五道语料库:一个用于预训练语言模型的超大规模汉语语料库

Sha Yuan , Hanyu Zhao , Zhengxiao Du , Ming Ding , Xiao Liu , Yukuo Cen , Xu Zou , Zhilin Yang , Jie Tang
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引用次数: 54

摘要

利用大规模训练数据构建具有更大参数量的预训练语言模型(PLM),可以显著改善下游任务。例如,OpenAI在570 GB英语训练数据上训练了1750亿个参数的GPT3模型,只用少量样本就可以构建下游应用。然而,目前还缺乏支持大规模plm的中文语料库。本文介绍了一个超大规模的汉语语料库“五道语料库”,包含约3tb的训练数据和1.08万亿的汉字。我们还发布了五道语料库的基础版本,包含约200gb的训练数据和720亿个汉字。作为基线,我们在基础版本上训练了一个具有30亿个参数的模型transformer-XL来测试语料库的效果。结果表明,在该语料库上训练的模型在汉语学习中取得了优异的成绩。数据和模型可分别在https://data.wudaoai.cn和https://github.com/THUDM/Chinese-Transformer-XL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。
WuDaoCorpora: A super large-scale Chinese corpora for pre-training language models

Using large-scale training data to build a pre-trained language model (PLM) with a larger volume of parameters can significantly improve downstream tasks. For example, OpenAI trained the GPT3 model with 175 billion parameters on 570 GB English training data, enabling downstream applications building with only a small number of samples. However, there is a lack of Chinese corpus to support large-scale PLMs. This paper introduces a super large-scale Chinese corpora WuDaoCorpora, containing about 3 TB training data and 1.08 trillion Chinese characters. We also release the base version of WuDaoCorpora, containing about 200 GB training data and 72 billion Chinese characters. As a baseline, we train a model transformer-XL with 3 billion parameters on the base version to test the corpora's effect. The results show that the models trained on this corpora can achieve excellent performance in Chinese. The data and model are available at https://data.wudaoai.cn and https://github.com/THUDM/Chinese-Transformer-XL, respectively.

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