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