def __init__(self, hparams, num_labels):
super().__init__()
self.hparams = hparams
model_name_or_path = "monologg/koelectra-base-discriminator"
config = AutoConfig.from_pretrained(model_name_or_path,
num_labels=num_labels)
self.model = AutoModelForSequenceClassification.from_pretrained(
model_name_or_path, config=config)
self.num_labels = num_labels
def test_conversion_adaptive_model_classification():
farm_model = Converter.convert_from_transformers(
"deepset/bert-base-german-cased-hatespeech-GermEval18Coarse",
device="cpu")
transformer_model = farm_model.convert_to_transformers()[0]
transformer_model2 = AutoModelForSequenceClassification.from_pretrained(
"deepset/bert-base-german-cased-hatespeech-GermEval18Coarse")
# compare weights
for p1, p2 in zip(transformer_model.parameters(),
transformer_model2.parameters()):
assert (p1.data.ne(p2.data).sum() == 0)