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Initial Model Training:
- Start with a small labeled dataset and train an initial model.
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Query Strategy:
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Use the model to predict on unlabeled data.
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Identify the most “informative” or “uncertain” data points (e.g., those the model is least confident about).
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Labeling:
- Send these selected data points to a human annotator for labeling.
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Model Update:
- Add the newly labeled data to the training set and retrain the model.
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Iteration:
- Repeat the process until the model achieves satisfactory performance or the labeling budget is exhausted.