Randeng Pegasus 523M Summary Chinese

🚀 燃燈-飛馬-523M-中文摘要模型

燃燈-飛馬-523M-中文摘要模型是中文版的PAGASUS-large，在多箇中文摘要數據集上進行微調，擅長處理文本摘要任務。

🚀 快速開始

模型相關鏈接

• 主頁：封神榜
• Github：Fengshenbang-LM

✨ 主要特性

該模型善於處理摘要任務，是基於多箇中文摘要數據集微調後的中文版PAGASUS-large。

📦 模型分類

📚 詳細文檔

模型信息

參考論文：PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

基於Randeng-Pegasus-523M-Chinese，我們在收集的7箇中文領域的文本摘要數據集（約4M個樣本）上微調了它，得到了摘要版本。這7個數據集為：education, new2016zh, nlpcc, shence, sohu, thucnews和weibo。

下游效果

💻 使用示例

基礎用法

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 = "據微信公眾號“界面”報道，4日上午10點左右，中國發改委反壟斷調查小組突擊查訪奔馳上海辦事處，調取數據材料，並對多名奔馳高管進行了約談。截止昨日晚9點，包括北京梅賽德斯-奔馳銷售服務有限公司東區總經理在內的多名管理人員仍留在上海辦公室內"
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: 反壟斷調查小組突擊查訪奔馳上海辦事處，對多名奔馳高管進行約談

📄 引用

如果您在您的工作中使用了我們的模型，可以引用我們的論文：

@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}
}

也可以引用我們的網站:

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