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A model-filtered version of C4, including two stages:
Removing Advertisements
A RoBERTa-large classifier is trained on 550K binary annotations by Qwen2.5-32B-Instruct. The classifier is then used to filter out advertisements in the whole corpus (see here for the removed advertisements).
Removing documents with low-quality language
Qwen2.5-32B-Instruct is used again to generate language quality annotation (on a scale of 1-5) for 250K samples. A RoBERT-large classifier is trained with regression on these annotations. Any document receiving a score of 1 or 2 from the classifier is removed.
Dataset Statistics
Number of samples: 241,791,494.
Size of downloaded parquet files: 397G.
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