Instructions to use HiTZ/BERnaT-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiTZ/BERnaT-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HiTZ/BERnaT-medium")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HiTZ/BERnaT-medium") model = AutoModelForMaskedLM.from_pretrained("HiTZ/BERnaT-medium") - Notebooks
- Google Colab
- Kaggle
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README.md
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Submitted to LREC 2026
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## Model Description
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BERnaT is a family of monolingual Basque encoder-only language models trained to better represent linguistic variation—including standard, dialectal, historical, and informal Basque—rather than focusing solely on standard textual corpora. Models were trained on corpora that combine high-quality standard Basque with varied sources such as social media and historical texts, aiming to enhance robustness and generalization across natural language understanding (NLU) tasks.
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## Training Data
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Submitted to LREC 2026
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### Model Description
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BERnaT is a family of monolingual Basque encoder-only language models trained to better represent linguistic variation—including standard, dialectal, historical, and informal Basque—rather than focusing solely on standard textual corpora. Models were trained on corpora that combine high-quality standard Basque with varied sources such as social media and historical texts, aiming to enhance robustness and generalization across natural language understanding (NLU) tasks.
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- **Developed by:** HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
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- **Funded by:** Ikergaitu and ALIA projects (Basque and Spanish Government)
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- **License:** Apache 2.0
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- **Model Type**: Encoder-only Transformer models (RoBERTa-style)
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- **Languages**: Basque (Euskara)
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## Training Data
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