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Hello everyone! I am currently working on a project that involves text classification. I'm not an expert on machine learning I'm trying to understand how to prepare data and the pipeline for feature extracting and processing data. Currently I'm reading large volumes of labelled data with text, so I am a little confused on how to prepare the data for the model to be trained effectively. Currently, I'm simply reading in the data from a csv file and using the sklearn tf-idf vectorizer to extract features, and using this vector input[label, text] I have 21 labels total, and plenty of training data to train the model. the output is a sparse matrix, that is in turn, used to train the model.
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