metadata

license: apache-2.0
language: zh
tags:
  - T5
  - chinese
  - sentencepiece
inference: true
widget:
  - text: 北京有悠久的 <extra_id_0>和 <extra_id_1>。
  - type: text-generation

Randeng-T5-Char-57M-Chinese

Main Page:Fengshenbang
Github: Fengshenbang-LM

简介 Brief Introduction

善于处理NLT任务，中文版的T5-small，采用了BertTokenizer和中文字级别词典。

Good at handling NLT tasks, Chinese T5-small， use BertTokenizer and chinese vocab.

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	自然语言转换 NLT	燃灯 Randeng	T5	57M	中文-Chinese

模型信息 Model Information

对比T5-small，训练了它的中文版。为了更好适用于中文任务，我们仅使用BertTokenzier，和支持中英文的词表，并且使用了语料库自适应预训练(Corpus-Adaptive Pre-Training, CAPT)技术在悟道语料库(180G版本)继续预训练。预训练目标为破坏span。具体地，我们在预训练阶段中使用了封神框架大概花费了8张A100约24小时。

Compared with T5-samll, we implement its Chinese version. In order to use for chinese tasks, we use BertTokenizer and Chinese vocabulary, and Corpus-Adaptive Pre-Training (CAPT) on the WuDao Corpora (180 GB version). The pretraining objective is span corruption. Specifically, we use the fengshen framework in the pre-training phase which cost about 24 hours with 8 A100 GPUs.

使用 Usage

from transformers import T5ForConditionalGeneration, BertTokenizer
import torch

tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Randeng-T5-Char-57M-Chinese', add_special_tokens=False)
model=T5ForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-T5-Char-57M-Chinese')

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}},
}