---
pretty_name: test strict shuffled
task_categories:
- text-generation
tags:
- parquet
license: odc-by
language:
- ja
---

# ClimbLab-Ja

ClimbLab-Ja is a filtered 300-billion-token Japanese corpus with 20 clusters. It is a Japanese adaptation of the [nvidia/Nemotron-ClimbLab](https://huggingface.co/datasets/nvidia/Nemotron-ClimbLab) approach. Based on [LLM-jp Corpus v4](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v4), we semantically reorganized and filtered the dataset into 20 distinct clusters, resulting in a high-quality 300-billion-token corpus. Specifically, we first grouped the data into 1,000 groups based on topic information. Then we assigned six scores from 0 to 5 to each group and document: quality, advertisement, informational value, educational value, cultural value, and creative value. Low-quality clusters and documents were removed based on these scores.

This dataset is for research and development only.

## Dataset Details

- Intended Usage: Pre-training language models.
- Format: Text in parquet format.
- Size: 300 billion tokens.

## Filtering

We removed clusters using the following rule:

`quality_mean >= 2 && advertisement_mean >= 2 && any(value_mean >= 3)`

where `value_mean` refers to informational value, educational value, cultural value, or creative value.

We also removed documents using the following rule:

`quality >= 2 && advertisement >= 2 && any(value >= 3)`

where `value` refers to informational value, educational value, cultural value, or creative value.

## License

The dataset structure, record IDs, filtering scores, mixture weights, and other metadata newly added in this release are licensed under [ODC-BY](https://opendatacommons.org/licenses/by/1-0/).
By using this dataset, you are also bound by any license agreements and terms of use of the original data sources listed in the [LICENSES.md](LICENSES.md).

This dataset was created using the Supermicro ARS-111GL-DNHR-LCC and FUJITSU Server PRIMERGY CX2550 M7 (Miyabi) at the Joint Center for Advanced High Performance Computing (JCAHPC).