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 approach. Based on 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. 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.
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).