Erlangshen-MegatronBert-3.9B-Chinese是一个比Erlangshen-MegatronBert-1.3B拥有更多参数的版本（39亿）。我们遵循原来的预训练方式在悟道数据集（300G版本）上进行预训练。具体地，我们在预训练阶段中使用了封神框架大概花费了64张A100（40G）约30天。

Erlangshen-MegatronBert-3.9B-Chinese (3.9B) is a larger version of Erlangshen-MegatronBert-1.3B. By following the original instructions, we apply WuDao Corpora (300 GB version) as the pretraining dataset. Specifically, we use the fengshen framework in the pre-training phase which cost about 30 days with 64 A100 (40G) GPUs.

下游效果 Performance

下游中文任务的得分（没有做任何数据增强）:

Scores on downstream Chinese tasks (without any data augmentation):

Model	afqmc	tnews	iflytek	ocnli	cmnli	wsc	csl
roberta-wwm-ext-large	0.7514	0.5872	0.6152	0.777	0.814	0.8914	0.86
Erlangshen-MegatronBert-1.3B	0.7608	0.5996	0.6234	0.7917	0.81	0.9243	0.872
Erlangshen-MegatronBert-3.9B	0.7561	0.6048	0.6204	0.8278	0.8517	-	-

使用 Usage

python

from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
import torch

tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-3.9B-Chinese', use_fast=False)
model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-3.9B-Chinese')
text = '生活的真谛是[MASK]。'
fillmask_pipe = FillMaskPipeline(model, tokenizer)
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}},
}

IDEA-CCNL/Erlangshen-MegatronBert-3.9B-Chinese

Erlangshen-MegatronBert-3.9B-Chinese

简介 Brief Introduction

模型分类 Model Taxonomy

模型信息 Model Information

更多信息 More Information

下游效果 Performance

使用 Usage

引用 Citation

IDEA-CCNL/
Erlangshen-MegatronBert-3.9B-Chinese