Hi @e101sg,
Re your first question about the metrics (no need to be sorry at all!):
ENTS_P is the Precision of NER component, that's right.
For the explanation of SCORE and other columns, I'll just refer you this post from Ines:
Re ner.teach command question:
The way you're planning to use it is correct. After the annotation with the model in the loop it is recommended to retrain the model on the entire dataset resulting from the ner.teach session. So, yes you'd be using your_New_dataset_name in train. Our docs on active learning have some more details on why this is the recommended way:
When you annotate with a model in the loop, the model is also updated in the background. So why do you still need to train your model on the annotations afterwards, and can’t just export the model that was updated in the loop? The main reason is that the model in the loop is only updated once each new annotation. This is never going to be as effective as batch training a model on the whole dataset, making multiple passes over the data, shuffling on each epoch and using other deep learning tricks like dropout rates, compounding batch sizes and so on. If you batch train your model with the collected annotations afterwards, you should receive the same model you had in the loop, just better.