FineWeb2 Edu Japanese
Very large Japanese educational dataset, composed of filtered texts from the web with partial elimination of noise, designed for NLP and educational uses.
Approximately 120 million texts (~89.3 billion tokens), text format, subsets in Parquet format
Open Data Commons Attribution License (ODC-by) v1.0
Description
The dataset FineWeb2 Edu Japanese includes approximately 120 million Japanese texts filtered for their educational quality, representing approximately 89.3 billion tokens. It is the result of rigorous sorting through annotation models that identify educational content and remove typical web noise. Several subsets allow access to specific portions, including short, cleaned texts.
What is this dataset for?
- Train and evaluate Japanese language models adapted to the education and comprehension of texts
- Improving the quality of LLMs for educational applications, such as learning assistants or automatic proofreaders
- Study the filtration and cleaning of large-scale web text data to optimize quality
Can it be enriched or improved?
Yes, it is possible to add additional annotations, such as thematic metadata or difficulty assessments. Cleaning can also be refined to reduce false positives in noise suppression.
🔎 In summary
🧠 Recommended for
- Japanese NLP researchers
- Development of educational assistants
- Large-scale LLMs training
🔧 Compatible tools
- Hugging Face Datasets
- PyTorch
- TensorFlow
- Apache Spark
- Pandas
💡 Tip
Use the cleaned subsets to quickly prototype before moving on to the full dataset.
Frequently Asked Questions
What sets this FineWeb2 Edu Japanese dataset apart from other Japanese datasets?
It focuses on texts of high educational value that are automatically filtered, with web noise cleaning, which improves the quality for learning.
What are the exact sizes and formats available?
Approximately 120 million texts (~89.3 billion tokens), available in several subsets, mainly in Parquet and plain text formats.
Can this dataset contain errors due to automatic cleaning?
Yes, automatic cleaning can delete valid text portions by mistake, it is advisable to check according to the use case.
