Xor Tydi Doctquery Mt5 Large

🚀 mT5-largeクエリ生成モデル

このモデルは、XOR QAデータで学習されたmT5-largeクエリ生成モデルです。異なる言語間の検索インデックスと検索のギャップを埋めるために使用され、クロス言語の密な検索を強化するのに役立ちます。

🚀 クイックスタート

モデルの説明

mT5-largeクエリ生成モデルは、XOR QAデータで学習されています。以下の論文で使用されています。

• Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
• Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval

使い方

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エントリと引用情報

@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}
}

📄 ライセンス

このプロジェクトはApache-2.0ライセンスの下で提供されています。