Model Training for NER

[ines (Ines Montani)]

@jsnleong

I have a blank ner model on hand (created from en_core_web_lg). When using any recipe to train my label (for eg ner.match, ner.teach), do I load in this blank model as the argument in the recipe?

Yes!

@jsnleong

When I perform ner.match to annotate via pattern file, I understand that no active learning takes place. So can I assume that, the annotations only gets learnt when I perform ner.batch-train?

Exactly! Also, keep in mind that recipes that do support active learning also don't just modify the model in place. (That would be bad – the model should always be properly batch trained afterwards.) The active learning only helps create better annotations.

@jsnleong

When running the batch training, the argument for spaCy model, do I specify the blank ner model?

Yes, the model passed into the training command is the model you want to update.

@jsnleong

After I get the trained model and saved it to a directory, whenever I like to train my model again on more data, or continue annotation from where I left off, do I just load the trained model? Is there any other arguments to put, because I notice there is a --resume functionality in ner.teach…

If you want to annotate with a model in the loop, you ideally want to be using the model you previously updated, yes. For recipes that don't use any active learning it usually doesn't matter – they only really use the model for tokenization etc., so you could theoretically also pass in the untrained base model.

@jsnleong

When training model for next time via batch-train, do I train on a brand new blank model or re-train on existing model?

We usually recommend starting with the blank base model every time and then updating it with all of the annotations. This may help prevent different interactions between the existing weights and new examples, and also makes it easier to compare your results, because they were produced by the same process, just with different amounts of data.