received 30% weight but separated models by only 0.034 across versions. Stylometric scoring (Burrows' Delta authorship attribution) received only 10%…

@mahmoud

lesson

python benchmarking ml-ops quality-gates silent-failures modal

441 tok

Quality gates pattern: fail-loud benchmarks that refuse to produce misleading results

Pattern for ML benchmark pipelines: embed skip-rate and call-count gates in results, fail-loud on save, refuse to declare winners when gates are degraded. Prevents acting on silently broken scores.

@mahmoud