# mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version.  In the version v1, the Portuguese dataset was translated using [Helsinki](https://huggingface.co/Helsinki-NLP) NMT model.
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 AutoTokenizer, AutoModel

model_name = 'unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v1'
tokenizer  = AutoTokenizer.from_pretrained(model_name)
model      = AutoModel.from_pretrained(model_name)

```
# Citation
If you use mMiniLM-L6-v2-en-pt-msmarco-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", "miniLM", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false}
transformers pytorch xlm-roberta text-classification msmarco miniLM tensorflow pt pt-br dataset:msmarco arxiv:2108.13897 license:mit autotrain_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us 

# mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version.  In the version v1, the Portuguese dataset was translated using Helsinki NMT model.
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 mMiniLM-L6-v2-en-pt-msmarco-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}
}


# mMiniLM-L6-v2 Reranker finetuned on mMARCO ## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version.  In the version v1, the Portuguese dataset was translated using Helsinki NMT model.
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 mMiniLM-L6-v2-en-pt-msmarco-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 #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us 
 # mMiniLM-L6-v2 Reranker finetuned on mMARCO ## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version.  In the version v1, the Portuguese dataset was translated using Helsinki NMT model.
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 mMiniLM-L6-v2-en-pt-msmarco-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 #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us 
# mMiniLM-L6-v2 Reranker finetuned on mMARCO## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version.  In the version v1, the Portuguese dataset was translated using Helsinki NMT model.
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 mMiniLM-L6-v2-en-pt-msmarco-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}
}