def METHOD_NAME():
    parser = PdArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    # Log model and data config
    model_args, data_args, training_args = load_config(
        model_args.config, "SequenceClassification", data_args.dataset, model_args, data_args, training_args
    )
    # Print model and data config
    training_args.print_config(model_args, "Model")
    training_args.print_config(data_args, "Data")
    paddle.set_device(training_args.device)
    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, world_size: {training_args.world_size}, "
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    data_args.dataset = data_args.dataset.strip()
    training_args.output_dir = os.path.join(training_args.output_dir, data_args.dataset)
    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )
    raw_datasets = load_dataset("clue", data_args.dataset)
    data_args.label_list = getattr(raw_datasets["train"], "label_list", None)
    num_classes = len(raw_datasets["train"].label_list)
    # Define tokenizer, model, loss function.
    model = ErnieForSequenceClassification.from_pretrained(model_args.model_name_or_path, num_classes=num_classes)
    tokenizer = ErnieTokenizer.from_pretrained(model_args.model_name_or_path)
    criterion = nn.loss.CrossEntropyLoss() if data_args.label_list else nn.loss.MSELoss()
    # Define dataset pre-process function
    trans_fn = partial(
        seq_convert_example,
        tokenizer=tokenizer,
        label_list=data_args.label_list,
        max_seq_len=data_args.max_seq_length,
        dynamic_max_length=data_args.dynamic_max_length,
    )
    # Define data collator
    data_collator = DataCollatorWithPadding(tokenizer)
    # Dataset pre-process
    logger.info("Data Preprocessing...")
    if training_args.do_train:
        train_dataset = raw_datasets["train"].map(trans_fn, lazy=training_args.lazy_data_processing)
    if training_args.do_eval:
        eval_dataset = raw_datasets["dev"].map(trans_fn, lazy=training_args.lazy_data_processing)
    if training_args.do_predict:
        test_dataset = raw_datasets["test"].map(trans_fn, lazy=training_args.lazy_data_processing)
    # Define the metrics of tasks.
    def compute_metrics(p):
        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
        preds = paddle.to_tensor(preds)
        label = paddle.to_tensor(p.label_ids)
        metric = Accuracy()
        metric.reset()
        result = metric.compute(preds, label)
        metric.update(result)
        accu = metric.accumulate()
        metric.reset()
        return {"accuracy": accu}
    trainer = Trainer(
        model=model,
        criterion=criterion,
        args=training_args,
        data_collator=data_collator,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        compute_metrics=compute_metrics,
    )
    checkpoint = None
    if training_args.resume_from_checkpoint is not None:
        checkpoint = training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
        checkpoint = last_checkpoint
    # Training
    if training_args.do_train:
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        metrics = train_result.metrics
        trainer.save_model()
        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()
    # Evaluate and tests model
    if training_args.do_eval:
        eval_metrics = trainer.evaluate()
        trainer.log_metrics("eval", eval_metrics)
    if training_args.do_predict:
        test_ret = trainer.predict(test_dataset)
        trainer.log_metrics("test", test_ret.metrics)
        logits = test_ret.predictions
        max_value = np.max(logits, axis=1, keepdims=True)
        exp_data = np.exp(logits - max_value)
        probs = exp_data / np.sum(exp_data, axis=1, keepdims=True)
        out_dict = {"label": probs.argmax(axis=-1).tolist(), "confidence": probs.max(axis=-1).tolist()}
        out_file = open(os.path.join(training_args.output_dir, "test_results.json"), "w")
        json.dump(out_dict, out_file)
    # Export inference model
    if training_args.do_export:
        # You can also load from certain checkpoint
        # trainer.load_state_dict_from_checkpoint("/path/to/checkpoint/")
        input_spec = [
            paddle.static.InputSpec(shape=[None, None], dtype="int64"),  # input_ids
            paddle.static.InputSpec(shape=[None, None], dtype="int64"),  # segment_ids
        ]
        model_args.export_model_dir = os.path.join(model_args.export_model_dir, data_args.dataset, "export")
        paddlenlp.transformers.export_model(
            model=trainer.model, input_spec=input_spec, path=model_args.export_model_dir
        )
        trainer.tokenizer.save_pretrained(model_args.export_model_dir)