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  ## Model description
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- This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://arxiv.org/abs/1909.05658).
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- [Turc et al.](https://arxiv.org/abs/1908.08962) have shown that the standard BERT recipe is effective on a wide range of model sizes. Following their paper, we released the 6 Chinese Whole Word Masking RoBERTa models. In order to facilitate users to reproduce the results, we used the publicly available corpus and word segmentation tool, and provided all training details.
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  You can download the 6 Chinese RoBERTa miniatures either from the [UER-py Github page](https://github.com/dbiir/UER-py/), or via HuggingFace from the links below:
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  pages={241},
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  year={2019}
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  }
 
 
 
 
 
 
 
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  ```
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  [2_128]:https://huggingface.co/uer/roberta-tiny-wwm-chinese-cluecorpussmall
 
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  ## Model description
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+ This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.
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+ [Turc et al.](https://arxiv.org/abs/1908.08962) have shown that the standard BERT recipe is effective on a wide range of model sizes. Following their paper, we released the 6 Chinese Whole Word Masking RoBERTa models. In order to facilitate users in reproducing the results, we used a publicly available corpus and word segmentation tool, and provided all training details.
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  You can download the 6 Chinese RoBERTa miniatures either from the [UER-py Github page](https://github.com/dbiir/UER-py/), or via HuggingFace from the links below:
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  pages={241},
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  year={2019}
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  }
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+
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+ @article{zhao2023tencentpretrain,
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+ title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
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+ author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
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+ journal={ACL 2023},
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+ pages={217},
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+ year={2023}
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  ```
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  [2_128]:https://huggingface.co/uer/roberta-tiny-wwm-chinese-cluecorpussmall