To get a Chinese DeBERTa-v2-xlarge (710M), we use WuDao Corpora (180 GB version) for pre-training. We employ the Whole Word Masking (wwm) in MLM. Specifically, we use the fengshen framework in the pre-training phase which cost about 21 days with 24 A100（40G） GPUs.

下游任务 Performance

我们展示了下列下游任务的结果：

We present the results on the following tasks:

Model	AFQMC	TNEWS1.1	IFLYTEK	OCNLI	CMNLI
RoBERTa-base	0.7406	0.575	0.6036	0.743	0.7973
RoBERTa-large	0.7488	0.5879	0.6152	0.777	0.814
IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese	0.7405	0.571	0.5977	0.7568	0.807
IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese	0.7498	0.5817	0.6042	0.8022	0.8301
IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese	0.7549	0.5873	0.6177	0.8012	0.8389

使用 Usage

python

from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
import torch

tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese', use_fast=False)
model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese')
text = '生活的真谛是[MASK]。'
fillmask_pipe = FillMaskPipeline(model, tokenizer, device=-1)
print(fillmask_pipe(text, top_k=10))

引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的论文：

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

text

@article{fengshenbang,
  author    = {Jiaxing Zhang and Ruyi Gan and 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},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}
}

也可以引用我们的网站:

You can also cite our website:

text

@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}

LYUQ/Erlangshen-DeBERTa-v2-710M-Chinese

Erlangshen-DeBERTa-v2-710M-Chinese

简介 Brief Introduction

模型分类 Model Taxonomy

模型信息 Model Information

下游任务 Performance

使用 Usage

引用 Citation

LYUQ/
Erlangshen-DeBERTa-v2-710M-Chinese