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README.md
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>ClimbLab is a filtered 1.2-trillion-token corpus with 20 clusters.
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Based on Nemotron-CC and SmolLM-Corpus, we employed our proposed CLIMB-clustering to semantically reorganize and filter this combined dataset into 20 distinct clusters, leading to a 1.2-trillion-token high-quality corpus. Specifically, we first grouped the data into 1,000 groups based on topic information. Then we applied two classifiers: one to detect advertisements and another to assess the educational value of the text. Each group was scored accordingly, and low-quality data with low scores was removed.
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license: cc-by-nc-4.0
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task_categories:
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- text-generation
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language:
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- en
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[ClimbLab](https://huggingface.co/datasets/nvidia/ClimbLab) is a high-quality pre-training corpus released by NVIDIA. Here is the description:
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>ClimbLab is a filtered 1.2-trillion-token corpus with 20 clusters.
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Based on Nemotron-CC and SmolLM-Corpus, we employed our proposed CLIMB-clustering to semantically reorganize and filter this combined dataset into 20 distinct clusters, leading to a 1.2-trillion-token high-quality corpus. Specifically, we first grouped the data into 1,000 groups based on topic information. Then we applied two classifiers: one to detect advertisements and another to assess the educational value of the text. Each group was scored accordingly, and low-quality data with low scores was removed.
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