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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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In a sun-drenched barnyard, a young squire plucked seeds from an ancient chestnut tree, admiring their golden sheen. He swiftly packed them into a weathered wooden case, ready to sow his first crops for the harvest season's start.
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Retrieval seeds are like tiny, fast-acting keys for quick memory access, stored in our brains ready for instant recall. When needed, they swiftly unlock related information from vast mental libraries with just a glance or whisper of the question.