Spacy 3.0.1 Accuracy prediction

Question

How to test accuracy of a spacy pretrained model in version 3.0.1. I want to see my output how accurate my tested model is predicted.This the code below for spacy version 2 but it doesn't work in spacy version 3.can somone tell me the code on spacy version 3.

 from spacy.gold import GoldParse
 from spacy.scorer import Scorer

def evaluate(nlp, examples, ent='PERSON'):
scorer = Scorer()
for input_, annot in examples:
    text_entities = []
    for entity in annot.get('entities'):
        if ent in entity:
            text_entities.append(entity)
    doc_gold_text = nlp.make_doc(input_)
    gold = GoldParse(doc_gold_text, entities=text_entities)
    pred_value = nlp(input_)
    scorer.score(pred_value, gold)
return scorer.scores

examples = [
("Trump says he's answered Mueller's Russia inquiry questions \u2013 live",{"entities":[[0,5,"PERSON"],[25,32,"PERSON"],[35,41,"GPE"]]}),
("Alexander Zverev reaches ATP Finals semis then reminds Lendl who is boss",{"entities":[[0,16,"PERSON"],[55,60,"PERSON"]]}),
("Britain's worst landlord to take nine years to pay off string of fines",{"entities":[[0,7,"GPE"]]}),
("Tom Watson: people's vote more likely given weakness of May's position",{"entities":[[0,10,"PERSON"],[56,59,"PERSON"]]}),
]

nlp = spacy.load('en_core_web_sm')
results = evaluate(nlp, examples)
print(results)

Answer 1

Personnally i had used this method, and i wish it will help you in your work: In your case, I think:

from spacy.training import Example

#get test data

test_data = [
    ("Trump says he's answered Mueller's Russia inquiry questions \u2013 
    live",{"entities":[[0,5,"PERSON"],[25,32,"PERSON"],[35,41,"GPE"]]}),
    ("Alexander Zverev reaches ATP Finals semis then reminds Lendl who is 
    boss",{"entities":[[0,16,"PERSON"],[55,60,"PERSON"]]}),
    ("Britain's worst landlord to take nine years to pay off string of fines", 
    {"entities":[[0,7,"GPE"]]}),
    ("Tom Watson: people's vote more likely given weakness of May's position", 
    {"entities":[[0,10,"PERSON"],[56,59,"PERSON"]]}),
]

#formatted test data in order to adapt with the new version 3 of Spacy

#get nlp object
nlp = spacy.load('en_core_web_sm')

new_test_data = []
for text, annots in test_data:
    new_test_data.append(Example.from_dict(nlp.make_doc(text), annots))

#end formatted test data

#begin evaluation
#using , the evaluate() methos

scores_model = nlp.evaluate(new_test_data)

#print scores that you want
#precision_model = scores_model["ents_p"]
#recall_model = scores_model["ents_r"]
#f_score_model = scores_model["ents_f"]
#scores_entities = scores_model["ents_per_type"]