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@@ -14,25 +14,25 @@ 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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- # multilingual-MiniLM-L6-v2-multi-msmarco Reranker finetuned on mMARCO
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  ## Introduction
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- multilingual-MiniLM-L6-v2-multi-msmarco is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 8 different languages, translated from English MS MARCO passages collection.
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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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  ```python
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  from transformers import AutoTokenizer, AutoModel
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- model_name = 'unicamp-dl/multilingual-MiniLM-L6-v2-multi-msmarco'
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModel.from_pretrained(model_name)
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  ```
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  # Citation
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- If you use multilingual-MiniLM-L6-v2-multi-msmarco, 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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+ # mMiniLM-L6-v2-mmarco-v1 Reranker finetuned on mMARCO
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  ## Introduction
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+ mMiniLM-L6-v2-mmarco-v1 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages collection.
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+ In the version v1, the datasets were 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.
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  ## Usage
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  ```python
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  from transformers import AutoTokenizer, AutoModel
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+ model_name = 'unicamp-dl/mMiniLM-L6-v2-mmarco-v1'
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModel.from_pretrained(model_name)
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  ```
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  # Citation
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+ If you use mMiniLM-L6-v2-mmarco-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},