metadata
license: odc-by
High-quality Chinese text from Common Crawl cleaned by the following steps:
- Documents containing more than 2% non-Chinese, non-English characters are removed. Those containing more than 30% digits or capital letters are also removed.
- Documents whose language is identified as non-Chinese by fasttext are removed.
- All text in Traditional Chinese is converted into Simplified Chinese.
- Low-quality documents (e.g. boilerplates, advertisements) are heuristically removed based on statistics such as average line length, portion of special characters, etc.
- Exact deduplication.
- Qwen2.5-32B-Instruct is used to generate language quality annotation (on a scale of 1-5) for 9.3M Chinese documents and 9.2M English documents, from which we sample 398K Chinese documents and 250K English documents to balance label distribution. An XLM-RoBERT-large classifier is trained with regression on these annotations. Any document receiving a score lower than 4 is removed.
Details about Model Annotations
On 2K samples, we compared the annotation distribution (in percentage) of Qwen2.5-Instruct 32B and 72B:
| Score | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 32B | 0.7 | 17.1 | 45.7 | 35.8 | 0.8 |
| 72B | 0.3 | 4.7 | 22.9 | 58.1 | 14.1 |
The scores between the two models have a correlation coefficient of 0.75, and manual inspection suggests that both are satisfactory. We eventually choose the 32B model for both efficiency and more balanced label distribution.
Data Statistics
- Number of samples: 623,807,180
- Size: 1.1TB (2120 parquet files)