def test_load_nlp_model_version_lt3(self, spacy_mock):
spacy_mock.configure_mock(
__version__="2.3.7") # some version less than 3.0
task = SpacyNLP(text="This is some text",
spacy_model_name="en_core_web_sm")
spacy_mock.load.assert_called_once_with("en_core_web_sm",
disable=[],
component_cfg={})
assert task.nlp is not None
import spacy
# load a spacy language model
nlp = spacy.load("en_core_web_sm")
# add a custom component to language model
def custom_component(doc):
# do something with document
return doc
nlp.add_pipe(custom_component, name="custom")
# create flow for NLP
with Flow("Natural Language Processing") as flow:
# create a spaCy doc from text, the equivalent of nlp('This is some text')
doc = SpacyNLP(text="This is some text", nlp=nlp)
# extract default components from language model pipeline
tagger = SpacyTagger(nlp)
parser = SpacyParser(nlp)
ner = SpacyNER(nlp)
# extract a custom component from language model pipeline by name
custom_pipeline_component = SpacyComponent("custom")
flow.run()
def test_text_passed_to_run(self):
nlp = MagicMock()
task = SpacyNLP(text="This is some text", nlp=nlp)
task.run()
assert nlp.call_args[0][0] == "This is some text"
def test_bad_model_raises_error(self):
with pytest.raises(ValueError) as exc:
task = SpacyNLP(text="This is some text",
spacy_model_name="not_a_spacy_model")
assert "not_a_spacy_model" in str(exc.value)
def test_initialization(self):
task = SpacyNLP(text="This is some text", nlp=spacy.blank("en"))
assert task.text == "This is some text"
def test_bad_model_raises_error(self):
with pytest.raises(ValueError, match="not_a_spacy_model"):
task = SpacyNLP(text="This is some text",
spacy_model_name="not_a_spacy_model")
def test_load_nlp_model(self):
spacy.cli.download("en_core_web_sm")
task = SpacyNLP(text="This is some text",
spacy_model_name="en_core_web_sm")
assert task.nlp is not None