Tagger Documentation
  • About Tagger
    • 🛸 Our Vision
    • 🔌 What We Do
  • Challenges in the Current Data Economy
    • 🌏 Chaotic Data Authentication
    • 📂 Difficulty in Data Acquisition
    • 📌 Quality of Labeled Data
    • 🪝 Data Silos
    • 🛡️ Privacy and Ethical Issues
    • 🧳 The Need for Continuous Maintenance
  • Our Solutions
    • 📃 Data Authentication Protocol
    • 🌲 A Full-Stack Decentralized AI Data Solutions Platform
      • Web 3 Crowdsourcing
      • Simple Onboarding, Instant Global Payments
      • DePIN-Based Data Collection and Sharing
      • AI Copilot Labeling Tool
      • Permissionless AI Marketplace
      • Data Developer Community
      • Human-In-The-Loop
  • Tagger Features
    • Data Authentication Protocol
    • Decentralized AI Data Collection
    • Decentralized AI Data Labeling
    • Data Evaluation, Cleaning, and Processing
    • Data Trading and Management
    • HITL Telegram Mini App
  • Hardware
    • ⌚ Health Monitoring Wristband
  • Tokenomics
    • ☑️ $TAG
    • 🪙 Token Distribution
    • 💡 Task Reward Calculation
      • AI Copilot Labeling
      • Manual Labeling
      • Data Review and Staking
      • 👥 Daily Task Bonus
  • Smart Contract and Audit
    • 📄 Audits
    • 🖼️ NFT Smart Contract
    • 🪙 Token Smart Contract
  • Roadmap
  • Team
  • Contact Us
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  1. Tagger Features

Data Evaluation, Cleaning, and Processing

Tagger employs a robust innovative data evaluation framework that continuously monitors and analyzes labeling performance using advanced statistical and computational techniques.

This framework employs a proprietary algorithm to identify variations in labeling accuracy. When significant deviations are detected, these data points undergo a rigorous review process. This process ensures accurate identification and understanding of anomalies, providing critical insights into the model's performance.

The identified data points are then utilized to incrementally retrain the pre-trained labeling model. This iterative process ensures continuous model evolution, adapting to new data patterns and improving overall accuracy and reliability. This method allows Tagger to maintain a high standard of data labeling with minimal human intervention.

This method represents an innovation in autonomous data processing, one where the reliance on extensive human labor is significantly diminished. This reduction in human involvement translates directly into cost savings and heightened efficiency. By automating critical components of the data cleaning process, Tagger not only streamlines operations but also challenges the existing paradigm, characterized by labor-intensive and error-prone structures, and ushers in a more efficient, reliable, and scalable model, driving significant economic potential, as clients and enterprises can respond more swiftly and accurately to market demands, operational challenges, and the exponential growth of data.

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Last updated 9 months ago