Shypd.aiThe largest open index of AI toolstape is an open-source research and education tool that provides data, weights, and code for benchmarking trained protein embeddings. It includes a pretraining corpus, supervised downstream tasks, and pretrained language model weights for protein biology.
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tape (Tasks Assessing Protein Embeddings) is an open-source project from the Song Lab at UC Berkeley, designed to benchmark and assess protein embeddings across various domains of protein biology. It offers a comprehensive suite of resources including a pretraining corpus, five supervised downstream tasks, pretrained language model weights, and benchmarking code. The tool has been updated to use PyTorch, providing an API for loading pretrained models like BERT, UniRep, and trRosetta. Users can embed proteins from FASTA files, train language models, and evaluate both language and downstream models. While the training code is provided, the developers recommend using frameworks like PyTorch Lightning or Fairseq for future compatibility and ease of use, focusing their efforts on maintaining model availability.
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