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Mengzi-BERT base model (Chinese)

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.

Mengzi: A lightweight yet Powerful Chinese Pre-trained Language Model

Usage

from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base")
model = BertModel.from_pretrained("Langboat/mengzi-bert-base")

Scores on nine chinese tasks (without any data augmentation)

Model AFQMC TNEWS IFLYTEK CMNLI WSC CSL CMRC2018 C3 CHID
RoBERTa-wwm-ext 74.30 57.51 60.80 80.70 67.20 80.67 77.59 67.06 83.78
Mengzi-BERT-base 74.58 57.97 60.68 82.12 87.50 85.40 78.54 71.70 84.16

RoBERTa-wwm-ext scores are from CLUE baseline

Citation

If you find the technical report or resource is useful, please cite the following technical report in your paper.

@misc{zhang2021mengzi,
      title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese}, 
      author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou},
      year={2021},
      eprint={2110.06696},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
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