← 返回资源分享
CPM: A Large-scale Generative Chinese Pre-trained Language Model
论文
论文
发布时间2020-12-01
发表arXiv:2012.00413
作者:Minlie Huang,Xiaoyan Zhu,Hao Zhou,Zhiyuan Liu,Maosong Sun,Xu Han,Yujia Qin,Fanchao Qi,Pei Ke,Haozhe Ji,Jie Tang,Xiaozhi Wang,Zhengyan Zhang,Juanzi Li,Yuxian Gu,Shengqi Chen,Deming Ye,Yusheng Su,Jian Guan,Yanan Zheng,Guoyang Zeng,Huanqi Cao,Daixuan Li,Zhenbo Sun,Wentao Han
详细介绍
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many NLP tasks in the settings of few-shot (even zero-shot) learning. The code and parameters are available at https://github.com/TsinghuaAI/CPM-Generate.
代码仓库 (10)
TsinghuaAI/CPM-Generate官方PyTorch
mindspore-ai/models/tree/master/official/nlp/cpmMindSpore
openbmb/bminfPyTorch
2024-MindSpore-1/Code2/tree/main/model-1/cpmbeeMindSpore
MindSpore-paper-code-3/code9/tree/main/cpmMindSpore
MindCode-4/code-3/tree/main/cpmbeeMindSpore
2023-MindSpore-1/ms-code-219/tree/main/cpmMindSpore
2024-MindSpore-1/Code2/tree/main/model-1/cpmMindSpore
MindCode-4/code-3/tree/main/cpmMindSpore
FLoutione/CPM-mindsporeMindSpore
