metadata

language:
  - zh
license: apache-2.0
tags:
  - DeBERTa
  - CWS
  - Chinese Word Segmentation
  - Chinese
inference: false

Erlangshen-DeBERTa-v2-97M-CWS-Chinese

Main Page:Fengshenbang
Github: Fengshenbang-LM

简介 Brief Introduction

善于处理NLU任务，采用中文分词的，中文版的0.97亿参数DeBERTa-v2-Base。

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

为了得到一个中文版的DeBERTa-v2-Base（97M），我们用悟道语料库(180G版本)进行预训练。我们使用了中文分词。具体地，我们在预训练阶段中使用了封神框架大概花费了24张A100约7天。

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

from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
import torch

tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-CWS-Chinese', use_fast=False)
model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-CWS-Chinese')
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

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:

@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:

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