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README.md
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license: apache-2.0
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---
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# Mengzi-oscar-base (Chinese Multi-modal pre-training model)
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Mengzi-oscar is trained based on the Multi-modal pre-training model [Oscar](https://github.com/microsoft/Oscar), and is initialized using [Mengzi-Bert-Base](https://github.com/Langboat/Mengzi). 3.7M pairs of images and texts were used, including 0.7M Chinese image-caption pairs, 3M Chinese image-question pairs, a total of 0.22M different images.
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## Usage
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#### Installation
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Check [INSTALL.md](https://github.com/microsoft/Oscar/blob/master/INSTALL.md) for installation instructions.
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## Citation
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If you find the technical report or resource is useful, please cite the following technical report in your paper.
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```
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```
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license: apache-2.0
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---
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# Mengzi-oscar-base (Chinese Multi-modal pre-training model)
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Mengzi-oscar is trained based on the Multi-modal pre-training model [Oscar](https://github.com/microsoft/Oscar), and is initialized using [Mengzi-Bert-Base](https://github.com/Langboat/Mengzi). 3.7M pairs of images and texts were used, including 0.7M Chinese image-caption pairs, 3M Chinese image-question pairs, a total of 0.22M different images.
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[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
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## Usage
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#### Installation
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Check [INSTALL.md](https://github.com/microsoft/Oscar/blob/master/INSTALL.md) for installation instructions.
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## Citation
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If you find the technical report or resource is useful, please cite the following technical report in your paper.
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```
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@misc{zhang2021mengzi,
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title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese},
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author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou},
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year={2021},
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eprint={2110.06696},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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