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--- |
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language: en |
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tags: |
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- question-answering |
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--- |
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# ReAtt |
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ReAtt is a retrieval-augmented model for knowledge-intensive tasks proposed in [Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer](https://arxiv.org/pdf/2212.02027.pdf). The original Github repository is [https://github.com/jzbjyb/ReAtt](https://github.com/jzbjyb/ReAtt). |
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## Description |
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`neulab/reatt-large-nq` (based on T5 architecture) is initialized with `google/t5-large-lm-adapt` and fine-tuned on Natural Questions with end-to-end retrieval-augmented training. |
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## Usage |
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Please refer to [https://github.com/jzbjyb/ReAtt](https://github.com/jzbjyb/ReAtt) for instructions to use this model. |
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## Reference |
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```bibtex |
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@inproceedings{jiang-etal-2022-reatt, |
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title = {Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer}, |
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author = {Zhengbao Jiang and Luyu Gao and Jun Araki and Haibo Ding and Zhiruo Wang and Jamie Callan and Graham Neubig}, |
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booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)}, |
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address = {Abu Dhabi, UAE}, |
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month = {December}, |
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year = {2022} |
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} |
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``` |
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