CPM: A large-scale generative Chinese Pre-trained language model

Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Maosong Sun
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引用次数: 86

Abstract

Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570 GB 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 100 GB 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.

CPM:大规模生成中文预训练语言模型
预训练语言模型(PLMs)已被证明对各种下游NLP任务是有益的。最近,拥有1750亿个参数和570gb训练数据的GPT-3因其少射(甚至零射)学习的能力而备受关注。然而,由于GPT-3的训练语料库主要是英语,且参数不公开,因此将GPT-3应用于汉语NLP任务仍然具有挑战性。在本技术报告中,我们发布了基于大规模中文训练数据生成式预训练的中文预训练语言模型(CPM)。据我们所知,CPM是最大的中文预训练语言模型,拥有26亿个参数和100 GB的中文训练数据,可以促进几个下游的中文NLP任务,如会话、文章生成、完形填空测试和语言理解。大量的实验表明,CPM在少量(甚至零次)学习的情况下,在许多NLP任务上取得了较好的表现。代码和参数可在https://github.com/TsinghuaAI/CPM上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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