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--- |
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language: |
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- zh |
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license: apache-2.0 |
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widget: |
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- text: "生活的真谛是[MASK]。" |
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--- |
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# Mengzi-BERT base model (Chinese) |
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Pretrained model on 300G Chinese corpus. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task. |
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[Mengzi: A lightweight yet Powerful Chinese Pre-trained Language Model](https://arxiv.org/abs/2110.06696) |
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## Usage |
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```python |
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from transformers import BertTokenizer, BertModel |
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tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base") |
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model = BertModel.from_pretrained("Langboat/mengzi-bert-base") |
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``` |
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## Scores on nine chinese tasks (without any data augmentation) |
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| Model | AFQMC | TNEWS | IFLYTEK | CMNLI | WSC | CSL | CMRC2018 | C3 | CHID | |
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|-|-|-|-|-|-|-|-|-|-| |
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|RoBERTa-wwm-ext| 74.30 | 57.51 | 60.80 | 80.70 | 67.20 | 80.67 | 77.59 | 67.06 | 83.78 | |
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|Mengzi-BERT-base| 74.58 | 57.97 | 60.68 | 82.12 | 87.50 | 85.40 | 78.54 | 71.70 | 84.16 | |
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RoBERTa-wwm-ext scores are from CLUE baseline |
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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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``` |
