Randeng Pegasus 523M Summary Chinese

🚀 燃灯-飞马-523M-中文摘要模型

燃灯-飞马-523M-中文摘要模型是中文版的PAGASUS-large，在多个中文摘要数据集上进行微调，擅长处理文本摘要任务。

🚀 快速开始

模型相关链接

• 主页：封神榜
• Github：Fengshenbang-LM

✨ 主要特性

该模型善于处理摘要任务，是基于多个中文摘要数据集微调后的中文版PAGASUS-large。

📦 模型分类

📚 详细文档

模型信息

参考论文：PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

基于Randeng-Pegasus-523M-Chinese，我们在收集的7个中文领域的文本摘要数据集（约4M个样本）上微调了它，得到了摘要版本。这7个数据集为：education, new2016zh, nlpcc, shence, sohu, thucnews和weibo。

下游效果

💻 使用示例

基础用法

from transformers import PegasusForConditionalGeneration
# Need to download tokenizers_pegasus.py and other Python script from Fengshenbang-LM github repo in advance,
# or you can download tokenizers_pegasus.py and data_utils.py in https://huggingface.co/IDEA-CCNL/Randeng_Pegasus_523M/tree/main
# Strongly recommend you git clone the Fengshenbang-LM repo:
# 1. git clone https://github.com/IDEA-CCNL/Fengshenbang-LM
# 2. cd Fengshenbang-LM/fengshen/examples/pegasus/
# and then you will see the tokenizers_pegasus.py and data_utils.py which are needed by pegasus model
from tokenizers_pegasus import PegasusTokenizer

model = PegasusForConditionalGeneration.from_pretrained("IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese")
tokenizer = PegasusTokenizer.from_pretrained("IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese")

text = "据微信公众号“界面”报道，4日上午10点左右，中国发改委反垄断调查小组突击查访奔驰上海办事处，调取数据材料，并对多名奔驰高管进行了约谈。截止昨日晚9点，包括北京梅赛德斯-奔驰销售服务有限公司东区总经理在内的多名管理人员仍留在上海办公室内"
inputs = tokenizer(text, max_length=1024, return_tensors="pt")

# Generate Summary
summary_ids = model.generate(inputs["input_ids"])
tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

# model Output: 反垄断调查小组突击查访奔驰上海办事处，对多名奔驰高管进行约谈

📄 引用

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：

@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:

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