What relevance rank score range do most documents typically have in the early stages of an Active Learning project?

In the early stages of an Active Learning project, it is common for most documents to fall within a relevance rank score range of 40 to 60. This range reflects the initial uncertainty in the model's ability to effectively distinguish between relevant and irrelevant documents. During this phase, the algorithm is still learning from the training data, and many documents will typically have moderate scores due to the ongoing iterative learning process.

The importance of this range lies in the fact that it represents documents that have been identified as potentially relevant but may not yet have strong confidence from the model. These scores indicate the model is still refining its understanding and prediction capabilities. As more feedback is provided and the model continues to evolve through iterations, one would expect documents to eventually receive higher scores as the model becomes more adept at classifying them accurately.