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Evaluation seeds are initial data points used to gauge performance metrics quickly in AI models. They're randomly selected samples fed into an algorithm to swiftly estimate how well a model will perform on diverse inputs.
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Evaluation seeds are short, specific metrics or criteria used to swiftly assess a project's success. They streamline decision-making by offering quick, actionable insights at each stage of evaluation.