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BERnaT-Standard-medium

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 ---
 library_name: transformers
+license: apache-2.0
 ---
+# BERnaT: Basque Encoders for Representing Natural Textual Diversity
+Submitted to LREC 2026
+## Abstract
+Language models depend on massive text corpora that are often filtered for quality, a process that can unintentionally
+exclude non-standard linguistic varieties, reduce model robustness and reinforce representational biases. In this
+paper, we argue that language models should aim to capture the full spectrum of language variation (dialectal,
+historical, informal, etc.) rather than relying solely on standardized text. Focusing on Basque, a morphologically rich
+and low-resource language, we construct new corpora combining standard, social media, and historical sources, and
+pre-train the BERnaT family of encoder-only models in three configurations: standard, diverse, and combined. We
+further propose an evaluation framework that separates Natural Language Understanding (NLU) tasks into standard
+and diverse subsets to assess linguistic generalization. Results show that models trained on both standard and
+diverse data consistently outperform those trained on standard corpora, improving performance across all task types
+without compromising standard benchmark accuracy. These findings highlight the importance of linguistic diversity in
+building inclusive, generalizable language models.
+## Results
+|                     | **AVG standard tasks** | **AVG diverse tasks** | **AVG overall** |
+|---------------------|:----------------------:|:---------------------:|:---------------:|
+| **BERnaT_standard** |                        |                       |                 |
+| medium              |          74.10         |         70.30         |      72.58      |
+| base                |          75.33         |         71.26         |      73.70      |
+| large               |          76.83         |         73.13         |      75.35      |
+| **BERnaT_diverse**  |                        |                       |                 |
+| medium              |          71.66         |         69.91         |      70.96      |
+| base                |          72.44         |         71.43         |      72.04      |
+| large               |          74.48         |         71.87         |      73.43      |
+| **BERnaT**          |                        |                       |                 |
+| medium              |          73.56         |         70.59         |      72.37      |
+| base                |          75.42         |         71.28         |      73.76      |
+| large               |        **77.88**       |       **73.77**       |    **76.24**    |
+## Acknowledgments
+This work has been partially supported by the Basque Government (Research group funding IT1570-22 and IKER-GAITU project), the Spanish Ministry for Digital Transformation and Civil Service, and the EU-funded NextGenerationEU Recovery, Transformation and Resilience Plan (ILENIA project, 2022/TL22/00215335; and ALIA project). The project also received funding from the European Union’s Horizon Europe research and innovation program under Grant Agreement No 101135724, Topic HORIZON-CL4-2023-HUMAN-01-21 and DeepKnowledge (PID2021-127777OB-C21) founded by MCIN/AEI/10.13039/501100011033 and FEDER. Jaione Bengoetxea, Julen Etxaniz and Ekhi Azurmendi hold a PhD grant from the Basque Government (PRE_2024_1_0028, PRE_2024_2_0028 and PRE_2024_1_0035, respectively). Maite Heredia and Mikel Zubillaga hold a PhD grant from the University of the Basque Country UPV/EHU (PIF23/218 and PIF24/04, respectively). The models were trained on the Leonardo supercomputer at CINECA under the EuroHPC Joint Undertaking, project EHPC-EXT-2024E01-042.
+## Citation:
+To cite our work, please use:
+```bibtex
+@misc{azurmendi2025bernatbasqueencodersrepresenting,
+      title={BERnaT: Basque Encoders for Representing Natural Textual Diversity},
+      author={Ekhi Azurmendi and Joseba Fernandez de Landa and Jaione Bengoetxea and Maite Heredia and Julen Etxaniz and Mikel Zubillaga and Ander Soraluze and Aitor Soroa},
+      year={2025},
+      eprint={2512.03903},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2512.03903},
+}
+```