README.md

metadata

license: apache-2.0
library_name: transformers
pipeline_tag: text2text-generation

Model description

mT5-base query generation model that is trained with XOR QA data.

Used in paper Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation

and Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval

How to use

from transformers import pipeline

lang2mT5 = dict(
    ar='Arabic',
    bn='Bengali',
    fi='Finnish',
    ja='Japanese',
    ko='Korean',
    ru='Russian',
    te='Telugu'
)
PROMPT = 'Generate a {lang} question for this passage: {title} {passage}'

title = 'Transformer (machine learning model)'
passage = 'A transformer is a deep learning model that adopts the mechanism of self-attention, differentially ' \
          'weighting the significance of each part of the input (which includes the recursive output) data.'


model_name_or_path = 'ielabgroup/xor-tydi-docTquery-mt5-base'
input_text = PROMPT.format_map({'lang': lang2mT5['ja'],
                                'title': title,
                                'passage': passage})

generator = pipeline(model=model_name_or_path,
                     task='text2text-generation',
                     device="cuda:0",
                     )

results = generator(input_text,
                    do_sample=True,
                    max_length=64,
                    num_return_sequences=10,
                    )

for i, result in enumerate(results):
    print(f'{i + 1}. {result["generated_text"]}')

BibTeX entry and citation info

@article{zhuang2022bridging,
  title={Bridging the gap between indexing and retrieval for differentiable search index with query generation},
  author={Zhuang, Shengyao and Ren, Houxing and Shou, Linjun and Pei, Jian and Gong, Ming and Zuccon, Guido and Jiang, Daxin},
  journal={arXiv preprint arXiv:2206.10128},
  year={2022}
}