> For the complete documentation index, see [llms.txt](https://tagger.gitbook.io/tagger-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://tagger.gitbook.io/tagger-documentation/tokenomics/task-reward-calculation/ai-copilot-labeling.md).

# AI Copilot Labeling

Tagger builds pre-trained AI copilot labeling models, allowing users to annotate industry-specific data with minimal effort - just a simple mouse click. As users contribute labeled data, the AI copilot continuously refines its detection capabilities, integrating new inputs to enhance accuracy. Over time, this iterative reinforcement pushes the model toward self-sufficiency, reducing reliance on manual intervention. The result: a self-improving system that moves ever closer to full autonomy.

**Reward Calculation Formula**\
\&#xNAN;*$TAG = single task reward × halving coefficient × account level coefficient × mask accuracy coefficient*

**Single Task Reward**\
The single task reward for AI copilot labeling remains relatively consistent across data catalogs, as the labeling process tends to have a similar level of difficulty.

**Account Coefficient**\
Refer to the "How to Earn $TAG" section.

**Halving Coefficient**\
Refer to the "Token Distribution" section.

**Mask Accuracy Coefficient**

| Mask Accuracy Coefficient | Mask Accurac % (x) |
| ------------------------- | ------------------ |
| 0                         | x < 85%            |
| 0.4                       | 85% ≤ x < 90%      |
| 0.8                       | 90% ≤ x < 95%      |
| 1.0                       | 95% ≤ x < 97%      |
| 1.5                       | x ≥ 97%            |


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