Label X

LabelX provides a distributed model evaluation service,supporting multi-dimensional human evaluation of large language model generation quality,multimodal model understanding ability,recommendation system performance,other aspects,generating quantitative model performance reports

Professional labeling teams need efficient task allocation and revenue management tools. Label X supports the creation of on-chain labeling teams, and team managers can assign tasks in batches and set revenue sharing ratios.🪙🪙🙋‍♂️

Label X records every step of the process, from task publishing, distribution, labeling, verification to settlement, generating an immutable on-chain audit log. Regulatory bodies and third parties can verify the compliance of data processing at any time.🎛️

and buyers can obtain commercial usage rights to the data. This activates the liquidity of labeled data assets.

The value of labeled data has not been fully realized, and a large amount of high-quality labeled data remains idle. Label X builds an on-chain data trading market, where data owners can list their labeled datasets for trading👇

Label X builds a settlement layer based on Layer 2, achieving second-level transaction confirmation and near-zero gas fees. It supports micro-settlement based on single judgments, adapting to the characteristics of atomic labeling tasks.🧰

Label X employs a distributed task scheduling system, supporting the simultaneous distribution of millions of tasks to nodes worldwide for parallel processing. Task completion time is linearly related to the number of nodes, and processing capacity can be scaled up as needed.💎

This helps companies conduct AI data labeling business compliantly globally.

Cross-border data labeling faces complex regulatory constraints. Label X incorporates a global data compliance framework, supporting legal requirements such as local data storage, user data deletion rights, and data processing record retention.

Cross-border data labeling faces complex regulatory constraints. Label X incorporates a global data compliance framework, supporting legal requirements such as local #data storage, user data deletion rights, and data processing record retention.

In sensitive fields such as medical care and finance, the original data cannot leave the local environment. Label X employs a federated learning architecture, where annotation tasks are executed locally on the #data holder's premises, with only model parameters.

Label X integrates an active learning framework, enabling the model to automatically identify high-value, difficult examples and push them to the annotation network. The annotation results are fed back to the model in real time.🔌

#Label X provides a long-term, stable supply of distributed annotation services, supporting data iteration throughout the entire model lifecycle. Requesters can establish continuously updating, on-chain annotation workflows.💽

#Label X opens up channels for individual participation, enabling ordinary participants to contribute cognitive computing power and receive corresponding benefits. #AI infrastructure is no longer monopolized by a few institutions, thus democratizing #computing power.

Label X uses on-chain protocols to automatically match demanders and labelers, and completes accurate distribution based on dimensions such as ability tags, historical performance, region, and language, reducing communication costs.🎛️

General-purpose #AI models require labeled data from multiple fields, including medicine, law, industry, and vision. Label X aggregates cross-domain labeler resources to form a distributed professional labeling pool.

Label X adopts an architecture featuring off-chain distributed annotation coupled with on-chain result verification. The annotation process is performed locally, with only critical results recorded on-chain for evidentiary purposes and settlement.

Label X subjects core logic—such as pricing mechanisms, task allocation rules, and penalty conditions—to community-based governance via voting. Governance resolutions are automatically encoded into smart contracts for execution, thereby ensuring complete transparency of the rules

🧰For scenarios involving small datasets and vertical-specific domains, Label X provides lightweight annotation units that reduce data acquisition costs and enhance the efficiency of deploying AI models at the edge.

AI projects typically require significant engineering resources to integrate with annotation services. Label X provides standardized, open interfaces, enabling model training platforms to connect to distributed annotation networks with a single click.🛩️