# mt5-base Reranker finetuned on mMARCO
## Introduction
mt5-base-mmarco-v1 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages.  In the version v1, the datasets were translated using [Helsinki](https://huggingface.co/Helsinki-NLP) NMT models.
Further information about the dataset or the translation method can be found on our paper [**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, MT5ForConditionalGeneration

model_name = 'unicamp-dl/mt5-base-mmarco-v1'
tokenizer  = T5Tokenizer.from_pretrained(model_name)
model      = MT5ForConditionalGeneration.from_pretrained(model_name)

```
# Citation
If you use mt5-base-mmarco-v1, 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\u00c3\u00aas"}], "inference": false}
transformers pytorch mt5 text2text-generation msmarco t5 tensorflow pt pt-br dataset:msmarco arxiv:2108.13897 license:mit autotrain_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us 

# mt5-base Reranker finetuned on mMARCO
## Introduction
mt5-base-mmarco-v1 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages.  In the version v1, the datasets were translated using Helsinki NMT models.
Further information about the dataset or the translation method can be found on our paper mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository.

## Usage

If you use mt5-base-mmarco-v1, 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}
}
# mt5-base Reranker finetuned on mMARCO ## Introduction
mt5-base-mmarco-v1 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages.  In the version v1, the datasets were translated using Helsinki NMT models.
Further information about the dataset or the translation method can be found on our paper mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository. ## Usage

If you use mt5-base-mmarco-v1, 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 #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us 
 # mt5-base Reranker finetuned on mMARCO ## Introduction
mt5-base-mmarco-v1 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages.  In the version v1, the datasets were translated using Helsinki NMT models.
Further information about the dataset or the translation method can be found on our paper mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository. ## Usage

If you use mt5-base-mmarco-v1, 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 #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us 
# mt5-base Reranker finetuned on mMARCO## Introduction
mt5-base-mmarco-v1 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages.  In the version v1, the datasets were translated using Helsinki NMT models.
Further information about the dataset or the translation method can be found on our paper mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository.## Usage

If you use mt5-base-mmarco-v1, 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}
}