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1 changed files with 16 additions and 6 deletions
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22
spacy/tests/pipeline/test_tok2vec.py
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@ -231,7 +231,7 @@ def test_tok2vec_listener_callback():
def test_tok2vec_listener_overfitting():
""" Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components """
"""Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
@ -264,7 +264,7 @@ def test_tok2vec_listener_overfitting():
def test_tok2vec_frozen_not_annotating():
""" Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating """
"""Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
@ -274,12 +274,16 @@ def test_tok2vec_frozen_not_annotating():
for i in range(2):
losses = {}
with pytest.raises(ValueError, match=r"the tok2vec embedding layer is not updated"):
nlp.update(train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"])
with pytest.raises(
ValueError, match=r"the tok2vec embedding layer is not updated"
):
nlp.update(
train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"]
)
def test_tok2vec_frozen_overfitting():
""" Test that a pipeline with a frozen & annotating tok2vec can still overfit """
"""Test that a pipeline with a frozen & annotating tok2vec can still overfit"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
@ -289,7 +293,13 @@ def test_tok2vec_frozen_overfitting():
for i in range(100):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"], annotates=["tok2vec"])
nlp.update(
train_examples,
sgd=optimizer,
losses=losses,
exclude=["tok2vec"],
annotates=["tok2vec"],
)
assert losses["tagger"] < 0.0001
# test the trained model