# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 100k steps. 
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.
## Usage
```python

from transformers import T5Tokenizer, T5ForConditionalGeneration

model_name = 'unicamp-dl/ptt5-base-msmarco-en-pt-100k-v2'
tokenizer  = T5Tokenizer.from_pretrained(model_name)
model      = T5ForConditionalGeneration.from_pretrained(model_name)

```
# Citation
If you use ptt5-base-msmarco-en-pt-100k-v2, please cite:

    @misc{bonifacio2021mmarco,
      title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, 
      author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and  and Roberto Lotufo and Rodrigo Nogueira},
      year={2021},
      eprint={2108.13897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


{"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false}
transformers pytorch t5 text2text-generation msmarco tensorflow pt pt-br dataset:msmarco arxiv:2108.13897 license:mit autotrain_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us 

# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 100k steps. 
Further information about the dataset or the translation method can be found on our mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository.
## Usage

If you use ptt5-base-msmarco-en-pt-100k-v2, please cite:

    @misc{bonifacio2021mmarco,
      title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, 
      author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and  and Roberto Lotufo and Rodrigo Nogueira},
      year={2021},
      eprint={2108.13897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO ## Introduction
ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 100k steps. 
Further information about the dataset or the translation method can be found on our mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository. ## Usage

If you use ptt5-base-msmarco-en-pt-100k-v2, please cite:

    @misc{bonifacio2021mmarco,
      title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, 
      author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and  and Roberto Lotufo and Rodrigo Nogueira},
      year={2021},
      eprint={2108.13897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us 
 # PTT5-base Reranker finetuned on both English and Portuguese MS MARCO ## Introduction
ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 100k steps. 
Further information about the dataset or the translation method can be found on our mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository. ## Usage

If you use ptt5-base-msmarco-en-pt-100k-v2, please cite:

    @misc{bonifacio2021mmarco,
      title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, 
      author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and  and Roberto Lotufo and Rodrigo Nogueira},
      year={2021},
      eprint={2108.13897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us 
# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO## Introduction
ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 100k steps. 
Further information about the dataset or the translation method can be found on our mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository.## Usage

If you use ptt5-base-msmarco-en-pt-100k-v2, please cite:

    @misc{bonifacio2021mmarco,
      title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, 
      author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and  and Roberto Lotufo and Rodrigo Nogueira},
      year={2021},
      eprint={2108.13897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}