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Related Questions
- What are some common metrics that data curation teams use to measure the effectiveness of their annotation processes?
- How can data curation teams ensure the quality and consistency of annotations across different annotators and tasks?
- What are some potential biases that can arise in annotation processes and how can teams mitigate them?
- How can data curation teams measure the impact of their annotation processes on downstream machine learning models?
- What strategies can data curation teams use to reduce annotation costs while maintaining data quality?
- How can data curation teams balance the need for high-quality annotations with the need for timely data availability?
- What are some best practices for communicating the challenges and complexities of annotation processes to stakeholders?
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