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@@ -23,7 +23,7 @@ The further pre-training is applied with a MLM objective (randomly masking 15\%
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  ## BureauBERTo model can be loaded like:
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- ```
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  from transformers import AutoModel, CamembertTokenizerFast
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  model_name = "colinglab/BureauBERTo"
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  tokenizer = CamembertTokenizerFast.from_pretrained(model_name)
@@ -37,7 +37,7 @@ If you find our resource or paper useful, please consider including the followin
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  @inproceedings{auriemma2023bureauberto,
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  address = {Pisa, Italy},
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  series = {{CEUR} {Workshop} {Proceedings}},
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- title = {{BureauBERTo}: adapting {UmBERTo} to the {Italian} bureaucratic language,
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  shorttitle = {{BureauBERTo}},
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  language = {en},
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  booktitle = {Ital-IA Convegno Nazionale CINI sull'Intelligenza Artificiale-Workshop on AI for Cybersecurity},
 
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  ## BureauBERTo model can be loaded like:
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+ ```python
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  from transformers import AutoModel, CamembertTokenizerFast
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  model_name = "colinglab/BureauBERTo"
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  tokenizer = CamembertTokenizerFast.from_pretrained(model_name)
 
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  @inproceedings{auriemma2023bureauberto,
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  address = {Pisa, Italy},
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  series = {{CEUR} {Workshop} {Proceedings}},
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+ title = {{BureauBERTo}: adapting {UmBERTo} to the {Italian} bureaucratic language},
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  shorttitle = {{BureauBERTo}},
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  language = {en},
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  booktitle = {Ital-IA Convegno Nazionale CINI sull'Intelligenza Artificiale-Workshop on AI for Cybersecurity},