Construct Scoring With Transformer HITL Core Set Learning

The patent applies preliminary action by pre-training the Transformer model on large amounts of unlabeled data before fine-tuning on labeled data. This pre-training phase prepares the model with general language understanding capabilities, so that when labeled data becomes available, the model can quickly adapt to specific tasks without requiring extensive annotation time. The core set construction method identifies and annotates only the most informative samples for fine-tuning, significantly reducing the time required compared to annotating all available data.

The patent extracts only the essential information needed for training by constructing a core set of representative samples from the larger dataset. Instead of using all available labeled data, the system identifies and selects a smaller subset of core samples that capture the most important patterns and relationships. This extraction approach reduces the annotation burden while maintaining model performance, as the core set contains the most informative examples for learning.

The patent extracts only the essential information needed for training by constructing a core set of representative samples from the larger dataset. Instead of using all available labeled data, the system identifies and selects a smaller subset of core samples that capture the most important patterns and relationships. This extraction approach reduces the annotation burden while maintaining model performance, as the core set contains the most informative examples for learning.

The patent uses copying by leveraging the pre-trained model's knowledge to generate synthetic training data or to transfer learned representations to new tasks. The pre-trained Transformer model can generate pseudo-labeled data that mimics the patterns of real labeled data, providing a cost-effective alternative to manual annotation. This copying approach allows the system to obtain training data at lower cost while maintaining the quality needed for accurate predictions.

The patent applies universality by training a single Transformer model on diverse, unlabeled data from multiple domains and tasks. This pre-trained model serves as a universal foundation that can be adapted to various specific tasks through fine-tuning on domain-specific core sets. Instead of requiring separate models for each task, the system uses one versatile pre-trained model that can handle multiple applications, reducing the complexity of data requirements for each individual task.

The patent applies preliminary action by pre-training the Transformer model on large amounts of unlabeled data from diverse sources before fine-tuning on specific tasks. This pre-training phase builds a universal knowledge base that encompasses multiple domains and task types. When the model needs to be adapted to a new task, it only requires fine-tuning on the core set for that specific task, rather than requiring complete retraining or task-specific data collection from scratch.