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Proc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following paper:

    title = "Pre-train or Annotate? Domain Adaptation with a Constrained Budget",
    author = "Bai, Fan  and
              Ritter, Alan  and
              Xu, Wei",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",


from transformers import *
tokenizer = AutoTokenizer.from_pretrained("fbaigt/proc_roberta")
model = AutoModelForTokenClassification.from_pretrained("fbaigt/proc_roberta")

More usage details can be found here.

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