Adding a Retokenize pipe while training NER model - spacy

Question

I am currenly attempting to train a NER model centered around Property Descriptions. I could get a fully trained model to function to my liking however, I now want to add a retokenize pipe to the model so that I can set up the model to train other things.
From here, I am having issues getting the retokenize pipe to actually work. Here is the definition:
def retok(doc):
ents = [(ent.start, ent.end, ent.label) for ent in doc.ents]
with doc.retokenize() as retok:
string_store = doc.vocab.strings
for start, end, label in ents:
retok.merge(
doc[start: end],
attrs=intify_attrs({'ent_type':label},string_store))
return doc
i am adding it into my training like this:
nlp.add_pipe(retok, after="ner")
and I am adding it into the Language Factories like this:
Language.factories['retok'] = lambda nlp, **cfg: retok(nlp)
The issue I keep getting is "AttributeError: 'English' object has no attribute 'ents'". Now I am assuming I am getting this error because the parameter that is being passed through this function is not a doc but actually the NLP model itself. I am not really sure to get a doc to flow into this pipe during training. At this point I don't really know where to go from here to get the pipe to function the way I want.
Any help is appreciated, thanks.

Answer 1

You can potentially use the built-in merge_entities pipeline component: https://spacy.io/api/pipeline-functions#merge_entities
The example copied from the docs:
texts = [t.text for t in nlp("I like David Bowie")]
assert texts == ["I", "like", "David", "Bowie"]
merge_ents = nlp.create_pipe("merge_entities")
nlp.add_pipe(merge_ents)
texts = [t.text for t in nlp("I like David Bowie")]
assert texts == ["I", "like", "David Bowie"]
If you need to customize it further, the current implementation of merge_entities (v2.2) is a good starting point:
def merge_entities(doc):
"""Merge entities into a single token.
doc (Doc): The Doc object.
RETURNS (Doc): The Doc object with merged entities.
DOCS: https://spacy.io/api/pipeline-functions#merge_entities
"""
with doc.retokenize() as retokenizer:
for ent in doc.ents:
attrs = {"tag": ent.root.tag, "dep": ent.root.dep, "ent_type": ent.l
abel}
retokenizer.merge(ent, attrs=attrs)
return doc
P.S. You are passing nlp to retok() below, which is where the error is coming from:
Language.factories['retok'] = lambda nlp, **cfg: retok(nlp)
See a related question: Spacy - Save custom pipeline