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Geralt-Targaryen tfivezhang commited on
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Super-squash branch 'main' using huggingface_hub

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Co-authored-by: tfivezhang <tfivezhang@users.noreply.huggingface.co>

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README.md ADDED
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+ ---
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+ license: odc-by
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+ ---
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+
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+ High-quality Chinese text from Common Crawl cleaned by the following steps:
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+
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+ - Documents containing more than 2% non-Chinese, non-English characters are removed. Those containing more than 30% digits or capital letters are also removed.
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+ - Documents whose language is identified as non-Chinese by fasttext are removed.
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+ - All text in Traditional Chinese is converted into Simplified Chinese.
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+ - Low-quality documents (e.g. boilerplates, advertisements) are heuristically removed based on statistics such as average line length, portion of special characters, etc.
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+ - Exact deduplication.
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+ - 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.
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+
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+ **Details about Model Annotations**
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+
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+ On 2K samples, we compared the annotation distribution (in percentage) of Qwen2.5-Instruct 32B and 72B:
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+
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+ | Score | 1 | 2 | 3 | 4 | 5 |
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+ | ----- | --- | ---- | ---- | ---- | ---- |
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+ | 32B | 0.7 | 17.1 | 45.7 | 35.8 | 0.8 |
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+ | 72B | 0.3 | 4.7 | 22.9 | 58.1 | 14.1 |
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+
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+ 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.
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+
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+ **Data Statistics**
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+ - Number of samples: 623,807,180
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+ - Size: 1.1TB (2120 parquet files)
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