You might want to perform the post-processing of data as well and keep only reliable predictions.",
After fine-tuning the model, you should look into the predictions and analyze the model's performance.
The next step is to perform hyperparameter search to find the optimum hyperparameters. Pre-process the text if necessary for the task. Make sure not to skip the exploratory data analysis. Predictions, logit_output = model.predict(["How to create a good text classification model? First step is to prepare good data. "xlmroberta", "classla/xlm-roberta-base-multilingual-text-genre-classifier", use_cuda=True, I have used the code below (reference here: ) several times but actually I'am not able to execute it since the error 'ModuleNotFoundError: No module named '' occurs without any apparent reason.Ĭode from simpletransformers.classification import ClassificationModel
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