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 https://github.com/i-machine-think/diagnnose.- Anthology ID:
- 2020.blackboxnlp-1.32
- Volume:
- Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- BlackboxNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 342–350
- Language:
- URL:
- https://aclanthology.org/2020.blackboxnlp-1.32
- DOI:
- 10.18653/v1/2020.blackboxnlp-1.32
- Cite (ACL):
- Jaap Jumelet. 2020. diagNNose: A Library for Neural Activation Analysis. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 342–350, Online. Association for Computational Linguistics.
- Cite (Informal):
- diagNNose: A Library for Neural Activation Analysis (Jumelet, BlackboxNLP 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.blackboxnlp-1.32.pdf
- Code
- i-machine-think/diagnnose