Instructions to use AiLab-IMCS-UL/lv-mbert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AiLab-IMCS-UL/lv-mbert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AiLab-IMCS-UL/lv-mbert-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AiLab-IMCS-UL/lv-mbert-large") model = AutoModelForMaskedLM.from_pretrained("AiLab-IMCS-UL/lv-mbert-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: lv | |
| license: apache-2.0 | |
| library_name: transformers | |
| pipeline_tag: fill-mask | |
| tags: | |
| - latvian | |
| - encoder | |
| - masked-lm | |
| - modernbert | |
| # Latvian ModernBERT large model | |
| Latvian ModernBERT text encoder model, released with the paper **"Pretraining and Benchmarking Modern Encoders for Latvian"**. | |
| For evaluation code and benchmark results, see: | |
| https://github.com/LUMII-AILab/latvian-encoders | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{znotins-2026-modern_lv_encoders, | |
| title = "Pretraining and Benchmarking Modern Encoders for {L}atvian", | |
| author = "Znotins, Arturs", | |
| booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({LoResLM})", | |
| year = "2026", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2026.loreslm-1.40/", | |
| pages = "461--470" | |
| } | |
| ``` | |
| ## See also | |
| - https://huggingface.co/AiLab-IMCS-UL/lv-mbert-base | |
| - https://huggingface.co/AiLab-IMCS-UL/lv-roberta-base | |
| - https://huggingface.co/AiLab-IMCS-UL/lv-deberta-base | |
| ## Acknowledgements | |
| This work was supported by the EU Recovery and Resilience Facility project [Language Technology Initiative](https://www.vti.lu.lv) (2.3.1.1.i.0/1/22/I/CFLA/002). | |