@inproceedings{gu-budhkar-2021-package,
    title = "A Package for Learning on Tabular and Text Data with Transformers",
    author = "Gu, Ken  and
      Budhkar, Akshay",
    editor = "Zadeh, Amir  and
      Morency, Louis-Philippe  and
      Liang, Paul Pu  and
      Ross, Candace  and
      Salakhutdinov, Ruslan  and
      Poria, Soujanya  and
      Cambria, Erik  and
      Shi, Kelly",
    booktitle = "Proceedings of the Third Workshop on Multimodal Artificial Intelligence",
    month = jun,
    year = "2021",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2021.maiworkshop-1.10/",
    doi = "10.18653/v1/2021.maiworkshop-1.10",
    pages = "69--73",
    abstract = "Recent progress in natural language processing has led to Transformer architectures becoming the predominant model used for natural language tasks. However, in many real- world datasets, additional modalities are included which the Transformer does not directly leverage. We present Multimodal- Toolkit, an open-source Python package to incorporate text and tabular (categorical and numerical) data with Transformers for downstream applications. Our toolkit integrates well with Hugging Face`s existing API such as tokenization and the model hub which allows easy download of different pre-trained models."
}