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I get this error. It appears to be a result of a later version of spacy being used. I am using spaCy version 3.3.1 . As I am a beginner, I don't know how to build a workaround or follow the instructions given. can you provide any suggestions.

ValueError Traceback (most recent call last)
Input In [11], in <cell line: 97>()
94 return (results)
96 patterns = create_training_data("../data/hp_characters.json", "PERSON")
---> 97 generate_rules(patterns)
98 # print (patterns)
100 nlp = spacy.load("hp_ner")

Input In [11], in generate_rules(patterns)
84 ruler = EntityRuler(nlp)
85 ruler.add_patterns(patterns)
---> 86 nlp.add_pipe(ruler)
87 nlp.to_disk("hp_ner")

File ~/Desktop/Current/NLP_Exp/mvenv/lib/python3.10/site-packages/spacy/language.py:773, in Language.add_pipe(self, factory_name, name, before, after, first, last, source, config, raw_config, validate)
771 bad_val = repr(factory_name)
772 err = Errors.E966.format(component=bad_val, name=name)
--> 773 raise ValueError(err)
774 name = name if name is not None else factory_name
775 if name in self.component_names:

ValueError: [E966] nlp.add_pipe now takes the string name of the registered component factory, not a callable component. Expected string, but got <spacy.pipeline.entityruler.EntityRuler object at 0x124583a40> (name: 'None').

• 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.

I'm noobing my way along the tutorial, trying to make a training module containing an entity ruler and test it. The language I'm working with is Romanian, maybe this has something to do with it. I can create it, load it but when I use it on a text it croaks on me.

Could you please help?

Here's my code:
` import spacy
import mysql.connector
import pandas as pd
from spacy.lang.ro import Romanian
from spacy.pipeline import EntityRuler
import json
import os

def load_data(file):
	with open(file, "r", encoding="utf-8") as f:
		data =json.load(f)
	return(data)

def generate_better_characters(file):
	data = load_data(file)
	print(len(data))
	new_characters = []
	for item in data:
		new_characters.append(item)
	for item in data:
		item = item.replace("și", "").replace("si", "").replace("Și", "")
		names = item.split()
		for name in names:
			name = name.strip()
			#	Debug
			#print(name)
			new_characters.append(name)
		if "(" in item:
			names = item.split("(")
			for name in names:
				name = name.replace(")", "").strip()
				new_characters.append(name)
		if "," in item:
			names = item.split(",")
			name = name.replace("și", "").replace("si", "").strip()
			#	Debug
			#print(name)
			if " " in name:
				new_names = name.split()
				for x in new_names:
					new_characters.append(x)
					#print(x)
			new_characters.apend(name)
	
	
	final_characters = []
	
	titles = ["Dr.", "Profesorul", "prof.", "Prof.", "Domnul", "domnul", "dl.", "Dl.", "Doamna", "doamna", "dna.", "domnisoara", "Domnisoara", "Dl. si dna."]
	
	for character in new_characters:
		if "" != character:
			final_characters.append(character)
			for title in titles:
				titled_char = f"{title} {character}"
				final_characters.append(titled_char)
	
	print(len(final_characters))
	final_characters = list(set(final_characters))
	print(len(final_characters))
	final_characters.sort()
	return(final_characters)

def create_training_data(file, type):
	data = generate_better_characters(file)
	patterns = []
	for item in data:
		pattern = {
					"label": type,
					"pattern": item
					}
		patterns.append(pattern)
	return(patterns)

def generate_rules(patterns):
	nlp = Romanian()
	ruler = EntityRuler(nlp)
	ruler.add_patterns(patterns)
	nlp.add_pipe("entity_ruler")
	nlp.to_disk("trained_ner")

patterns = create_training_data("C:\\tutorial.json", "PERSON")
generate_rules(patterns)

with open(f"C:\\mltutorial.txt", "r", encoding='utf-8') as f:
	text = f.read()

nlp = spacy.load("trained_ner")
doc = nlp(text)

for ent in doc.ents:
	print(ent.text)`

ValueError Traceback (most recent call last)
in ()
60
61 patterns = create_training_data("/content/hp_char.json","PERSON")
---> 62 generate_rules(patterns)

2 frames
/usr/local/lib/python3.7/dist-packages/spacy/language.py in create_pipe(self, factory_name, name, config, raw_config, validate)
637 lang_code=self.lang,
638 )
--> 639 raise ValueError(err)
640 pipe_meta = self.get_factory_meta(factory_name)
641 config = config or {}

ValueError: [E002] Can't find factory for 'ruler' for language English (en). This usually happens when spaCy calls nlp.create_pipe with a custom component name that's not registered on the current language class. If you're using a Transformer, make sure to install 'spacy-transformers'. If you're using a custom component, make sure you've added the decorator @Language.component (for function components) or @Language.factory (for class components).

Available factories: attribute_ruler, tok2vec, merge_noun_chunks, merge_entities, merge_subtokens, token_splitter, parser, beam_parser, entity_linker, ner, beam_ner, entity_ruler, lemmatizer, tagger, morphologizer, senter, sentencizer, textcat, textcat_multilabel, en.lemmatizer