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I'm doing multi-label classification on text data using BERT model. Since the dataset is huge, around 50 thousand rows, I was thinking to use stratify sampling on dataset to reduce it to around 2-4 thousand to hyperparameter tune on. I'm confused between trading off number of trails with size of sample set. Example: Would training 3000 rows with 5 trials will be better than training 1500 rows with 10 trials? Moreover, thinking if I should drop epoch from tuning and focus on learning rate and weight decay.

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