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chinese_clean_passages_80m

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metadata
dataset_info:
  features:
    - name: passage
      dtype: string
  splits:
    - name: train
      num_bytes: 18979214734
      num_examples: 88328203
  download_size: 1025261393
  dataset_size: 18979214734

chinese_clean_passages_80m

包含8千余万（88328203）个纯净中文段落，不包含任何字母、数字。
Containing more than 80 million pure & clean Chinese passages, without any letters/digits/special tokens.

文本长度大部分介于50~200个汉字之间。
The passage length is approximately 50~200 Chinese characters.

通过datasets.load_dataset()下载数据，会产生38个大小约340M的数据包，共约12GB，所以请确保有足够空间。
Downloading the dataset will result in 38 data shards each of which is about 340M and 12GB in total. Make sure there's enough space in your device:)

>>> 
passage_dataset = load_dataset('beyond/chinese_clean_passages_80m')

<<<
Downloading data: 100%|█| 341M/341M [00:06<00:00, 52.0MB
Downloading data: 100%|█| 342M/342M [00:06<00:00, 54.4MB
Downloading data: 100%|█| 341M/341M [00:06<00:00, 49.1MB
Downloading data: 100%|█| 341M/341M [00:14<00:00, 23.5MB
Downloading data: 100%|█| 341M/341M [00:10<00:00, 33.6MB
Downloading data: 100%|█| 342M/342M [00:07<00:00, 43.1MB
...(38 data shards)

本数据集被用于训练GENIUS模型中文版，如果这个数据集对您的研究有帮助，请引用以下论文。 This dataset is created for the pre-training of GENIUS model, if you find this dataset useful, please cite our paper. 

@article{guo2022genius,
  title={GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation},
  author={Guo, Biyang and Gong, Yeyun and Shen, Yelong and Han, Songqiao and Huang, Hailiang and Duan, Nan and Chen, Weizhu},
  journal={arXiv preprint arXiv:2211.10330},
  year={2022}
}

Acknowledgment:
数据是基于CLUE中文预训练语料集进行处理、过滤得到的。
This dataset is processed/filtered from the CLUE pre-training corpus.

原始数据集引用:

@misc{bright_xu_2019_3402023,
author       = {Bright Xu},
title        = {NLP Chinese Corpus: Large Scale Chinese Corpus for NLP },
month        = sep,
year         = 2019,
doi          = {10.5281/zenodo.3402023},
version      = {1.0},
publisher    = {Zenodo},
url          = {https://doi.org/10.5281/zenodo.3402023}
}