@inproceedings{jumelet-2020-diagnnose,
    title = "diag{NN}ose: A Library for Neural Activation Analysis",
    author = "Jumelet, Jaap",
    editor = "Alishahi, Afra  and
      Belinkov, Yonatan  and
      Chrupa{\l}a, Grzegorz  and
      Hupkes, Dieuwke  and
      Pinter, Yuval  and
      Sajjad, Hassan",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.blackboxnlp-1.32/",
    doi = "10.18653/v1/2020.blackboxnlp-1.32",
    pages = "342--350",
    abstract = "In this paper we introduce diagNNose, an open source library for analysing the activations of deep neural networks. diagNNose contains a wide array of interpretability techniques that provide fundamental insights into the inner workings of neural networks. We demonstrate the functionality of diagNNose with a case study on subject-verb agreement within language models. diagNNose is available at \url{https://github.com/i-machine-think/diagnnose}."
}