@inproceedings{stollenwerk-2023-nerblackbox,
    title = "nerblackbox: A High-level Library for Named Entity Recognition in Python",
    author = "Stollenwerk, Felix",
    editor = "Tan, Liling  and
      Milajevs, Dmitrijs  and
      Chauhan, Geeticka  and
      Gwinnup, Jeremy  and
      Rippeth, Elijah",
    booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/declare-journal/2023.nlposs-1.20/",
    doi = "10.18653/v1/2023.nlposs-1.20",
    pages = "174--178",
    abstract = "We present \textbf{nerblackbox}, a python library to facilitate the use of state-of-the-art transformer-based models for named entity recognition. It provides simple-to-use yet powerful methods to access data and models from a wide range of sources, for fully automated model training and evaluation as well as versatile model inference. While many technical challenges are solved and hidden from the user by default, \textbf{nerblackbox} also offers fine-grained control and a rich set of customizable features. It is thus targeted both at application-oriented developers as well as machine learning experts and researchers."
}