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@@ -40,4 +40,37 @@ metrics:
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  accuracy 0.98 54504270
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  macro avg 0.83 0.75 0.78 54504270
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  weighted avg 0.98 0.98 0.98 54504270
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  accuracy 0.98 54504270
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  macro avg 0.83 0.75 0.78 54504270
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  weighted avg 0.98 0.98 0.98 54504270
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+ ```
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+
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+
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+
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+
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+ ## How to cite us
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+
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+ ```
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+ @article{guhr-EtAl:2021:fullstop,
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+ title={FullStop: Multilingual Deep Models for Punctuation Prediction},
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+ author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim},
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+ booktitle = {Proceedings of the Swiss Text Analytics Conference 2021},
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+ month = {June},
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+ year = {2021},
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+ address = {Winterthur, Switzerland},
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+ publisher = {CEUR Workshop Proceedings},
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+ url = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf}
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+ }
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+
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+ ```
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+
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+ ```
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+ @misc{https://doi.org/10.48550/arxiv.2301.03319,
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+ doi = {10.48550/ARXIV.2301.03319},
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+ url = {https://arxiv.org/abs/2301.03319},
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+ author = {Vandeghinste, Vincent and Guhr, Oliver},
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+ keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},
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+ title = {FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers},
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+ publisher = {arXiv},
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+ year = {2023},
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+ copyright = {Creative Commons Attribution Share Alike 4.0 International}
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+ }
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+
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+ ```