mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset
Paper • 2108.13897 • Published
How to use unicamp-dl/mMiniLM-L6-v2-pt-v2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="unicamp-dl/mMiniLM-L6-v2-pt-v2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/mMiniLM-L6-v2-pt-v2")
model = AutoModelForSequenceClassification.from_pretrained("unicamp-dl/mMiniLM-L6-v2-pt-v2")mMiniLM-L6-v2-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. 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.
from transformers import AutoTokenizer, AutoModel
model_name = 'unicamp-dl/mMiniLM-L6-v2-pt-msmarco-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
If you use mMiniLM-L6-v2-pt-msmarco-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}
}