Training a pipeline component from scratch

In the previous section, we saw how to update the existing NER component according to our data. In this section, we will create a brand-new NER component for the medicine domain.

Let's start with a small dataset to understand the training procedure. Then we'll be experimenting with a real medical NLP dataset. The following sentences belong to the medicine domain and include medical entities such as drug and disease names:

Methylphenidate/DRUG is effectively used in treating children with epilepsy/DISEASE and ADHD/DISEASE.           
Patients were followed up for 6 months.
Antichlamydial/DRUG antibiotics/DRUG may be useful for curing coronary-artery/DISEASE disease/DISEASE.

The following code block shows how to train an NER component from scratch. As we mentioned before, it's better to create our own NER rather than updating spaCy's default NER model as medical entities...