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license: apache-2.0 |
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Pretrained models for our paper (https://arxiv.org/abs/2210.08431) |
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```bibtex |
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@inproceedings{wu-etal-2022-modeling, |
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title = "Modeling Context With Linear Attention for Scalable Document-Level Translation", |
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author = "Zhaofeng Wu and Hao Peng and Nikolaos Pappas and Noah A. Smith", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", |
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month = dec, |
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year = "2022", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
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Please see the "Files and versions" tab for the models. You can find our IWSLT models and our OpenSubtitles models that are early-stopped based on BLEU and consistency scores, respectively. The `c` part in the checkpoint name refers to the number of context sentences used; it is the same as the sliding window size (the `L` in our paper) minus one. |
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