  During the development of large language models (LLMs), the scale and quality
of the pre-training data play a crucial role in shaping LLMs' capabilities. To
accelerate the research of LLMs, several large-scale datasets, such as C4 [1],
Pile [2], RefinedWeb [3] and WanJuan [4], have been released to the public.
However, most of the released corpus focus mainly on English, and there is
still lack of complete tool-chain for extracting clean texts from web data.
Furthermore, fine-grained information of the corpus, e.g. the quality of each
text, is missing. To address these challenges, we propose in this paper a new
complete tool-chain EvalWeb to extract Chinese clean texts from noisy web data.
First, similar to previous work, manually crafted rules are employed to discard
explicit noisy texts from the raw crawled web contents. Second, a well-designed
evaluation model is leveraged to assess the remaining relatively clean data,
and each text is assigned a specific quality score. Finally, we can easily
utilize an appropriate threshold to select the high-quality pre-training data
for Chinese. Using our proposed approach, we release the largest and latest
large-scale high-quality Chinese web text ChineseWebText, which consists of
1.42 TB and each text is associated with a quality score, facilitating the LLM
researchers to choose the data according to the desired quality thresholds. We
also release a much cleaner subset of 600 GB Chinese data with the quality
exceeding 90%.
