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
metadata
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
@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 (2.3.1.1.i.0/1/22/I/CFLA/002).