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简介 Brief Introduction


Good at solving text summarization tasks, after fine-tuning on multiple Chinese text summarization datasets, Chinese PAGASUS-base.

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 PEFASUS 238M 文本摘要任务-中文 Summary-Chinese

模型信息 Model Information

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

基于Randeng-Pegasus-238M-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summary版本。这7个数据集为:education, new2016zh, nlpcc, shence, sohu, thucnews和weibo。

Based on Randeng-Pegasus-238M-Chinese, we fine-tuned a text summarization version (summary) on 7 Chinese text summarization datasets, with totaling around 4M samples. The datasets include: education, new2016zh, nlpcc, shence, sohu, thucnews and weibo.

下游效果 Performance

datasets rouge-1 rouge-2 rouge-L
LCSTS 43.46 29.59 39.76

使用 Usage

from transformers import PegasusForConditionalGeneration,BertTokenizer
# 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-238M-Summary-Chinese")
tokenizer = PegasusTokenizer.from_pretrained("IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese")

text = "在北京冬奥会自由式滑雪女子坡面障碍技巧决赛中,中国选手谷爱凌夺得银牌。祝贺谷爱凌!今天上午,自由式滑雪女子坡面障碍技巧决赛举行。决赛分三轮进行,取选手最佳成绩排名决出奖牌。第一跳,中国选手谷爱凌获得69.90分。在12位选手中排名第三。完成动作后,谷爱凌又扮了个鬼脸,甚是可爱。第二轮中,谷爱凌在道具区第三个障碍处失误,落地时摔倒。获得16.98分。网友:摔倒了也没关系,继续加油!在第二跳失误摔倒的情况下,谷爱凌顶住压力,第三跳稳稳发挥,流畅落地!获得86.23分!此轮比赛,共12位选手参赛,谷爱凌第10位出场。网友:看比赛时我比谷爱凌紧张,加油!"
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: 滑雪女子坡面障碍技巧决赛谷爱凌获银牌

引用 Citation


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

  author    = {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 and Ruyi Gan and Jiaxing Zhang},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}


You can also cite our website:

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