{"url":"/dataset/cluecorpus2020","name":"CLUECorpus2020","full_name":null,"description_markdown":"CLUECorpus2020 is a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation. It has 100G raw corpus with 35 billion Chinese characters, which is retrieved from Common Crawl. \r\n\r\nSource: [CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model](/paper/cluecorpus2020-a-large-scale-chinese-corpus)","description_withheld":null,"homepage":"https://github.com/CLUEbenchmark/CLUECorpus2020/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/cluecorpus2020-a-large-scale-chinese-corpus","title":"CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model","first_author":"Liang Xu","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Text Generation","url":"/task/text-generation","datasets_with_task":"/datasets/task/text-generation"},{"name":"Language Modelling","url":"/task/language-modelling","datasets_with_task":"/datasets/task/language-modelling"},{"name":"Self-Supervised Learning","url":"/task/self-supervised-learning","datasets_with_task":"/datasets/task/self-supervised-learning"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["CLUECorpus2020"],"data_loaders":[{"repo":"https://github.com/CLUEbenchmark/CLUECorpus2020","url":"https://github.com/CLUEbenchmark/CLUECorpus2020","frameworks":["tf"]}],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}