import spacy
from spacy.tokens import DocBin

nlp = spacy.blank("en")
training_data = [
("Tokyo Tower is 333m tall.", [(0, 11, "BUILDING")]),
]
the DocBin will store the example documents
db = DocBin()
for text, annotations in training_data:
doc = nlp(text)
ents = []
for start, end, label in annotations:
span = doc.char_span(start, end, label=label)
ents.append(span)
doc.ents = ents
db.add(doc)
db.to_disk("./train.spacy")