def test_add_label_deserializes_correctly():
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
ner1 = EntityRecognizer(Vocab(), model)
ner1.add_label("C")
ner1.add_label("B")
ner1.add_label("A")
ner1.initialize(lambda: [_ner_example(ner1)])
ner2 = EntityRecognizer(Vocab(), model)
# the second model needs to be resized before we can call from_bytes
ner2.model.attrs["resize_output"](ner2.model, ner1.moves.n_moves)
ner2.from_bytes(ner1.to_bytes())
assert ner1.moves.n_moves == ner2.moves.n_moves
for i in range(ner1.moves.n_moves):
assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)
def test_add_label_deserializes_correctly():
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
ner1 = EntityRecognizer(Vocab(), model, **config)
ner1.add_label("C")
ner1.add_label("B")
ner1.add_label("A")
ner1.initialize(lambda: [_ner_example(ner1)])
ner2 = EntityRecognizer(Vocab(), model, **config)
# the second model needs to be resized before we can call from_bytes
ner2.model.attrs["resize_output"](ner2.model, ner1.moves.n_moves)
ner2.from_bytes(ner1.to_bytes())
assert ner1.moves.n_moves == ner2.moves.n_moves
for i in range(ner1.moves.n_moves):
assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)
