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@@ -14,9 +14,9 @@ widget:
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  - text: "Texto de exemplo em português"
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  inference: false
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  ---
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- # PTT5-base Reranker finetuned on Portuguese MS MARCO
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  ## Introduction
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- ptt5-base-msmarco-pt-100k is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. This model was finetuned for 100k steps.
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  Further information about the dataset or the translation method can be found on our [**mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository.
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  ## Usage
@@ -24,17 +24,17 @@ Further information about the dataset or the translation method can be found on
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  from transformers import T5Tokenizer, T5ForConditionalGeneration
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- model_name = 'unicamp-dl/ptt5-base-msmarco-pt-100k'
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  tokenizer = T5Tokenizer.from_pretrained(model_name)
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  model = T5ForConditionalGeneration.from_pretrained(model_name)
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  ```
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  # Citation
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- If you use ptt5-base-msmarco-pt-100k, please cite:
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  @misc{bonifacio2021mmarco,
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  title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset},
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- author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
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  year={2021},
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  eprint={2108.13897},
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  archivePrefix={arXiv},
 
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  - text: "Texto de exemplo em português"
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  inference: false
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  ---
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+ # PTT5-base-msmarco-pt-100k-v1 Reranker finetuned on Portuguese MS MARCO
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  ## Introduction
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+ ptt5-base-msmarco-pt-100k-v1 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using [Helsinki](https://huggingface.co/Helsinki-NLP) NMT model. This model was finetuned for 100k steps.
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  Further information about the dataset or the translation method can be found on our [**mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository.
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  ## Usage
 
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  from transformers import T5Tokenizer, T5ForConditionalGeneration
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+ model_name = 'unicamp-dl/ptt5-base-msmarco-pt-100k-v1'
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  tokenizer = T5Tokenizer.from_pretrained(model_name)
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  model = T5ForConditionalGeneration.from_pretrained(model_name)
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  ```
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  # Citation
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+ If you use ptt5-base-msmarco-pt-100k-v1, please cite:
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  @misc{bonifacio2021mmarco,
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  title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset},
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+ author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo and Rodrigo Nogueira},
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  year={2021},
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  eprint={2108.13897},
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  archivePrefix={arXiv},