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"""
Basic testing of the NER tagger.
"""
import pytest
import stanza
from tests import *
from stanza.models.ner.scorer import score_by_token, score_by_entity
pytestmark = pytest.mark.pipeline
EN_DOC = "Chris Manning is a good man. He works in Stanford University."
EN_DOC_GOLD = """
<Span text=Chris Manning;type=PERSON;start_char=0;end_char=13>
<Span text=Stanford University;type=ORG;start_char=41;end_char=60>
""".strip()
def test_ner():
nlp = stanza.Pipeline(**{'processors': 'tokenize,ner', 'dir': TEST_MODELS_DIR, 'lang': 'en', 'logging_level': 'error'})
doc = nlp(EN_DOC)
assert EN_DOC_GOLD == '\n'.join([ent.pretty_print() for ent in doc.ents])
def test_ner_scorer():
pred_sequences = [['O', 'S-LOC', 'O', 'O', 'B-PER', 'E-PER'],
['O', 'S-MISC', 'O', 'E-ORG', 'O', 'B-PER', 'I-PER', 'E-PER']]
gold_sequences = [['O', 'B-LOC', 'E-LOC', 'O', 'B-PER', 'E-PER'],
['O', 'S-MISC', 'B-ORG', 'E-ORG', 'O', 'B-PER', 'E-PER', 'S-LOC']]
token_p, token_r, token_f = score_by_token(pred_sequences, gold_sequences)
assert pytest.approx(token_p, abs=0.00001) == 0.625
assert pytest.approx(token_r, abs=0.00001) == 0.5
assert pytest.approx(token_f, abs=0.00001) == 0.55555
entity_p, entity_r, entity_f = score_by_entity(pred_sequences, gold_sequences)
assert pytest.approx(entity_p, abs=0.00001) == 0.4
assert pytest.approx(entity_r, abs=0.00001) == 0.33333
assert pytest.approx(entity_f, abs=0.00001) == 0.36363
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