def create_and_check_electra_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} (sequence_output,) = model(inputs) inputs = [input_ids, input_mask] (sequence_output,) = model(inputs) (sequence_output,) = model(input_ids) result = { "sequence_output": sequence_output.numpy(), } self.parent.assertListEqual( list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size] )
def create_and_check_electra_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = TFElectraModel(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def test_model_from_pretrained(self): # for model_name in TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["google/electra-small-discriminator"]: model = TFElectraModel.from_pretrained(model_name) self.assertIsNotNone(model)
def test_model_from_pretrained(self): # for model_name in list(TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: for model_name in ["electra-small-discriminator"]: model = TFElectraModel.from_pretrained(model_name, cache_dir=CACHE_DIR) self.assertIsNotNone(model)