OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for
  LLM Training
Authors: name: Yijiong Yu, name: Ziyun Dai, name: Zekun Wang, name: Wei Wang, name: Ran Chen, name: Ji Pei

Large language models (LLMs) have demonstrated remarkable capabilities, but
their success heavily relies on the quality of pretraining corpora. For Chinese
LLMs, the scarcity of high-quality Chinese datasets presents a significant
challenge, often limiting their performance. To address this issue, we propose
the OpenCSG Chinese Corpus, a series of high-quality datasets specifically
designed for LLM pretraining, post-training, and fine-tuning. This corpus
includes Fineweb-edu-chinese, Fineweb-edu-chinese-v2, Cosmopedia-chinese, and
Smoltalk-chinese, each with distinct characteristics: Fineweb-edu datasets
focus on filtered, high-quality content derived from diverse Chinese web
sources; Cosmopedia-chinese provides synthetic, textbook-style data for
knowledge-intensive training; and Smoltalk-chinese emphasizes stylistic and
diverse chat-format data. The OpenCSG Chinese Corpus is characterized by its
high-quality text, diverse coverage across domains, and scalable, reproducible
data curation processes. Additionally, we conducted extensive experimental
analyses, including evaluations on smaller parameter models, which demonstrated
significant performance improvements in tasks such as C-Eval, showcasing the
effectiveness of the corpus for training Chinese LLMs.