Rives et al. (PNAS, published 5 April 2021) state that they use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity.
One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.
What it connects
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences → states provision → Unsupervised training on 86 billion amino acids across 250 million sequences
Where it appears
No collection page holds this claim. It is listed with its records in the explorer.