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Creating 10.21105.joss.03652.crossref.xml
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131 changes: 131 additions & 0 deletions joss.03652/10.21105.joss.03652.crossref.xml
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<unstructured_citation>Transformer eXplainability and eXploration , Martindale, Nathan and Stewart, Scott L., The Transformer eXplainability and eXploration library is intended to aid in the explorability and explainability of transformer classification networks, or transformer language models with sequence classification heads. The basic function of this library is to take a trained transformer and test/train dataset and produce an ipywidget dashboard which can be displayed in a jupyter notebook or in jupyter lab., [Computer Software] https://doi.org/10.11578/dc.20210129.1, 2021, jan, 1</unstructured_citation>
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<unstructured_citation>PyTorch: An Imperative Style, High-Performance Deep Learning Library, Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith, Advances in Neural Information Processing Systems 32, Wallach, H. and Larochelle, H. and Beygelzimer, A. and d’ Alché-Buc, F. and Fox, E. and Garnett, R., 8024–8035, 2019, Curran Associates Inc., http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf</unstructured_citation>
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<unstructured_citation>Transformers: State-of-the-Art Natural Language Processing, Wolf, Thomas and Debut, Lysandre and Sanh, Victor and Chaumond, Julien and Delangue, Clement and Moi, Anthony and Cistac, Pierric and Rault, Tim and Louf, Rémi and Funtowicz, Morgan and Davison, Joe and Shleifer, Sam and von Platen, Patrick and Ma, Clara and Jernite, Yacine and Plu, Julien and Xu, Canwen and Scao, Teven Le and Gugger, Sylvain and Drame, Mariama and Lhoest, Quentin and Rush, Alexander M., Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, oct, 2020, Online, Association for Computational Linguistics, https://www.aclweb.org/anthology/2020.emnlp-demos.6, 38–45, 10</unstructured_citation>
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<unstructured_citation>Attention is not explanation, Jain, Sarthak and Wallace, Byron C, arXiv preprint arXiv:1902.10186, 2019</unstructured_citation>
</citation>
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<doi>10.1145/3366424.3383542</doi>
</citation>
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<unstructured_citation>Scikit-learn: Machine Learning in Python, Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E., Journal of Machine Learning Research, 12, 2825–2830, 2011</unstructured_citation>
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<unstructured_citation>Python Software Foundation, Black - The Uncompromising Code Formatter, https://github.com/psf/black, 2021</unstructured_citation>
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<unstructured_citation>Project Jupyter Contributors, ipywidgets: Interactive HTML Widgets, https://github.com/jupyter-widgets/ipywidgets, 2021</unstructured_citation>
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