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README.md
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- text: "Texto de exemplo em português"
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inference: false
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---
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#
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## Introduction
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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/
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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
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@misc{bonifacio2021mmarco,
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title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset},
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author={Luiz Henrique Bonifacio and Israel Campiotti and
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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-en-msmarco Reranker finetuned on English MS MARCO
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## Introduction
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mMiniLM-L6-v2-pt-msmarco is a multilingual miniLM-based model fine-tuned on English MS MARCO passage dataset. 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-en-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 mMiniLM-L6-v2-en-msmarco, please cite:
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@misc{bonifacio2021mmarco,
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title={mMARCO: A Multilingual Version of the 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},
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