---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- en
---
[ClimbLab](https://huggingface.co/datasets/nvidia/ClimbLab) is a high-quality pre-training corpus released by NVIDIA. Here is the description:

>ClimbLab is a filtered 1.2-trillion-token corpus with 20 clusters. 
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.

But it is released in gpt-2 tokens which is not easy-to-use. Therefore,we use gpt-2 tokenizer to detokenize them into raw texts.

⚠️ Please note: This version is not officially released or maintained by NVIDIA. We are not responsible for the content, accuracy, or updates of this dataset.


## Citation:
If you find this dataset helpful, please cite the following [paper](https://arxiv.org/abs/2504.13161):

```
@article{diao2025climb,
  author    = {Shizhe Diao and Yu Yang and Yonggan Fu and Xin Dong and Dan Su and Markus Kliegl and Zijia Chen and Peter Belcak and Yoshi Suhara and Hongxu Yin and Mostofa Patwary and Celine Lin and Jan Kautz and Pavlo Molchanov},
  title={CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training}, 
  journal   = {arXiv preprint},
  year      = {2025},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url={https://arxiv.org/abs/2504.13161}, 
}
```