TAPE (Tasks Assessing Protein Embeddings)
A benchmark for evaluating protein language models through five biologically relevant semi-supervised learning tasks.
What is TAPE (Tasks Assessing Protein Embeddings)?
TAPE (Tasks Assessing Protein Embeddings) is a benchmark and toolkit for evaluating protein language models. It provides a set of five biologically relevant downstream tasks—such as secondary structure prediction and contact prediction—to assess how well learned protein embeddings capture functional and structural information. The project includes pretrained models, datasets, and training/evaluation code to standardize comparisons in protein representation learning.
Bioinformatics researchers and machine learning scientists working on protein sequence modeling who need to benchmark their models against standardized biological tasks.
TAPE offers a unified, extensible framework for evaluating protein embeddings across multiple biological domains, with pretrained models and curated datasets that reduce implementation overhead. Its focus on biologically meaningful tasks makes it more relevant for real-world applications than generic language modeling benchmarks.
Overview
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
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