def METHOD_NAME(model_type, model_name):
    # Train and Evaluation data needs to be in a Pandas Dataframe of two columns.
    # The first column is the text with type str, and the second column is the
    # label with type int.
    train_data = [
        ["Example sentence belonging to class 1", 1],
        ["Example sentence belonging to class 0", 0],
    ]
    train_df = pd.DataFrame(train_data, columns=["text", "labels"])
    eval_data = [
        ["Example eval sentence belonging to class 1", 1],
        ["Example eval sentence belonging to class 0", 0],
    ]
    eval_df = pd.DataFrame(eval_data, columns=["text", "labels"])
    # Create a ClassificationModel
    model = ClassificationModel(
        model_type,
        model_name,
        use_cuda=False,
        args={
            "no_save": True,
            "reprocess_input_data": True,
            "overwrite_output_dir": True,
            "scheduler": "constant_schedule",
            "max_seq_length": 20,
        },
    )
    # Train the model
    model.train_model(train_df)
    # Evaluate the model
    result, model_outputs, wrong_predictions = model.eval_model(eval_df)
    predictions, raw_outputs = model.predict(["Some arbitary sentence"])