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--- |
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license: apache-2.0 |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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inference: |
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parameters: |
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do_sample: true |
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max_length: 64 |
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top_k: 10 |
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temperature: 1 |
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num_return_sequences: 10 |
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widget: |
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- text: >- |
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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. |
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- text: >- |
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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. |
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--- |
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## Model description |
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mT5-base query generation model that is trained with XOR QA data. |
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Used in paper [Bridging the Gap Between Indexing and Retrieval for |
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Differentiable Search Index with Query Generation](https://arxiv.org/pdf/2206.10128.pdf) |
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and [Augmenting Passage Representations with Query Generation |
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for Enhanced Cross-Lingual Dense Retrieval](https://arxiv.org/pdf/2305.03950.pdf) |
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### How to use |
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```python |
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from transformers import pipeline |
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lang2mT5 = dict( |
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ar='Arabic', |
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bn='Bengali', |
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fi='Finnish', |
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ja='Japanese', |
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ko='Korean', |
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ru='Russian', |
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te='Telugu' |
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) |
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PROMPT = 'Generate a {lang} question for this passage: {title} {passage}' |
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title = 'Transformer (machine learning model)' |
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passage = 'A transformer is a deep learning model that adopts the mechanism of self-attention, differentially ' \ |
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'weighting the significance of each part of the input (which includes the recursive output) data.' |
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model_name_or_path = 'ielabgroup/xor-tydi-docTquery-mt5-base' |
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input_text = PROMPT.format_map({'lang': lang2mT5['ja'], |
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'title': title, |
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'passage': passage}) |
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generator = pipeline(model=model_name_or_path, |
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task='text2text-generation', |
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device="cuda:0", |
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) |
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results = generator(input_text, |
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do_sample=True, |
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max_length=64, |
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num_return_sequences=10, |
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) |
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for i, result in enumerate(results): |
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print(f'{i + 1}. {result["generated_text"]}') |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@article{zhuang2022bridging, |
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title={Bridging the gap between indexing and retrieval for differentiable search index with query generation}, |
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author={Zhuang, Shengyao and Ren, Houxing and Shou, Linjun and Pei, Jian and Gong, Ming and Zuccon, Guido and Jiang, Daxin}, |
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journal={arXiv preprint arXiv:2206.10128}, |
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year={2022} |
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} |
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@inproceedings{zhuang2023augmenting, |
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title={Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval}, |
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author={Zhuang, Shengyao and Shou, Linjun and Zuccon, Guido}, |
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booktitle={Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval}, |
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year={2023} |
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} |
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``` |
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