metadata

language:
  - zh
license: apache-2.0
widget:
  - text: 生活的真谛是[MASK]。

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}
}