recurrent neural networks nextwe define our data fields list that would be required while creating the dataset this list represents each column within the dataset if we plan to not use particular column within this datasetwe would need to assign "noneto the column name when defining the list of columns we assign this list to the trainval_fields variable we then create tabulardataset object with the streamlined list of operations necessary on the data columns note that the splits(function does not actually split an existing dataset it should be used only when we already have individually separated datasets in the path nextwe need to build the vocabulary ( numericized representation of the unique words that appear in our field textthis step is very important and has several means of execution we can use pretrained word embedding to create vocabulary or we can custom-train one using pretrained one is simpleso we will use this in our next example we set the maximum number of vocabularies to , the function will also create two additional wordstaking the total to , --one for all the unknown tokens (for examplenew wordsand the other for padding (used to make sentences of equal lengthfinallywe create the iterator objects the sort_within_batch parameter sorts the data within each mini-batch in decreasing order according to the sort_key this is necessary when we want to use pack_ padded_sequence with the padded sequence data and convert the padded sequence tensor to packedsequence object we will not leverage this feature in our first exercisebut we will use it in the next exercisewhere we improve our model essentiallypytorch adds padding to the sequences such that all sequences are of equal length the process is made efficient by sorting the data in the decreasing order of the keyand ensures that the network does not learn the pads the last line prints the most frequent words in the vocab and returns the index associated with each word in the vector (embeddingwith the data ready to be processedwe will construct our rnn classas shown in listing -