---
language:
  - eu
configs:
- config_name: booktegi
  data_files:
  - split: train
    path: booktegi/train.jsonl.gz
  - split: validation
    path: booktegi/valid.jsonl.gz
  - split: test
    path: booktegi/test.jsonl.gz
- config_name: colossal-oscar
  data_files:
  - split: train
    path: colossal-oscar/train.jsonl.gz
  - split: validation
    path: colossal-oscar/valid.jsonl.gz
  - split: test
    path: colossal-oscar/test.jsonl.gz
- config_name: culturax
  data_files:
  - split: train
    path: CulturaX/train.jsonl.gz
  - split: validation
    path: CulturaX/valid.jsonl.gz
  - split: test
    path: CulturaX/test.jsonl.gz
- config_name: egunkaria
  data_files:
  - split: train
    path: egunkaria/train.jsonl.gz
  - split: validation
    path: egunkaria/valid.jsonl.gz
  - split: test
    path: egunkaria/test.jsonl.gz
- config_name: euscrawl-v1.1
  data_files:
  - split: train
    path: euscrawl-v1.1/train.jsonl.gz
  - split: validation
    path: euscrawl-v1.1/valid.jsonl.gz
  - split: test
    path: euscrawl-v1.1/test.jsonl.gz
- config_name: hplt-v1
  data_files:
  - split: train
    path: hplt-v1/train.jsonl.gz
  - split: validation
    path: hplt-v1/valid.jsonl.gz
  - split: test
    path: hplt-v1/test.jsonl.gz
- config_name: wikipedia
  data_files:
  - split: train
    path: wikipedia/train.jsonl.gz
  - split: validation
    path: wikipedia/valid.jsonl.gz
  - split: test
    path: wikipedia/test.jsonl.gz
task_categories:
  - fill-mask
  - text-generation
---
# Latxa Corpus v1.1

This is the training corpus of the Latxa v1.1 base language model, a LLama 2 model trained on Basque text.

- **Repository:** [https://github.com/hitz-zentroa/latxa](https://github.com/hitz-zentroa/latxa)
- **Papers:** [Latxa: An Open Language Model and Evaluation Suite for Basque](https://arxiv.org/)
- **Curated by:** HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
- **Language(s):** eu

## Summary

Latxa's training corpus combines various existing datasets, as well as some new ones that we hereby release.
The raw document mix has been deduplicated and processed; here you'll find the final version of the corpus.
Our data sources are introduced briefly below. 
For more details, consult our [paper]().

* **Euscrawl v1.1 <sup color="red">[new]</sup>**: An updated version of [EusCrawl v1](https://www.ixa.eus/euscrawl/) [1], including new content up to November 2023.
* **Egunkaria <sup color="red">[new]</sup>**: Content from the Egunkaria daily newspaper.
* **Booktegi <sup color="red">[new]</sup>**: Content from [https://www.booktegi.eus/](https://www.booktegi.eus/) EPUB books.
* **Wikipedia**: Basque Wikipedia's [dump](https://dumps.wikimedia.org/) from November 2023.
* **CulturaX**: The Basque portion of the [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) corpus [2].
* **Colossal OSCAR**: The Basque portion of several [Colossal OSCAR](https://huggingface.co/datasets/oscar-corpus/colossal-oscar-1.0) releases.
* **HPLT v1**: The Basque portion of the [HPLT v1](https://hplt-project.org/datasets/v1) [3] corpus.

## Statistics

The size of each dataset in terms of number of documents can be found below:

|                |     Train |  Valid |   Test |
|----------------|----------:|-------:|-------:|
| CulturaX       | 1,283,429 | 13,096 | 13,098 |
| EusCrawl v1.1  | 1,758,084 | 17,861 | 17,736 |
| HPLT v1        |   367,238 |  3,797 |  3,699 |
| Colossal OSCAR |   233,753 |  2,483 |  2,276 |
| Wikipedia      |   400,902 |  4,063 |  4,092 |
| Egunkaria      |   172,876 |  1,766 |  1,764 |
| Booktegi       |       161 |      4 |      1 |


## Citation

To cite our work, please use:

```bibtex
@misc{etxaniz2024latxa,
      title={{L}atxa: An Open Language Model and Evaluation Suite for {B}asque}, 
      author={Julen Etxaniz and Oscar Sainz and Naiara Perez and Itziar Aldabe and German Rigau and Eneko Agirre and Aitor Ormazabal and Mikel Artetxe and Aitor Soroa},
      year={2024},
      eprint={},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## References

[1] Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri, Olatz Perez-de Viñaspre, and Aitor Soroa. 2022.
[Does corpus quality really matter for low-resource languages?](https://doi.org/10.18653/v1/2022.emnlp-main.499). 
In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7383–7390, Abu Dhabi, United Arab Emirates. 
Association for Computational Linguistics.

[2] Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, Hieu Man, Nghia Trung Ngo, Franck Dernoncourt, Ryan A. Rossi, and Thien Huu Nguyen. 2023. 
[CulturaX: A cleaned, enormous, and multilingual dataset for large language models in 167 languages](https://arxiv.org/abs/2309.09400). 
arXiv preprint arXiv:2309.09400

[3] Mikko Aulamo, Nikolay Bogoychev, Shaoxiong Ji, Graeme Nail, Gema Ramírez-Sánchez, Jörg Tiedemann, Jelmer van der Linde, and Jaume Zaragoza. 2023. 
[HPLT: High performance language technologies](https://aclanthology.org/2023.eamt-1.61). 
In Proceedings of the 24th Annual Conference of the European Association for Machine Transla tion, pages 517–518, Tampere, Finland. 
European Association for Machine Translation.