 Here, we addressed two questions: (1) To which extent can HPC up-scale protein LMs to larger databases and larger models? (2) To which extent can LMs extract features from single proteins to get closer to the performance of methods using evolutionary information? Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids  from 2.1 billion protein sequences ( BFD ).
<clarity>  Here, we addressed two questions: (1) To which extent can HPC up-scale protein LMs to larger databases and larger models? (2) To which extent can LMs extract features from single proteins to get closer to the performance of methods using evolutionary information? Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids  from 2.1 billion protein sequences ( BFD ).
 Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models ( BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids from 2.1 billion protein sequences ( BFD ).