简介 Brief Introduction


Good at solving NLU tasks, adopting Chinese Word Segmentation (CWS), Chinese DeBERTa-v2-Base with 97M parameters.

模型分类 Model Taxonomy

需求 Demand 任务 Task 系列 Series 模型 Model 参数 Parameter 额外 Extra
通用 General 自然语言理解 NLU 二郎神 Erlangshen DeBERTa-v2 97M 中文分词-中文 CWS-Chinese

模型信息 Model Information

参考论文:DeBERTa: Decoding-enhanced BERT with Disentangled Attention


To get a Chinese DeBERTa-v2-Base (97M), we use WuDao Corpora (180 GB version) for pre-training. We employ Chinese Word Segmentation (CWS). Specifically, we use the fengshen framework in the pre-training phase which cost about 7 days with 24 A100 GPUs.

使用 Usage

模型下载地址 Download Address


加载模型 Loading Models

from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
import torch

tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-CWS-Chinese', use_fast=False)
text = '生活的真谛是[MASK]。'
fillmask_pipe = FillMaskPipeline(model, tokenizer, device=7)
print(fillmask_pipe(text, top_k=10))

引用 Citation


If you are using the resource for your work, please cite the our paper:

  author    = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}


You can also cite our website: