Erlangshen-ZEN2-345M-Chinese

主页面：封神榜
代码仓库：Fengshenbang-LM

简介 Brief Introduction

ZEN2模型参数量达3.45亿，采用N-gram编码技术增强文本语义理解，擅长处理自然语言理解（NLU）任务。

ZEN2 model, which uses N-gram to enhance text semantic and has 345M parameters, is adept at NLU tasks.

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	ZEN2	345M	中文-Chinese

模型信息 Model Information

我们与ZEN团队合作，基于封神框架开源发布了ZEN2模型。具体来说，ZEN通过整合无监督学习提取的知识，利用N-gram方法学习不同粒度的文本信息。ZEN2则采用大规模数据集和特殊的预训练策略，对N-gram增强编码器进行预训练。接下来，我们将继续与ZEN团队合作，探索预训练语言模型（PLM）的优化方法，以提升下游任务性能。

We open source and publicly release ZEN2 using our Fengshen Framework in collaboration with the ZEN team. More precisely, by bringing together knowledge extracted by unsupervised learning, ZEN learns different textual granularity information through N-gram methods. ZEN2 pre-trains the N-gram-enhanced encoders with large-scale datasets and special pre-training strategies. In the next step, we continue with the ZEN team to explore the optimization of PLM and improve the performance on downstream tasks.

下游效果 Performance

分类任务 Classification

模型(准确率)	afqmc	tnews	iflytek	ocnli	cmnli
Erlangshen-ZEN2-345M-Chinese	0.741	0.584	0.599	0.788	0.80
Erlangshen-ZEN2-668M-Chinese	0.75	0.60	0.589	0.81	0.82

抽取任务 Extraction

模型(F1值)	WEIBO(test)	Resume(test)	MSRA(test)	OntoNote4.0(test)	CMeEE(dev)	CLUENER(dev)
Erlangshen-ZEN2-345M-Chinese	65.26	96.03	95.15	78.93	62.81	79.27
Erlangshen-ZEN2-668M-Chinese	70.02	96.08	95.13	80.89	63.37	79.22

使用 Usage

由于transformers库中未包含ZEN2相关的模型结构，因此您可以在我们的Fengshenbang-LM中找到该模型结构并运行代码。

Since there is no structure of ZEN2 in transformers library, you can find the structure of ZEN2 and run the codes in Fengshenbang-LM.

 git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git

from fengshen.models.zen2.ngram_utils import ZenNgramDict
from fengshen.models.zen2.tokenization import BertTokenizer
from fengshen.models.zen2.modeling import ZenForSequenceClassification, ZenForTokenClassification

pretrain_path = 'IDEA-CCNL/Erlangshen-ZEN2-345M-Chinese'

tokenizer = BertTokenizer.from_pretrained(pretrain_path)
model_classification = ZenForSequenceClassification.from_pretrained(pretrain_path)
model_extraction = ZenForTokenClassification.from_pretrained(pretrain_path)
ngram_dict = ZenNgramDict.from_pretrained(pretrain_path, tokenizer=tokenizer)

你可以从下方的链接获得我们做分类和抽取的详细示例。

You can get classification and extraction examples below.

分类 classification example on fengshen

抽取 extraction example on fengshen

引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文：

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

@article{Sinovation2021ZEN2,
  title="{ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders}",
  author={Yan Song, Tong Zhang, Yonggang Wang, Kai-Fu Lee},
  journal={arXiv preprint arXiv:2105.01279},
  year={2021},
}

如果您在您的工作中使用了我们的模型，也可以引用我们的总论文：

如果您将本资源用于您的工作，请引用我们的总论文：

@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:

您也可以引用我们的网站:

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