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  ## Model description
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- Cased fine-tuned XLM-RoBERTa-large model for Hungarian, trained to classify **sentences** based on their Plain Language properties.
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  ## Intended uses & limitations
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@@ -56,10 +56,14 @@ tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/Hun_RoBERTa_large_Plain"
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  model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/Hun_RoBERTa_large_Plain")
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  ```
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- # Citation:
 
 
 
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  @PhDThesis{ Uveges:2024,
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  author = {{"U}veges, Istv{\'a}n},
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  title = {K{\"o}z{\'e}rthet{\"o} és automatiz{\'a}ci{\'o} - k{\'i}s{\'e}rletek a jog, term{\'e}szetesnyelv-feldolgoz{\'a}s {\'e}s informatika hat{\'a}r{\'a}n.},
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  year = {2024},
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  school = {Szegedi Tudom{\'a}nyegyetem}
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- }
 
 
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  ## Model description
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+ Cased fine-tuned XLM-RoBERTa-large model for Hungarian, trained on a dataset (~13k sentences) provided by National Tax and Customs Administration - Hungary (NAV): Public Accessibilty Programme.
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  ## Intended uses & limitations
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  model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/Hun_RoBERTa_large_Plain")
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  ```
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+ # Citation
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+
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+ Bibtex:
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+ ```bibtex
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  @PhDThesis{ Uveges:2024,
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  author = {{"U}veges, Istv{\'a}n},
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  title = {K{\"o}z{\'e}rthet{\"o} és automatiz{\'a}ci{\'o} - k{\'i}s{\'e}rletek a jog, term{\'e}szetesnyelv-feldolgoz{\'a}s {\'e}s informatika hat{\'a}r{\'a}n.},
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  year = {2024},
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  school = {Szegedi Tudom{\'a}nyegyetem}
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+ }
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+ ```