metadata
license: apache-2.0
library_name: transformers
pipeline_tag: text2text-generation
inference:
parameters:
do_sample: true
max_length: 64
top_k: 10
temperature: 1
num_return_sequences: 10
widget:
- text: >-
Generate a Japanese question for this passage: Transformer (machine
learning model) 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.
- text: >-
Generate a Arabic question for this passage: Transformer (machine learning
model) 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 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}
}
@inproceedings{zhuang2023augmenting,
title={Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval},
author={Zhuang, Shengyao and Shou, Linjun and Zuccon, Guido},
booktitle={Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval},
year={2023}
}