MTCue: Model Checkpoints

The model checkpoints contained here are associated with an ACL 2023 paper entitled "MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation" (citation is to be added when the Proceedings are published).
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Each .zip file here contains a checkpoint to a baseline translation model and MTCue for the language pair in the name (e.g. en.de is the English-to-German language pair). How to use them is described in detail in the associated GitHub repository.
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The models (checkpoints.zip) were trained in PyTorch and via the Fairseq toolkit:
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.,  Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf,  A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S.,  Steiner, B., Fang, L., … Chintala, S. (2019). PyTorch: An imperative  style, high-performance deep learning library. Advances in Neural  Information Processing Systems, 32(NeurIPS).
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Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A Fast, Extensible Toolkit for Sequence Modeling. In <em>Proceedings  of the 2019 Conference of the North American Chapter of the Association  for Computational Linguistics (Demonstrations)</em>, pages 48–53, Minneapolis, Minnesota. Association for Computational Linguistics.
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Full documentation to how to use the resources is included in the [GitHub repository](https://github.com/st-vincent1/MTCue) which contains a link to this ORDA page.