Model: virtual-human-chc/prot_t5_xl_uniref50
Author: virtual-human-chc
Pipeline: feature-extraction
Library: transformers
Tags: transformers, pytorch, t5, text2text-generation, protein language model, computational-biology, high-performance-computing, machine-learning, language-modeling, deep-learning, proteins, chemistry, natural-language-processing, rostlab, virtual-human-chc, feature-extraction, en, dataset:UniRef50, license:apache-2.0, text-generation-inference, endpoints_compatible, region:us
Card metadata: tags: protein language model, computational-biology, high-performance-computing, machine-learning, language-modeling, deep-learning, proteins, chemistry, natural-language-processing, rostlab, virtual-human-chc
datasets: UniRef50
license: apache-2.0
language: en
pipeline_tag: feature-extraction
library_name: transformers
citation: @ARTICLE{prottrans,
  author={Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Yu, Wang and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and Bhowmik, Debsindhu and Rost, Burkhard},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing},
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3095381}
}

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Created: 2025-10-21 18:23:17