Currently doing the following:
def tokenize_text(text):
    return [letter for letter in text]

def tokenize_ints(int_lst):
    return int(int_lst[0])

text_Field = Field(tokenize = tokenize_text,
               eos_token = '<eos>')

index_Field = Field(tokenize= tokenize_ints)

train_Datafields = [("Index", None),("SRC", text_Field), ("TRG", text_Field)]

train_data = TabularDataset(
        path = "train.csv",
        format = "csv",
        skip_header = True,
        fields = train_Datafields)

text_Field.build_vocab(train_data)

How do I use that vocab to transform dataset into one hot encoded vectors so that I can pass that into a net?