 Use examples  ```python from simpletransformers.classification import ClassificationModel model_args= {             "num_train_epochs": 15,             "learning_rate": 1e-5,             "max_seq_length": 512,             "silent": True             } model = ClassificationModel(     "xlmroberta", "TajaKuzman/xlm-roberta-base-multilingual-text-genres", use_cuda=True,     args=model_args      ) predictions, logit_output = model.predict(["How to create a good text classification model? First step is to prepare good data. Make sure not to skip the exploratory data analysis. Pre-process the text if necessary for the task. The next step is to perform hyperparameter search to find the optimum hyperparameters. After fine-tuning the model, you should look into the predictions and analyze the model's performance. You might want to perform the post-processing of data as well and keep only reliable predictions.",                                          "On our site, you can find a great genre identification model which you can use for thousands of different tasks. With our model, you can fastly and reliably obtain high-quality genre predictions and explore which genres exist in your corpora. Available for free!"]                                         ) predictions 