  Protein modeling is an increasingly popular area of machine learning
research. Semi-supervised learning has emerged as an important paradigm in
protein modeling due to the high cost of acquiring supervised protein labels,
but the current literature is fragmented when it comes to datasets and
standardized evaluation techniques. To facilitate progress in this field, we
introduce the Tasks Assessing Protein Embeddings (TAPE), a set of five
biologically relevant semi-supervised learning tasks spread across different
domains of protein biology. We curate tasks into specific training, validation,
and test splits to ensure that each task tests biologically relevant
generalization that transfers to real-life scenarios. We benchmark a range of
approaches to semi-supervised protein representation learning, which span
recent work as well as canonical sequence learning techniques. We find that
self-supervised pretraining is helpful for almost all models on all tasks, more
than doubling performance in some cases. Despite this increase, in several
cases features learned by self-supervised pretraining still lag behind features
extracted by state-of-the-art non-neural techniques. This gap in performance
suggests a huge opportunity for innovative architecture design and improved
modeling paradigms that better capture the signal in biological sequences. TAPE
will help the machine learning community focus effort on scientifically
relevant problems. Toward this end, all data and code used to run these
experiments are available at https://github.com/songlab-cal/tape.
