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A guy on r/MachineLearning roasted my training data in 2022, and I've labeled everything by hand since
He told me my scraped dataset had 30 percent duplicates and I was basically training my model on the same cat pictures over and over, so now I label 500 examples myself before I trust anything, and honestly the models got better, has anyone else found that small clean sets beat huge messy ones?
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