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Status: Unchecked ×Keyword: protein primary structure ×Clear all

1 claim from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications

   Yang, Li, Tan et al. · arXiv (Cornell University) · 2023

   PETA trained protein language models with 14 vocabulary sizes under three tokenization methods and tested their transfer learning on 33 downstream datasets, to find which vocabulary size works best.

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   1. UncheckedIn this benchmark, protein language models worked best with vocabularies of 50 to 200 tokens, and worse with more than 800.“Extensive experiments indicate that vocabulary sizes between 50 and 200 optimize the model, whereas sizes exceeding 800 detrimentally affect the model's representational performance.”