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@@ -60,12 +60,12 @@ be run on consumer-grade hardware.
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  **Gervásio 7B PT-PT** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
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  For the record, its full name is **Gervásio Produz Textos em Português**, to which corresponds the natural acronym **GPT PT**,
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- and which is know tough more shortly as **Gervásio PT-***, or even more briefly just as **Gervásio**, among his acquaintances.
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  These models are fully documented in the respective [publication](https://arxiv.org/abs/?):
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  ``` latex
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- @misc{albertina-pt,
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  title={Advancing Generative AI for Portuguese with Open Decoder Gervásio~PT*},
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  author={Rodrigo Santos, João Silva, Luís Gomes, João Rodrigues, António Branco},
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  year={2024},
@@ -95,7 +95,7 @@ Gervásio-7B-PTPT-Decoder is distributed under an [MIT license](https://huggingf
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  **Gervásio 7B PT-PT** was trained over standard supervised fine-tuning, and to keep some alignment with mainstream benchmarks for English, we resorted to tasks and respective datasets in the GLUE and the SuperGLUE collections.
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- We selected those datasets where the outcome of their machine translation into Portuguese could preserve, in the target language, the linguistic properties at stake.
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  From GLUE, we resorted to the following four tasks:
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  - MRPC (paraphrase Detection).
 
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  **Gervásio 7B PT-PT** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
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  For the record, its full name is **Gervásio Produz Textos em Português**, to which corresponds the natural acronym **GPT PT**,
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+ and which is known more shortly as Gervásio PT-* or, even more briefly, just as Gervásio, among its acquaintances.
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  These models are fully documented in the respective [publication](https://arxiv.org/abs/?):
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  ``` latex
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+ @misc{gervasio,
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  title={Advancing Generative AI for Portuguese with Open Decoder Gervásio~PT*},
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  author={Rodrigo Santos, João Silva, Luís Gomes, João Rodrigues, António Branco},
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  year={2024},
 
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  **Gervásio 7B PT-PT** was trained over standard supervised fine-tuning, and to keep some alignment with mainstream benchmarks for English, we resorted to tasks and respective datasets in the GLUE and the SuperGLUE collections.
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+ We selected those datasets where the outcome of their machine translation into European Portuguese could preserve, in the target language, the linguistic properties at stake.
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  From GLUE, we resorted to the following four tasks:
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  - MRPC (paraphrase Detection).