derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
via model-based source attribution.
Tips:
CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
  or links to generate coherent text. Refer to the original implementation for
  more information.
CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
