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license: afl-3.0 |
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**Please use 'Bert' related tokenizer classes and 'Nezha' related model classes** |
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[NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) |
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Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu. |
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The original checkpoints can be found [here](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-PyTorch) |
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## Example Usage |
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``` |
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from transformers import BertTokenizer, NezhaModel |
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tokenizer = BertTokenizer.from_pretrained("sijunhe/nezha-base-wwm") |
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model = NezhaModel.from_pretrained("sijunhe/nezha-base-wwm") |
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text = "我爱北京天安门" |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
