DeepFM Features

Question

I'm trying to train a binary classification model using DeepFM for the first time. The dataset consists of anonymized ids mapped to a list of segments with a boolean 1 or 0 if they have the segment.

The data is one hot encoded so data looks like:

id	SEGMENT1	SEGMENT2	SEGMENT3	Label
id1	0	1	0	0
id2	1	1	1	1
id2	1	0	1	1

I am training via the documentation in deepctr documents, but they have a requirement for dense (numeric) and sparse features (categorical). I would assume I dense since its defined by 0 and 1 and I don't need to transform anything with label-encoder for categorical. Do I still need to use dnn_feature_columns and linear_feature_columns? I don't have both in my data.

linear_feature_columns = fixlen_feature_columns

feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)

train_model_input = {name: train[name] for name in feature_names}
test_model_input = {name: test[name] for name in feature_names}

model = DeepFM(linear_feature_columns, dnn_feature_columns, task='binary')
model.compile("adam", "binary_crossentropy",
                  metrics=['binary_crossentropy'], )

Thank you in advance!