If you created your component with nlp.create_pipe('name'): remove nlp.create_pipe and call nlp.add_pipe('name') instead.

[TheContentGym]

File:
==============
import spacy
from pysbd.utils import PySBDFactory
nlp = spacy.blank('en')

nlp.add_pipe(PySBDFactory(nlp))
doc = nlp("It costs $5 to make a small python program. This is a demo to nacho.")
print(list(doc.sents))

Error

• If you created your component with nlp.create_pipe('name'): remove nlp.create_pipe and call nlp.add_pipe('name') instead.

• If you passed in a component like TextCategorizer(): call nlp.add_pipe with the string name instead, e.g. nlp.add_pipe('textcat').

• If you're using a custom component: Add the decorator @Language.component (for function components) or @Language.factory (for class components / factories) to your custom component and assign it a name, e.g. @Language.component('your_name'). You can then run nlp.add_pipe('your_name') to add it to the pipeline.
  =============
  Question: what am I missing here?

[ljvmiranda921]

Hi @TheContentGym ,

The nlp.add_pipe function takes in a factory name (a string). If you're using a custom component, then I recommend looking at this documentation on how to properly make one. Also, next time please format your code in Markdown (use backticks) and paste the full traceback.