  Transformers have recently taken the center stage in language modeling after
LSTM's were considered the dominant model architecture for a long time. In this
project, we investigate the performance of the Transformer architectures-BERT
and Transformer-XL for the language modeling task. We use a sub-word model
setting with the Finnish language and compare it to the previous State of the
art (SOTA) LSTM model. BERT achieves a pseudo-perplexity score of 14.5, which
is the first such measure achieved as far as we know. Transformer-XL improves
upon the perplexity score to 73.58 which is 27\% better than the LSTM model.
