def create_and_check_albert_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFAlbertForSequenceClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_labels])
def load_model_and_tokenizer():
unzipped_saved_model_dir = get_unzipped_dir_path(MODEL_ZIP_PATH,
UNZIPPED_MODEL_PATH)
print("Loading pretrained ALBERT classification model")
start = time.time()
config = AlbertConfig.from_pretrained(unzipped_saved_model_dir,
num_labels=NUM_LABELS,
max_length=DEFAULT_MAX_LEN)
model = TFAlbertForSequenceClassification.from_pretrained(
unzipped_saved_model_dir, config=config)
tokenizer = AlbertTokenizer.from_pretrained(unzipped_saved_model_dir,
do_lower_case=True)
duration = time.time() - start
print(f"Initializing model took {duration}")
return model, tokenizer