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@@ -46,16 +46,18 @@ We can observe the following:
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  ## More details
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- Testing model with **generated** test sets provides following result:
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-
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- - `1` token prediction -> Precission: 0,911; Recall: 0,975; F1: 0,942
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-
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- Testing the model with test sets constructed using the **Šolar Eval** dataset provides the following results:
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-
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-
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- - `1` token prediction -> Precission: 0,900; Recall: 0,860; F1: 0,880
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- - `2` token prediction -> Precission: 0,826; Recall:0,853; F1: 0,839
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- - `3` token prediction -> Precission: 0,518; Recall: 0,671; F1: 0,585
 
 
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  ## Acknowledgement
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  ## More details
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+ The model, along with its training and evaluation, is described in more detail in the following paper.
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+ ```
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+ @inproceedings{neural-spell-checker,
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+ author = {Klemen, Matej and Bo\v{z}i\v{c}, Martin and Holdt, \v{S}pela Arhar and Robnik-\v{S}ikonja, Marko},
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+ title = {Neural Spell-Checker: Beyond Words with Synthetic Data Generation},
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+ year = {2024},
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+ doi = {10.1007/978-3-031-70563-2_7},
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+ booktitle = {Text, Speech, and Dialogue: 27th International Conference, TSD 2024, Brno, Czech Republic, September 9–13, 2024, Proceedings, Part I},
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+ pages = {85–96},
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+ numpages = {12}
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+ }
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+ ```
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  ## Acknowledgement
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