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122 bytes added ,  14:12, 12 October 2017
For data preprocessing, we adopt the same standard as in the [http://ai.stanford.edu/~amaas/data/sentiment/ IMDB] dataset.
# '''To general users:''' your input (usually a single ".txt" file contains many exampleseach as a row) will be split into a training set (80% by default) and a testing set (20% by default). The target labels you want to predict will be the sub-folder names. The description content (usually a block of text) of each example the examples will go into a separate ".txt" file and the name of the file can be determined by the userfiles. To process your own datasetrun the script, you basically need to specify the file namefollowing: "File Name" : without the ".txt" extension, expected "Expected Columns" : total number of columns, in the input file "Content Index" : the column index of the content "Label Index" : the column index and of the label index.
==Model Training/Prediction==
==General Guidelines for Tuning the Hyper-Parameters==
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