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Related Questions
- What strategies can human annotators use to ensure data quality while still maintaining a large dataset size?
- How can annotators efficiently balance the trade-off between data quality and dataset size?
- What techniques can be employed to minimize errors in large-scale annotation while maintaining data quality?
- How can annotators prioritize data quality in the face of large dataset sizes and limited resources?
- What is the optimal balance between data quality and dataset size for a given machine learning project?
- Can human annotators use active learning techniques to select a representative subset of high-quality data for training models?
- How can annotators leverage data augmentation and generation techniques to increase dataset size while maintaining data quality?
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