Evaluation in a Spacy NER model

Question

I am trying to evaluate a trained NER Model created using spacy lib. Normally for these kind of problems you can use f1 score (a ratio between precision and recall). I could not find in the documentation an accuracy function for a trained NER model.

I am not sure if it's correct but I am trying to do it with the following way(example) and using f1_score from sklearn:

from sklearn.metrics import f1_score
import spacy
from spacy.gold import GoldParse


nlp = spacy.load("en") #load NER model
test_text = "my name is John" # text to test accuracy
doc_to_test = nlp(test_text) # transform the text to spacy doc format

# we create a golden doc where we know the tagged entity for the text to be tested
doc_gold_text= nlp.make_doc(test_text)
entity_offsets_of_gold_text = [(11, 15,"PERSON")]
gold = GoldParse(doc_gold_text, entities=entity_offsets_of_gold_text)

# bring the data in a format acceptable for sklearn f1 function
y_true = ["PERSON" if "PERSON" in x else 'O' for x in gold.ner]
y_predicted = [x.ent_type_ if x.ent_type_ !='' else 'O' for x in doc_to_test]
f1_score(y_true, y_predicted, average='macro')`[1]
> 1.0

Any thoughts are or insights are useful.

Answer 1

For those one having the same question in the following link:

spaCy/scorer.py

you can find different metrics including: fscore, recall and precision. An example using scorer:

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

def evaluate(ner_model, examples):
    scorer = Scorer()
    for input_, annot in examples:
        doc_gold_text = ner_model.make_doc(input_)
        gold = GoldParse(doc_gold_text, entities=annot)
        pred_value = ner_model(input_)
        scorer.score(pred_value, gold)
    return scorer.scores

# example run

examples = [
    ('Who is Shaka Khan?',
     [(7, 17, 'PERSON')]),
    ('I like London and Berlin.',
     [(7, 13, 'LOC'), (18, 24, 'LOC')])
]

ner_model = spacy.load(ner_model_path) # for spaCy's pretrained use 'en_core_web_sm'
results = evaluate(ner_model, examples)

where input_ is the text(ex. "my name is John") and annot is the annotations (ex. [(11,16,"PEOPLE")]

The scorer.scores returns multiple scores. The example is taken from spaCy example in github (link does not work anymore)