Text Classification Model Training Data Verification

The system performs preliminary verification of training data samples before they are used for model training. A verification module checks whether each training sample is more similar to its intended class or to other classes, and automatically corrects misaligned samples by adding or removing words. This preliminary action ensures that only high-quality, accurately classified training data is used, improving classification accuracy without requiring more time for manual generation.

The verification module implements a feedback mechanism that automatically checks each training sample to determine whether it is more similar to its intended class or to other classes. When a sample is found to be misaligned (i.e., more similar to another class), the system provides feedback by automatically correcting the sample through word addition or removal. This closed-loop feedback ensures that training data maintains high classification precision and does not drift across class boundaries.

The system implements self-service by enabling automatic verification and correction of training data samples without requiring continuous human intervention. The verification module autonomously checks training samples, identifies misclassified samples, and corrects them by adding or removing words based on similarity metrics. This self-service capability significantly improves training data generation efficiency and reduces the time and cost associated with manual data preparation.