language:
- eu
pretty_name: ZelaiHandiClean 🤠
task_categories:
- text-generation
size_categories:
- 100M<n<1B
Dataset Summary
A large, cleaned Basque-language corpus originally based on the ZelaiHandi dataset, augmented with books and Wikipedia articles to support language-modeling experiments. The data have been normalized and stripped of extraneous whitespace, blank lines and non-linguistic characters.
For example, this are the stats for Ekaia subset:
| Metric | Value |
|---|---|
| Initial characters (Ekaia subset) | 14,480,942 |
| Final characters (after cleaning) | 12,746,071 |
| Overall cleaned | 11.98 % |
| Extra spaces removed | 0.03 % |
| Blank lines removed | 15.47 % |
| Non-linguistic characters removed | 11.96 % |
Supported Tasks
- Causal language modeling
- Masked language modeling
- Next-sentence prediction
- Any downstream Basque NLP task (fine-tuning)
Languages
- Basque (
eu)
Dataset Statistics
| Metric | Value |
|---|---|
| Total words | 660 million |
| Disk size | 5.8 GB |
| Additional books scraped from Booktegui | 400 |
| Wikipedia articles added | 2,500 |
Dataset Structure
Each example in the JSONL files has the following schema:
{
"id": "unique for each document",
"periodico": "source",
"lugar": "geographic focus of the source",
"dominio": "type of content (articles, news, books…)",
"texto": "raw text used for training",
"licencia": "document license"
}
There is no specific train-val distinction in the dataset, but I would just take the dataset, divide it in 100 chunks of text and use 1 of them for val to make sure the model is generalising well.
Data Collection and Cleaning
Original Source
- ZelaiHandi dataset (Basque news, books, articles)
Cleaning Steps
- Removed extra whitespace and blank lines
- Normalized Unicode characters
- Stripped non-linguistic symbols (HTML tags, control characters)
Augmentation
- +400 books (liburuak) scraped from Booktegui
- +2,500 articles from the Basque Wikipedia (Wikipediabi)
- Wikipedia Berria and Legebiltzarra datasets were ingested in three parts to avoid interface issues
Considerations for Use
- All text is raw; you may wish to tokenize or further normalize per your model’s requirements. I have my own basque tokenizer and I will maybe also upload it.
- Maintain consistent train/validation splits for reproducible benchmarks.
License
Various Creative Commons licenses (CC-BY, CC-BY-SA).
See each JSONL record’s "licencia" field for details.
Citation
If you use this dataset, please cite:
Orai NLP Teknologiak (2025). ZelaiHandi + Booktegui + Wikipediabi Basque Corpus. CC-BY-SA.
Acknowledgements
Special thanks to:
- San Vicente, Iñaki & Urbizu
- Gorka & Corral
- Ander & Beloki
- Zuhaitz & Saralegi
- Xabier
…for creating the original ZelaiHandi dataset, which served as the foundation for this cleaned and slightly expanded corpus.
*I have just noticed that there is STILL A LOT to clean, so I will be uploading the updates during this month 🤠