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The set of GPT2 models, except for GPT2-xlarge model, are pre-trained by UER-py, which is introduced in this paper. The GPT2-xlarge model is pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. Besides, the other models could also be pre-trained by TencentPretrain. The model is used to generate Chinese texts. You can download the set of Chinese GPT2 models either from the UER-py Modelzoo page, or via HuggingFace from the links below: Note that the 6-layer model is called GPT2-distil model because it follows the configuration of distilgpt2, and the pre-training does not involve the supervision of larger models.

Text generation
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This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py, which is introduced in this paper. Besides, the models could also be fine-tuned by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. You can download the 5 Chinese RoBERTa-Base classification models either from the UER-py Modelzoo page, or via HuggingFace from the links below: You can use this model directly with a pipeline for text classification (take the case of roberta-base-finetuned-chinanews-chinese):

Text analysis
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