Under what condition is it appropriate to stop training your entities?

The rationale for stopping the training of entities primarily revolves around achieving satisfactory performance metrics. When both precision and recall reach acceptable levels, it indicates that the model is effectively balanced in terms of correctly identifying positive instances (precision) and capturing all relevant instances (recall). This balance is crucial, especially in contexts where false positives and false negatives have significant implications.

A high precision means that when the model predicts a positive outcome, it is likely to be correct, while a high recall means the model identifies most actual positive instances. Therefore, once these metrics meet pre-defined thresholds, you can reasonably conclude that the training process has yielded a model capable of performing its intended tasks effectively.

In contrast, a low F1 score, which is a harmonic mean of precision and recall, suggests that there may be issues with model performance, and thus stopping training at this point would not be appropriate. Training on an insufficient number of examples is generally not advisable, as a model may not generalize well if it is only trained on 15 examples. Similarly, the complete absence of indicators suggests that there is no data to assess model performance, making it impossible to determine whether training should continue or stop.