Before revision:  In this paper , we compare different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning , and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
After revision:  In this paper , we compare different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.