def METHOD_NAME(args):
    paddle.set_device(args.device)
    rank = paddle.distributed.get_rank()
    trainer_num = paddle.distributed.get_world_size()
    if trainer_num > 1:
        paddle.distributed.init_parallel_env()
    set_seed(args.seed)
    # Create dataset, tokenizer and dataloader.
    train_ds, dev_ds, test_ds = load_dataset(
        datafiles=(
            os.path.join(args.data_dir, "train.txt"),
            os.path.join(args.data_dir, "dev.txt"),
            os.path.join(args.data_dir, "test.txt"),
        )
    )
    label_vocab = load_dict(os.path.join(args.data_dir, "tag.dic"))
    tokenizer = AutoTokenizer.from_pretrained("ernie-1.0")
    trans_func = partial(convert_to_features, tokenizer=tokenizer, label_vocab=label_vocab)
    train_ds.map(trans_func)
    dev_ds.map(trans_func)
    test_ds.map(trans_func)
    ignore_label = -1
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # input_ids
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"),  # token_type_ids
        Stack(dtype="int64"),  # seq_len
        Pad(axis=0, pad_val=ignore_label, dtype="int64"),  # labels
    ): fn(samples)
    train_loader = create_dataloader(
        dataset=train_ds, mode="train", batch_size=args.batch_size, batchify_fn=batchify_fn
    )
    dev_loader = create_dataloader(dataset=dev_ds, mode="dev", batch_size=args.batch_size, batchify_fn=batchify_fn)
    test_loader = create_dataloader(dataset=test_ds, mode="test", batch_size=args.batch_size, batchify_fn=batchify_fn)
    # Define the model netword and its loss
    model = AutoModelForTokenClassification.from_pretrained("ernie-1.0", num_classes=len(label_vocab))
    if trainer_num > 1:
        model = paddle.DataParallel(model)
    metric = ChunkEvaluator(label_list=label_vocab.keys(), suffix=True)
    loss_fn = paddle.nn.loss.CrossEntropyLoss(ignore_index=ignore_label)
    optimizer = paddle.optimizer.AdamW(learning_rate=2e-5, parameters=model.parameters())
    global_step = 0
    tic_train = time.time()
    for epoch in range(args.epochs):
        for step, batch in enumerate(train_loader):
            input_ids, token_type_ids, length, labels = batch
            logits = model(input_ids, token_type_ids)
            loss = paddle.mean(loss_fn(logits, labels))
            global_step += 1
            if global_step % 10 == 0 and rank == 0:
                print(
                    "global step %d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s"
                    % (global_step, epoch, step, loss, 10 / (time.time() - tic_train))
                )
                tic_train = time.time()
            loss.backward()
            optimizer.step()
            optimizer.clear_grad()
            if global_step % 100 == 0 and rank == 0:
                evaluate(model, metric, dev_loader)
                save_dir = os.path.join(args.save_dir, "model")
                model_to_save = model._layers if isinstance(model, paddle.DataParallel) else model
                model_to_save.save_pretrained(save_dir)
                tokenizer.save_pretrained(save_dir)
            if global_step > args.max_steps:
                return
    if rank == 0:
        preds = predict(model, test_loader, test_ds, label_vocab)
        file_path = "ernie_results.txt"
        with open(file_path, "w", encoding="utf8") as fout:
            fout.write("\n".join(preds))
        # Print some examples
        print("The results have been saved in the file: %s, some examples are shown below: " % file_path)
        print("\n".join(preds[:10]))