Move inp_len to model_settings.py

train.py

@@ -16,7 +16,6 @@ tokenizer = Tokenizer() # a tokenizer is a thing to split text into words, it mi
 tokenizer.fit_on_texts(list(dset.keys()))
 
 vocab_size = len(tokenizer.get_vocabulary())
-inp_len = 10 # limit of the input length, after 10 words the
 
 model = Sequential()
 model.add(Embedding(input_dim=vocab_size, output_dim=emb_size, input_length=inp_len))