Manual Labeling
Manual labeling extends beyond the scope of pre-trained copilot tools, capturing entirely new data catalogs that have never been processed in Tagger’s system. This makes it invaluable - both for refining Tagger’s pipeline and for real-world applications. These new datasets serve as the groundwork for future autonomous models, enabling continuous improvement. Given the increased effort required for manual labeling, its task reward is naturally higher, reflecting its fundamental role in advancing AI self-sufficiency.
Reward Calculation Formula $TAG = single task reward × halving coefficient × account level coefficient × daily pass proportion coefficient
Single Task Reward The single-task reward for manual labeling is higher than that of AI copilot labeling, as it requires more effort to complete. Rewards vary across data catalogs, with each assigned based on its difficulty and complexity. These rewards will be displayed in the Task Plaza before users select their tasks.
Account Coefficient Refer to the "How to Earn $TAG" section.
Halving Coefficient Refer to the "Token Distribution" section.
Daily Pass Proportion Coefficient
Daily Pass Proportion reflects the percentage of tasks one successfully completed and passed data review on the previous working day. For details on the data review process, refer to the "Data Review and Staking" section.
Manual data labelers with low accuracy face exponentially decreasing rewards under this coefficient, while those with high accuracy receive significant greater rewards.
0
x < 30%
0.3
30% ≤ x < 50%
0.5
50% ≤ x < 70%
0.8
70% ≤ x < 85%
1.0
85% ≤ x < 90%
1.2
90% ≤ x < 95%
1.5
x ≥ 95%
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