 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.
<coherence>  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.
 This paper compares 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.