
## Model description

mT5-large 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](https://arxiv.org/pdf/2206.10128.pdf)

and [Augmenting Passage Representations with Query Generation
for Enhanced Cross-Lingual Dense Retrieval](https://arxiv.org/pdf/2305.03950.pdf)

### How to use
```python
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-large'
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

```bibtex
@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}
}
```
{"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."}]}
transformers pytorch safetensors mt5 text2text-generation arxiv:2206.10128 arxiv:2305.03950 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #arxiv-2206.10128 #arxiv-2305.03950 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


## Model description

mT5-large 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


### BibTeX entry and citation info


## Model description

mT5-large 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 ### BibTeX entry and citation info
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #arxiv-2206.10128 #arxiv-2305.03950 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ## Model description

mT5-large 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 ### BibTeX entry and citation info